diff --git a/.Rbuildignore b/.Rbuildignore index 5f75dbbb..9c9f3e7e 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -27,6 +27,7 @@ ^Makefile$ ^revdep$ ^vignettes/template_instructions.txt$ -^vignettes/random_reports.txt$ -^vignettes/plots.txt$ -^vignettes/distributions.txt$ +^vignettes/random_reports.Rmd$ +^vignettes/plots.Rmd$ +^vignettes/distributions.Rmd$ +^vignettes/sim_mixed.Rmd$ diff --git a/CRAN-RELEASE b/CRAN-RELEASE new file mode 100644 index 00000000..f2457ff8 --- /dev/null +++ b/CRAN-RELEASE @@ -0,0 +1,2 @@ +This package was submitted to CRAN on 2021-09-13. +Once it is accepted, delete this file and tag the release (commit 40d23d7). diff --git a/README.Rmd b/README.Rmd index 2f8ccade..27cb5df2 100644 --- a/README.Rmd +++ b/README.Rmd @@ -3,14 +3,13 @@ output: github_document always_allow_html: yes --- -# faux [![DOI](https://zenodo.org/badge/163506566.svg)](https://zenodo.org/badge/latestdoi/163506566) -[![CRAN version](https://www.r-pkg.org/badges/version-last-release/faux)]() -[![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/faux)]() +![CRAN version](https://www.r-pkg.org/badges/version-last-release/faux) +![Downloads](https://cranlogs.r-pkg.org/badges/grand-total/faux) [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) [![Travis build status](https://travis-ci.org/debruine/faux.svg?branch=master)](https://travis-ci.org/debruine/faux) [![Coverage status](https://codecov.io/gh/debruine/faux/branch/master/graph/badge.svg)](https://codecov.io/github/debruine/faux?branch=master) @@ -52,7 +51,5 @@ And the development version from [GitHub](https://github.com/debruine/faux) with devtools::install_github("debruine/faux") ``` -See the [development version manual](https://debruine.github.io/faux/dev/). - -Please note that the [34m'faux'[39m project is released with a [Contributor Code of Conduct](https://github.com/debruine/faux/blob/master/CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms. +Please note that the 'faux' project is released with a [Contributor Code of Conduct](https://github.com/debruine/faux/blob/master/CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms. diff --git a/README.html b/README.html index 8336ab5d..eed6f4d4 100644 --- a/README.html +++ b/README.html @@ -5,7 +5,7 @@ -faux +Installation +

Contrasts other than treatment coding for factors with more than two levels have always confused me. I try to design all my own studies to never have more than two levels per categorical factor, but students in my data skills class are always trying to analyse data with three-level categories (or worse).

+

I thought it was just me, but some recent Twitter discussion showed me I’m not alone. There’s a serious jingle-jangle problem with contrasts. Here are some of the explainers that I found useful (thanks to everyone who recommended them).

+
    +
  1. +Contrasts in R by Marissa Barlaz
    2. +
  2. +Coding categorical predictor variables in factorial designs by Dale Barr
    3. +
  3. +Contrast Coding in Multiple Regression Analysis by Matthew Davis
    4. +
  4. +R Library Contrast Coding Systems for categorical variables by UCLA Statistical Consulting
    5. +
  5. +Rosetta store: Contrasts by GAMLj
    6. +
  6. +Coding Schemes for Categorical Variables by Phillip Alday
    7. +
  7. +Experimental personality designs: analyzing categorical by continuous variable interactions by West, Aiken & Krull
    8. +
+
+

+Terminology

+

Contrasts often have multiple names. The names I’m using try to maintain the relationship with the base R function, apart from anova coding, which was suggested by Dale Barr after I got frustrated that people use so many different labels for that (extremely useful) coding and each of the terms used is also used to refer to totally different codings by others.

+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
My nameOther namesR functionfaux function
TreatmentTreatment (2), Dummy (1, 4, 6), Simple (5)contr.treatmentcontr_code_treatment
AnovaDeviation (2), Contrast (1), Simple (4)contr.treatment - 1/kcontr_code_anova
SumSum (1, 2, 6), Effects (3), Deviation (4, 5), Unweighted Effects (7)contr.sumcontr_code_sum
DifferenceContrast (3), Forward/Backward (4), Repeated (5)MASS::contr.sdifcontr_code_difference
HelmertReverse Helmert (1, 4), Difference (5), Contrast (7)contr.helmert / (column_i+1)contr_code_helmert
PolynomialPolynomial (5), Orthogonal Polynomial (4), Trend (3)contr.polycontr_code_poly
+
+
+

+Faux Contrast Functions

+

These functions are under development

+

First, we’ll set up a simple experimental design and analyse it with lm(). I set empirical = TRUE to make interpreting the estimates easier.

+
+df <- sim_design(between = list(pet = c("cat", "dog", "ferret")), 
+                 n = c(50), mu = c(2, 4, 9), empirical = TRUE)
+

+

Notice that the default contrast is treatment coding, with “cat” as the baseline condition. The estimate for the intercept is the mean value for cats (\(2\)), while the term petdog is the mean value for dogs minus cats (\(4 - 2\)), and the term petferret is the mean value for ferrets minus cats (\(9 - 2\)).

+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +dog + +ferret +
+cat + +0 + +0 +
+dog + +1 + +0 +
+ferret + +0 + +1 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +2 + +0.141 + +14.1 + +0 +
+petdog + +2 + +0.200 + +10.0 + +0 +
+petferret + +7 + +0.200 + +35.0 + +0 +
+
+

In all the subsequent examples, try putting into words what the estimates for the intercept and terms mean in relation to the mean values for each group in the data.

+
+

+
+

+Treatment

+

Treatment coding (also called dummy coding) sets the mean of the reference group as the intercept. It is straightforward to interpret, especially when your factors have an obvious default or baseline value. This is the same type of coding as the default for factors shown above (unless you change your default using options()), but with clearer term labels.

+
+df$pet <- contr_code_treatment(df$pet)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.dog-cat + +.ferret-cat +
+cat + +0 + +0 +
+dog + +1 + +0 +
+ferret + +0 + +1 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +2 + +0.141 + +14.1 + +0 +
+pet.dog-cat + +2 + +0.200 + +10.0 + +0 +
+pet.ferret-cat + +7 + +0.200 + +35.0 + +0 +
+
+

Each contrast compares one level with the reference level, which defaults to the first level, but you can set with the base argument. Now the intercept estimates the mean value for dogs (\(4\)).

+
+df$pet <- contr_code_treatment(df$pet, base = "dog")
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-dog + +.ferret-dog +
+cat + +1 + +0 +
+dog + +0 + +0 +
+ferret + +0 + +1 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +4 + +0.141 + +28.3 + +0 +
+pet.cat-dog + +-2 + +0.200 + +-10.0 + +0 +
+pet.ferret-dog + +5 + +0.200 + +25.0 + +0 +
+
+

Set the reference level to the third level (“ferret”).

+
+df$pet <- contr_code_treatment(df$pet, base = 3)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-ferret + +.dog-ferret +
+cat + +1 + +0 +
+dog + +0 + +1 +
+ferret + +0 + +0 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +9 + +0.141 + +63.6 + +0 +
+pet.cat-ferret + +-7 + +0.200 + +-35.0 + +0 +
+pet.dog-ferret + +-5 + +0.200 + +-25.0 + +0 +
+
+
+
+

+Anova

+

Anova coding is identical to treatment coding, but sets the grand mean as the intercept. Each contrast compares one level with a reference level. This gives us values that are relatively easy to interpret and map onto ANOVA values.

+

Below is anova coding with the first level (“cat”) as the default base. Now the intercept is the grand mean, which is the mean of the three group means (\((2 + 4 + 9)/3\)). Notice that this is different from the mean value of y in our dataset (5, since the number of pets in each group is unbalanced. The term pet_dog-cat is the mean value for dogs minus cats (\(4 - 2\)) and the term pet_ferret-cat is the mean value for ferrets minus cats (\(9 - 2\)).

+
+df$pet <- contr_code_anova(df$pet)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.dog-cat + +.ferret-cat +
+cat + +-0.333 + +-0.333 +
+dog + +0.667 + +-0.333 +
+ferret + +-0.333 + +0.667 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.dog-cat + +2 + +0.200 + +10.0 + +0 +
+pet.ferret-cat + +7 + +0.200 + +35.0 + +0 +
+
+

Anova coding with “dog” as the base. How does the interpretation of the terms change?

+
+df$pet <- contr_code_anova(df$pet, base = "dog")
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-dog + +.ferret-dog +
+cat + +0.667 + +-0.333 +
+dog + +-0.333 + +-0.333 +
+ferret + +-0.333 + +0.667 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.cat-dog + +-2 + +0.200 + +-10.0 + +0 +
+pet.ferret-dog + +5 + +0.200 + +25.0 + +0 +
+
+

Anova coding with the third level (“ferret”) as the base.

+
+df$pet <- contr_code_anova(df$pet, base = 3)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-ferret + +.dog-ferret +
+cat + +0.667 + +-0.333 +
+dog + +-0.333 + +0.667 +
+ferret + +-0.333 + +-0.333 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.cat-ferret + +-7 + +0.200 + +-35.0 + +0 +
+pet.dog-ferret + +-5 + +0.200 + +-25.0 + +0 +
+
+
+
+

+Sum

+

Sum coding also sets the grand mean as the intercept. Each contrast compares one level with the grand mean. Therefore, the estimate for pet_cat-intercept is the difference between the mean value for cats and the grand mean (\(2 - 5\)).

+
+df$pet <- contr_code_sum(df$pet)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-intercept + +.dog-intercept +
+cat + +1 + +0 +
+dog + +0 + +1 +
+ferret + +-1 + +-1 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.24 + +0 +
+pet.cat-intercept + +-3 + +0.115 + +-25.98 + +0 +
+pet.dog-intercept + +-1 + +0.115 + +-8.66 + +0 +
+
+

You can’t compare all levels with the grand mean, and have to omit one level. This is the last level by default, but you can change it with the omit argument.

+
+df$pet <- contr_code_sum(df$pet, omit = "dog")
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-intercept + +.ferret-intercept +
+cat + +1 + +0 +
+dog + +-1 + +-1 +
+ferret + +0 + +1 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.cat-intercept + +-3 + +0.115 + +-26.0 + +0 +
+pet.ferret-intercept + +4 + +0.115 + +34.6 + +0 +
+
+

Omit the first level (“cat”).

+
+df$pet <- contr_code_sum(df$pet, omit = 1)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.dog-intercept + +.ferret-intercept +
+cat + +-1 + +-1 +
+dog + +1 + +0 +
+ferret + +0 + +1 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.24 + +0 +
+pet.dog-intercept + +-1 + +0.115 + +-8.66 + +0 +
+pet.ferret-intercept + +4 + +0.115 + +34.64 + +0 +
+
+
+
+

+Difference

+

A slightly different form of contrast coding is difference coding, also called forward, backward, or successive differences coding. It compares each level to the previous one, rather than to a baseline level.

+
+df$pet <- contr_code_difference(df$pet)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.dog-cat + +.ferret-dog +
+cat + +-0.667 + +-0.333 +
+dog + +0.333 + +-0.333 +
+ferret + +0.333 + +0.667 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.dog-cat + +2 + +0.200 + +10.0 + +0 +
+pet.ferret-dog + +5 + +0.200 + +25.0 + +0 +
+
+

If you want to change which levels are compared, you can re-order the factor levels.

+
+df$pet <- contr_code_difference(df$pet, levels = c("ferret", "cat", "dog"))
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.cat-ferret + +.dog-cat +
+ferret + +-0.667 + +-0.333 +
+cat + +0.333 + +-0.333 +
+dog + +0.333 + +0.667 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.cat-ferret + +-7 + +0.200 + +-35.0 + +0 +
+pet.dog-cat + +2 + +0.200 + +10.0 + +0 +
+
+
+
+

+Helmert

+

Helmert coding sets the grand mean as the intercept. Each contrast compares one level with the mean of previous levels. This coding is somewhat different than the results from stats::contr.helmert() to make it easier to interpret the estimates. Thus, pet_ferret-cat.dog is the mean value for ferrets minus the mean value for cats and dogs averaged together (\(9-(2+4)/2\)).

+
+df$pet <- contr_code_helmert(df$pet)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.dog-cat + +.ferret-cat.dog +
+cat + +-0.5 + +-0.333 +
+dog + +0.5 + +-0.333 +
+ferret + +0.0 + +0.667 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.082 + +61.2 + +0 +
+pet.dog-cat + +2 + +0.200 + +10.0 + +0 +
+pet.ferret-cat.dog + +6 + +0.173 + +34.6 + +0 +
+
+

You can change the comparisons by reordering the levels.

+
+df$pet <- contr_code_helmert(df$pet, levels = c("ferret", "dog", "cat"))
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + +
+ +.dog-ferret + +.cat-ferret.dog +
+ferret + +-0.5 + +-0.333 +
+dog + +0.5 + +-0.333 +
+cat + +0.0 + +0.667 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5.0 + +0.082 + +61.2 + +0 +
+pet.dog-ferret + +-5.0 + +0.200 + +-25.0 + +0 +
+pet.cat-ferret.dog + +-4.5 + +0.173 + +-26.0 + +0 +
+
+
+
+

+Polynomial

+

Polynomial coding is the default for ordered factors in R. We’ll set up a new data simulation with five ordered times.

+
+df <- sim_design(list(time = 1:5),
+                 mu = 1:5 * 0.25 + (1:5 - 3)^2 * 0.5,
+                 sd = 5, long = TRUE)
+

+

The function contr_code_poly() uses contr.poly() to set up the polynomial contrasts for the linear (^1), quadratic (^2), cubic (^3), and quartic (^4) components.

+
+df$time <- contr_code_poly(df$time)
+ + + + + + + + + +
+Contrasts + +lm(y ~ pet, df) +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+^1 + +^2 + +^3 + +^4 +
+-0.632 + +0.535 + +-0.316 + +0.120 +
+-0.316 + +-0.267 + +0.632 + +-0.478 +
+0.000 + +-0.535 + +0.000 + +0.717 +
+0.316 + +-0.267 + +-0.632 + +-0.478 +
+0.632 + +0.535 + +0.316 + +0.120 +
+		 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +2.031 + +0.223 + +9.110 + +0.000 +
+time^1 + +0.239 + +0.498 + +0.480 + +0.631 +
+time^2 + +1.946 + +0.498 + +3.905 + +0.000 +
+time^3 + +-0.198 + +0.498 + +-0.397 + +0.692 +
+time^4 + +-0.004 + +0.498 + +-0.009 + +0.993 +
+
+
+
+
+
+

+Add Contrasts

+

The function add_contrasts() lets you add contrasts to a column in a data frame and also adds new columns for each contrast (unless add_cols = FALSE). This is especially helpful if you want to test only a subset of the contrasts.

+
+df <- sim_design(list(time = 1:5),
+                 mu = 1:5 * 0.25 + (1:5 - 3)^2 * 0.5,
+                 sd = 5, long = TRUE, plot = FALSE) %>%
+  add_contrast("time", "poly")
+
+# test only the linear and quadratic contrasts
+lm(y ~ `time^1` + `time^2`, df) %>% broom::tidy()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +1.69 + +0.239 + +7.09 + +0.000 +
+time^1 + +0.78 + +0.534 + +1.46 + +0.145 +
+time^2 + +2.06 + +0.534 + +3.85 + +0.000 +
+

You can set colnames to change the default column names for the contrasts. This can be useful if you want to add different codings for the same factor or if the default names are too long.

+
+btwn <- list(condition = c("control", "experimental")) 
+
+df <- sim_design(between = btwn, n = 1, plot = FALSE) %>%
+  add_contrast("condition", "treatment", colnames = "cond.tr") %>%
+  add_contrast("condition", "anova", colnames = "cond.aov") %>%
+  add_contrast("condition", "difference", colnames = "cond.dif") %>%
+  add_contrast("condition", "sum", colnames = "cond.sum") %>%
+  add_contrast("condition", "helmert", colnames = "cond.hmt") %>%
+  add_contrast("condition", "poly", colnames = "cond.poly")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+id + +condition + +y + +cond.tr + +cond.aov + +cond.dif + +cond.sum + +cond.hmt + +cond.poly +
+S1 + +control + +0.697 + +0 + +-0.5 + +-0.5 + +1 + +-0.5 + +-0.707 +
+S2 + +experimental + +0.512 + +1 + +0.5 + +0.5 + +-1 + +0.5 + +0.707 +
+

However, if a new column has a duplicate name to an existing column, add_contrast() will automatically add a contrast suffix to the new column.

+
+btwn <- list(pet = c("cat", "dog", "ferret")) 
+
+df <- sim_design(between = btwn, n = 1, plot = FALSE) %>%
+  add_contrast("pet", "treatment") %>% 
+  add_contrast("pet", "anova") %>%
+  add_contrast("pet", "sum") %>%
+  add_contrast("pet", "difference") %>%
+  add_contrast("pet", "helmert") %>%
+  add_contrast("pet", "poly")
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+id + +pet + +y + +pet.dog-cat + +pet.ferret-cat + +pet.dog-cat.aov + +pet.ferret-cat.aov + +pet.cat-intercept + +pet.dog-intercept + +pet.dog-cat.dif + +pet.ferret-dog + +pet.dog-cat.hmt + +pet.ferret-cat.dog + +pet^1 + +pet^2 +
+S1 + +cat + +-0.72 + +0 + +0 + +-0.33 + +-0.33 + +1 + +0 + +-0.67 + +-0.33 + +-0.5 + +-0.33 + +-0.71 + +0.41 +
+S2 + +dog + +-0.36 + +1 + +0 + +0.67 + +-0.33 + +0 + +1 + +0.33 + +-0.33 + +0.5 + +-0.33 + +0.00 + +-0.82 +
+S3 + +ferret + +-1.43 + +0 + +1 + +-0.33 + +0.67 + +-1 + +-1 + +0.33 + +0.67 + +0.0 + +0.67 + +0.71 + +0.41 +
+
+
+

+Examples

+
+

+2x2 Design

+

Let’s use simulated data with empirical = TRUE to explore how to interpret interactions between two 2-level factors coded in different ways.

+
+mu <- c(0, 4, 6, 10)
+df <- sim_design(between = list(time = c("am", "pm"),
+                                pet = c("cat", "dog")),
+                 n = c(50, 60, 70, 80), mu = mu, empirical = TRUE)
+

+

The table below shows the cell and marginal means. The notation \(Y_{..}\) is used to denote a mean for a specific grouping. The . is used to indicate the mean over all groups of that factor; Y.. is the grand mean. While you’ll usually see the subscripts written as numbers to indicate the factor levels, we’re using letters here so you don’t have to keep referring to the order of factors and levels.

+ + + + + + + + + + + + + + + + + + + + + + + + + + + +
catdogmean
am\(Y_{ca} = 0\)\(Y_{da} = 4\)\(Y_{.a} = 2\)
pm\(Y_{cp} = 6\)\(Y_{dp} = 10\)\(Y_{.p} = 8\)
mean\(Y_{c.} = 3\)\(Y_{d.} = 7\)\(Y_{..} = 5\)
+
+

+
+
+Treatment
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptcat when am\(Y_{ca}\)\(0\)
pet.dog-catdog minus cat, when am\(Y_{da} - Y_{ca}\)\(4 - 0 = 4\)
time.pm-ampm minus am, for cats\(Y_{cp} - Y_{ca}\)\(6 - 0 = 6\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((10 - 6) - (4 - 0) = 0\)
+
+df %>%
+  add_contrast("pet", "treatment") %>%
+  add_contrast("time", "treatment") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +0 + +0.141 + +0.0 + +1 +
+pet.dog-cat + +4 + +0.191 + +20.9 + +0 +
+time.pm-am + +6 + +0.185 + +32.4 + +0 +
+pet.dog-cat:time.pm-am + +0 + +0.252 + +0.0 + +1 +
+
+
+
+Anova
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(5\)
pet.dog-catmean dog minus mean cat\(Y_{d.} - Y_{c.}\)\(7 - 3 = 4\)
time.pm-ammean pm minus mean am\(Y_{.p} - Y_{.a}\)\(8 - 2 = 6\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((10 - 6) - (4 - 0) = 0\)
+
+df %>%
+  add_contrast("pet", "anova") %>%
+  add_contrast("time", "anova") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.063 + +79.4 + +0 +
+pet.dog-cat + +4 + +0.126 + +31.8 + +0 +
+time.pm-am + +6 + +0.126 + +47.6 + +0 +
+pet.dog-cat:time.pm-am + +0 + +0.252 + +0.0 + +1 +
+
+
+
+Sum
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(3\)
pet.cat-interceptmean cat minus grand mean\(Y_{c.} - Y_{..}\)\(3 - 5 = -2\)
time.am-interceptmean am minus grand mean\(Y_{.a} - Y_{..}\)\(2 - 5 = -3\)
pet.cat-intercept:time.am-interceptcat minus mean when am, minus cat minus mean when pm, divided by 2\(\frac{(Y_{ca} - Y_{.a}) - (Y_{cp} - Y_{.p})}{2}\)\(\frac{(0 - 2) - (6 - 8)}{2} = 0\)
+
+df %>%
+  add_contrast("pet", "sum") %>%
+  add_contrast("time", "sum") %>%
+  lm(y ~ pet * time, .) %>%  
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.063 + +79.4 + +0 +
+pet.cat-intercept + +-2 + +0.063 + +-31.8 + +0 +
+time.am-intercept + +-3 + +0.063 + +-47.6 + +0 +
+pet.cat-intercept:time.am-intercept + +0 + +0.063 + +0.0 + +1 +
+
+
+
+Difference
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(5\)
pet.dog-catmean dog minus mean cat\(Y_{d.} - Y_{c.}\)\(7 - 3 = 4\)
time.pm-ammean pm minus mean am\(Y_{.p} - Y_{.a}\)\(8 - 2 = 6\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((10 - 6) - (4 - 0) = 0\)
+
+df %>%
+  add_contrast("pet", "difference") %>%
+  add_contrast("time", "difference") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.063 + +79.4 + +0 +
+pet.dog-cat + +4 + +0.126 + +31.8 + +0 +
+time.pm-am + +6 + +0.126 + +47.6 + +0 +
+pet.dog-cat:time.pm-am + +0 + +0.252 + +0.0 + +1 +
+
+
+
+Helmert
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(5\)
pet.dog-catmean dog minus mean cat\(Y_{d.} - Y_{c.}\)\(7 - 3 = 4\)
time.pm-ammean pm minus mean am\(Y_{.p} - Y_{.a}\)\(8 - 2 = 6\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((10 - 6) - (4 - 0) = 0\)
+
+df %>%
+  add_contrast("pet", "helmert") %>%
+  add_contrast("time", "helmert") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +5 + +0.063 + +79.4 + +0 +
+pet.dog-cat + +4 + +0.126 + +31.8 + +0 +
+time.pm-am + +6 + +0.126 + +47.6 + +0 +
+pet.dog-cat:time.pm-am + +0 + +0.252 + +0.0 + +1 +
+
+
+
+
+

+

N.B. In the case of 2-level factors, anova, difference, and Helmert coding are identical. Treatment coding differs only in the intercept.

+

Remember that interactions can always be described in two ways, since (A1 - A2) - (B1 - B2) == (A1 - B1) - (A2 - B2). Therefore, “dog minus cat when pm, minus dog minus cat when am” is the same as “pm minus am for dogs, minus pm minus am for cats”. The way you describe it in a paper depends on which version maps onto your hypothesis more straightforwardly. The examples above might be written as “the difference between dogs and cats was bigger in the evening than the morning” or “the difference between evening and morning was bigger for dogs than for cats”. Make sure you check the plots to make sure you are describing the relationships in the right direction.

+
+
+

+2x3 Design

+

Let’s use simulated data with empirical = TRUE to explore how to interpret interactions between a 2-level factor and a 3-level factor coded in different ways.

+
+mu <- c(0, 5, 7, 6, 2, 1)
+df <- sim_design(between = list(time = c("am", "pm"),
+                                pet = c("cat", "dog", "ferret")),
+                 n = c(50, 60, 70, 80, 90, 100), mu = mu, empirical = TRUE)
+

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
catdogferretmean
am\(Y_{ca} = 0\)\(Y_{da} = 5\)\(Y_{fa} = 7\)\(Y_{.a} = 4\)
pm\(Y_{cp} = 6\)\(Y_{dp} = 2\)\(Y_{fp} = 1\)\(Y_{.p} = 3\)
mean\(Y_{c.} = 3\)\(Y_{d.} = 3.5\)\(Y_{f.} = 4\)\(Y_{..} = 3.5\)
+
+

+
+
+Treatment
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptcat when am\(Y_{ca}\)\(0\)
pet.dog-catdog minus cat, when am\(Y_{da} - Y_{ca}\)\(5 - 0 = 5\)
pet.ferret-catferret minus cat, when am\(Y_{fa} - Y_{ca}\)\(7 - 0 = 7\)
time.pm-ampm minus am, for cats\(Y_{cp} - Y_{ca}\)\(6 - 0 = 6\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((2 - 6) - (5 - 0) = -9\)
pet.ferret-cat:time.pm-amferret minus cat when pm, minus ferret minus cat when am\((Y_{fp} - Y_{cp}) - (Y_{fa} - Y_{ca})\)\((1 - 6) - (7 - 0) = -12\)
+
+df %>%
+  add_contrast("pet", "treatment") %>%
+  add_contrast("time", "treatment") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +0 + +0.141 + +0.0 + +1 +
+pet.dog-cat + +5 + +0.191 + +26.1 + +0 +
+pet.ferret-cat + +7 + +0.185 + +37.8 + +0 +
+time.pm-am + +6 + +0.180 + +33.3 + +0 +
+pet.dog-cat:time.pm-am + +-9 + +0.246 + +-36.7 + +0 +
+pet.ferret-cat:time.pm-am + +-12 + +0.238 + +-50.4 + +0 +
+
+
+
+Anova
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(3.5\)
pet.dog-catmean dog minus mean cat\(Y_{d.} - Y_{c.}\)\(3.5 - 3 = 0.5\)
pet.ferret-catmean ferret minus mean cat\(Y_{f.} - Y_{c.}\)\(4 - 3 = 1\)
time.pm-ammean pm minus mean am\(Y_{.p} - Y_{.a}\)\(3 - 4 = -1\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((2 - 6) - (5 - 0) = -9\)
pet.ferret-cat:time.pm-amferret minus cat when pm, minus ferret minus cat when am\((Y_{fp} - Y_{cp}) - (Y_{fa} - Y_{ca})\)\((1 - 6) - (7 - 0) = -12\)
+
+df %>%
+  add_contrast("pet", "anova") %>%
+  add_contrast("time", "anova") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +3.5 + +0.048 + +72.22 + +0 +
+pet.dog-cat + +0.5 + +0.123 + +4.07 + +0 +
+pet.ferret-cat + +1.0 + +0.119 + +8.39 + +0 +
+time.pm-am + +-1.0 + +0.097 + +-10.32 + +0 +
+pet.dog-cat:time.pm-am + +-9.0 + +0.246 + +-36.66 + +0 +
+pet.ferret-cat:time.pm-am + +-12.0 + +0.238 + +-50.36 + +0 +
+
+
+
+Sum
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(3\)
pet.cat-interceptmean cat minus grand mean\(Y_{c.} - Y_{..}\)\(3 - 3.5 = -0.5\)
pet.dog-interceptmean dog minus grand mean\(Y_{d.} - Y_{..}\)\(3.5 - 3.5 = 0\)
time.am-interceptmean am minus grand mean\(Y_{.a} - Y_{..}\)\(4 - 3.5 = 0.5\)
pet.cat-intercept:time.am-interceptcat minus mean when am, minus cat minus mean when pm, divided by 2\(\frac{(Y_{ca} - Y_{.a}) - (Y_{cp} - Y_{.p})}{2}\)\(\frac{(0 - 4) - (6 - 3)}{2} = -3.5\)
pet.dog-intercept:time.am-interceptdog minus mean when am, minus dog minus mean when pm, divided by 2\(\frac{(Y_{da} - Y_{.a}) - (Y_{dp} - Y_{.p})}{2}\)\(\frac{(5 - 4) - (2 - 3)}{2} = 1\)
+
+df %>%
+  add_contrast("pet", "sum") %>%
+  add_contrast("time", "sum") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +3.5 + +0.048 + +72.22 + +0 +
+pet.cat-intercept + +-0.5 + +0.071 + +-7.03 + +0 +
+pet.dog-intercept + +0.0 + +0.068 + +0.00 + +1 +
+time.am-intercept + +0.5 + +0.048 + +10.32 + +0 +
+pet.cat-intercept:time.am-intercept + +-3.5 + +0.071 + +-49.22 + +0 +
+pet.dog-intercept:time.am-intercept + +1.0 + +0.068 + +14.64 + +0 +
+
+
+
+Difference
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(3.5\)
pet.dog-catmean dog minus mean cat\(Y_{d.} - Y_{c.}\)\(3.5 - 3 = 0.5\)
pet.ferret-dogmean ferret minus mean dog\(Y_{f.} - Y_{d.}\)\(4 - 3.5 = 0.5\)
time.pm-ammean pm minus mean am\(Y_{.p} - Y_{.a}\)\(3 - 4 = -1\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((2 - 6) - (5 - 0) = -9\)
pet.ferret-dog:time.pm-amferret minus dog when pm, minus ferret minus dog when am\((Y_{fp} - Y_{dp}) - (Y_{fa} - Y_{da})\)\((1 - 2) - (7 - 5) = -3\)
+
+df %>%
+  add_contrast("pet", "difference") %>%
+  add_contrast("time", "difference") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +3.5 + +0.048 + +72.22 + +0 +
+pet.dog-cat + +0.5 + +0.123 + +4.07 + +0 +
+pet.ferret-dog + +0.5 + +0.114 + +4.38 + +0 +
+time.pm-am + +-1.0 + +0.097 + +-10.32 + +0 +
+pet.dog-cat:time.pm-am + +-9.0 + +0.246 + +-36.66 + +0 +
+pet.ferret-dog:time.pm-am + +-3.0 + +0.228 + +-13.15 + +0 +
+
+
+
+Helmert
+ ++++++ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
terminterpretationformulavalue
interceptgrand mean\(Y_{..}\)\(3.5\)
pet.dog-catmean dog minus mean cat\(Y_{d.} - Y_{c.}\)\(3.5 - 3 = 0.5\)
pet.ferret-cat.dogmean ferret minus mean of cat and dog\(Y_{f.} - \frac{Y_{c.} + Y_{d.}}{2}\)\(4 - \frac{3 + 3.5}{2} = 0.75\)
time.pm-ammean pm minus mean am\(Y_{.p} - Y_{.a}\)\(3 - 4 = -1\)
pet.dog-cat:time.pm-amdog minus cat when pm, minus dog minus cat when am\((Y_{dp} - Y_{cp}) - (Y_{da} - Y_{ca})\)\((2 - 6) - (5 - 0) = -9\)
pet.ferret-cat.dog:time.pm-amferret minus mean of cat and dog when pm, minus ferret minus mean of cat and dog when am\((Y_{fp} - \frac{Y_{cp} + Y_{dp}}{2}) - (Y_{fa} - \frac{Y_{ca} + Y_{da}}{2})\)\((1 - \frac{6 + 2}{2}) - (7 - \frac{0 + 5}{2}) = -7.5\)
+
+df %>%
+  add_contrast("pet", "helmert") %>%
+  add_contrast("time", "helmert") %>%
+  lm(y ~ pet * time, .) %>% 
+  broom::tidy() %>% kable() %>% kable_styling()
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+term + +estimate + +std.error + +statistic + +p.value +
+(Intercept) + +3.50 + +0.048 + +72.22 + +0 +
+pet.dog-cat + +0.50 + +0.123 + +4.07 + +0 +
+pet.ferret-cat.dog + +0.75 + +0.099 + +7.56 + +0 +
+time.pm-am + +-1.00 + +0.097 + +-10.32 + +0 +
+pet.dog-cat:time.pm-am + +-9.00 + +0.246 + +-36.66 + +0 +
+pet.ferret-cat.dog:time.pm-am + +-7.50 + +0.198 + +-37.81 + +0 +
+
+
+
+
+

+

N.B. In this case, difference coding is identical to anova coding except that the second pet contrast is ferret versus dog instead of ferret versus cat.

+
+
+

+3x3 Design

+

Please just don’t. You’re going to break my head.

+
+
+ + + + + + + + + + + + + + + + diff --git a/docs/articles/contrasts_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/contrasts_files/figure-html/unnamed-chunk-1-1.png new file mode 100644 index 00000000..fb9beb41 Binary files /dev/null and b/docs/articles/contrasts_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/articles/contrasts_files/figure-html/unnamed-chunk-16-1.png b/docs/articles/contrasts_files/figure-html/unnamed-chunk-16-1.png new file mode 100644 index 00000000..57061735 Binary files /dev/null and b/docs/articles/contrasts_files/figure-html/unnamed-chunk-16-1.png differ diff --git a/docs/articles/contrasts_files/figure-html/unnamed-chunk-23-1.png b/docs/articles/contrasts_files/figure-html/unnamed-chunk-23-1.png new file mode 100644 index 00000000..ebc5f0f8 Binary files /dev/null and b/docs/articles/contrasts_files/figure-html/unnamed-chunk-23-1.png differ diff --git a/docs/articles/contrasts_files/figure-html/unnamed-chunk-30-1.png b/docs/articles/contrasts_files/figure-html/unnamed-chunk-30-1.png new file mode 100644 index 00000000..82890866 Binary files /dev/null and b/docs/articles/contrasts_files/figure-html/unnamed-chunk-30-1.png differ diff --git a/docs/articles/contrasts_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/contrasts_files/header-attrs-2.10.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/contrasts_files/header-attrs-2.10.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/contrasts_files/kePrint-0.0.1/kePrint.js b/docs/articles/contrasts_files/kePrint-0.0.1/kePrint.js new file mode 100644 index 00000000..e6fbbfc4 --- /dev/null +++ b/docs/articles/contrasts_files/kePrint-0.0.1/kePrint.js @@ -0,0 +1,8 @@ +$(document).ready(function(){ + if (typeof $('[data-toggle="tooltip"]').tooltip === 'function') { + $('[data-toggle="tooltip"]').tooltip(); + } + if ($('[data-toggle="popover"]').popover === 'function') { + $('[data-toggle="popover"]').popover(); + } +}); diff --git a/docs/articles/contrasts_files/lightable-0.0.1/lightable.css b/docs/articles/contrasts_files/lightable-0.0.1/lightable.css new file mode 100644 index 00000000..3be3be90 --- /dev/null +++ b/docs/articles/contrasts_files/lightable-0.0.1/lightable.css @@ -0,0 +1,272 @@ +/*! + * lightable v0.0.1 + * Copyright 2020 Hao Zhu + * Licensed under MIT (https://github.com/haozhu233/kableExtra/blob/master/LICENSE) + */ + +.lightable-minimal { + border-collapse: separate; + border-spacing: 16px 1px; + width: 100%; + margin-bottom: 10px; +} + +.lightable-minimal td { + margin-left: 5px; + margin-right: 5px; +} + +.lightable-minimal th { + margin-left: 5px; + margin-right: 5px; +} + +.lightable-minimal thead tr:last-child th { + border-bottom: 2px solid #00000050; + empty-cells: hide; + +} + +.lightable-minimal tbody tr:first-child td { + padding-top: 0.5em; +} + +.lightable-minimal.lightable-hover tbody tr:hover { + background-color: #f5f5f5; +} + +.lightable-minimal.lightable-striped tbody tr:nth-child(even) { + background-color: #f5f5f5; +} + +.lightable-classic { + border-top: 0.16em solid #111111; + border-bottom: 0.16em solid #111111; + width: 100%; + margin-bottom: 10px; + margin: 10px 5px; +} + +.lightable-classic tfoot tr td { + border: 0; +} + +.lightable-classic tfoot tr:first-child td { + border-top: 0.14em solid #111111; +} + +.lightable-classic caption { + color: #222222; +} + +.lightable-classic td { + padding-left: 5px; + padding-right: 5px; + color: #222222; +} + +.lightable-classic th { + padding-left: 5px; + padding-right: 5px; + font-weight: normal; + color: #222222; +} + +.lightable-classic thead tr:last-child th { + border-bottom: 0.10em solid #111111; +} + +.lightable-classic.lightable-hover tbody tr:hover { + background-color: #F9EEC1; +} + +.lightable-classic.lightable-striped tbody tr:nth-child(even) { + background-color: #f5f5f5; +} + +.lightable-classic-2 { + border-top: 3px double #111111; + border-bottom: 3px double #111111; + width: 100%; + margin-bottom: 10px; +} + +.lightable-classic-2 tfoot tr td { + border: 0; +} + +.lightable-classic-2 tfoot tr:first-child td { + border-top: 3px double #111111; +} + +.lightable-classic-2 caption { + color: #222222; +} + +.lightable-classic-2 td { + padding-left: 5px; + padding-right: 5px; + color: #222222; +} + +.lightable-classic-2 th { + padding-left: 5px; + padding-right: 5px; + font-weight: normal; + color: #222222; +} + +.lightable-classic-2 tbody tr:last-child td { + border-bottom: 3px double #111111; 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+} + +.lightable-material.lightable-striped tbody td { + border: 0; +} + +.lightable-material.lightable-striped thead tr:last-child th { + border-bottom: 1px solid #ddd; +} + +.lightable-material-dark { + min-width: 100%; + white-space: nowrap; + table-layout: fixed; + font-family: Roboto, sans-serif; + border: 1px solid #FFFFFF12; + border-collapse: collapse; + margin-bottom: 10px; + background-color: #363640; +} + +.lightable-material-dark tfoot tr td { + border: 0; +} + +.lightable-material-dark tfoot tr:first-child td { + border-top: 1px solid #FFFFFF12; +} + +.lightable-material-dark th { + height: 56px; + padding-left: 16px; + padding-right: 16px; + color: #FFFFFF60; +} + +.lightable-material-dark td { + height: 52px; + padding-left: 16px; + padding-right: 16px; + color: #FFFFFF; + border-top: 1px solid #FFFFFF12; +} + +.lightable-material-dark.lightable-hover tbody tr:hover { + background-color: #FFFFFF12; +} + +.lightable-material-dark.lightable-striped tbody tr:nth-child(even) { + background-color: #FFFFFF12; +} + +.lightable-material-dark.lightable-striped tbody td { + border: 0; +} + +.lightable-material-dark.lightable-striped thead tr:last-child th { + border-bottom: 1px solid #FFFFFF12; +} + +.lightable-paper { + width: 100%; + margin-bottom: 10px; + color: #444; +} + +.lightable-paper tfoot tr td { + border: 0; +} + +.lightable-paper tfoot tr:first-child td { + border-top: 1px solid #00000020; +} + +.lightable-paper thead tr:last-child th { + color: #666; + vertical-align: bottom; + border-bottom: 1px solid #00000020; + line-height: 1.15em; + padding: 10px 5px; +} + +.lightable-paper td { + vertical-align: middle; + border-bottom: 1px solid #00000010; + line-height: 1.15em; + padding: 7px 5px; +} + +.lightable-paper.lightable-hover tbody tr:hover { + background-color: #F9EEC1; +} + +.lightable-paper.lightable-striped tbody tr:nth-child(even) { + background-color: #00000008; +} + +.lightable-paper.lightable-striped tbody td { + border: 0; +} + diff --git a/docs/articles/distributions.html b/docs/articles/distributions.html index 5c0605de..258e11b5 100644 --- a/docs/articles/distributions.html +++ b/docs/articles/distributions.html @@ -17,9 +17,9 @@ - + - + @@ -42,7 +42,7 @@ faux - 1.0.0 + 1.1.0 @@ -82,12 +82,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -111,7 +117,7 @@ -
    +

    Distribution Conversions

    diff --git a/docs/articles/distributions_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/distributions_files/header-attrs-2.10.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/distributions_files/header-attrs-2.10.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/index.html b/docs/articles/index.html index 74f45801..bf09b921 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -45,9 +45,11 @@ + + - + @@ -83,7 +85,7 @@ faux - 1.0.0 + 1.1.0
    @@ -123,12 +125,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -175,10 +183,14 @@

    Demos

    Mixed Design Simulation
    +
    Randomised Reports
    +
    Distribution Conversions
    Codebook Demo
    +
    Contrasts
    +
    diff --git a/docs/articles/plots.html b/docs/articles/plots.html index 96da2ce2..0f7058d6 100644 --- a/docs/articles/plots.html +++ b/docs/articles/plots.html @@ -17,9 +17,9 @@ - + - + @@ -42,7 +42,7 @@ faux - 1.0.0 + 1.1.0 @@ -82,12 +82,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -111,7 +117,7 @@ -
    +

    Plotting

    @@ -125,7 +131,8 @@

    Plotting

    +library(faux) +library(ggplot2)

    Plotting Designs

    @@ -222,25 +229,99 @@

    plot_design(data)

    +
    +

    +Palettes

    +

    You can change the default brewer palette from “Dark2” to any of the options (see ?ggplot2::scale_colour_brewer for the options).

    +
    +data <- sim_design(5, n = 20, mu = 1:5, plot = FALSE)
+cowplot::plot_grid(
+  plot_design(data, palette = "Purples") + ggtitle("Purples"),
+  plot_design(data, palette = "Pastel2") + ggtitle("Pastel2"),
+  plot_design(data, palette = "Spectral") + ggtitle("Spectral"),
+  nrow = 1
+)
    +

    +

    You can also add a non-brewer palette (or any other ggplot function). You will get a message about a duplicate scale, but you can suppress this with suppressMessages() or in the r chunk.

    +
    +plot_design(data) + 
+  scale_fill_viridis_d() +
+  theme_classic() +
+  xlab("") +
+  ggtitle("Plot with viridis fill and classic theme")
+#> Scale for 'fill' is already present. Adding another scale for 'fill', which
+#> will replace the existing scale.
    +

    +

    Change order of factors

    -
    +
    List the factors you want to show in the order of fill/colour, x-axis, facet row(s), facet column(s).
    
+
     data <- sim_design(c(2,2,2), n = 50, mu = 1:8, sd = 16, plot = FALSE)
-abc <- plot_design(data, "A", "B", "C", geoms = "violin")
-acb <- plot_design(data, "A", "C", "B", geoms = "violin")
-bac <- plot_design(data, "B", "A", "C", geoms = "violin")
-bca <- plot_design(data, "B", "C", "A", geoms = "violin")
-cab <- plot_design(data, "C", "A", "B", geoms = "violin")
-cba <- plot_design(data, "C", "B", "A", geoms = "violin")
+abc <- plot_design(data, "W1", "W2", "W3", geoms = "violin")
+acb <- plot_design(data, "W1", "W3", "W2", geoms = "violin")
+bac <- plot_design(data, "W2", "W1", "W3", geoms = "violin")
+bca <- plot_design(data, "W2", "W3", "W1", geoms = "violin")
+cab <- plot_design(data, "W3", "W1", "W2", geoms = "violin")
+cba <- plot_design(data, "W3", "W2", "W1", geoms = "violin")
 
 cowplot::plot_grid(abc, acb, bac, bca, cab, cba, nrow = 3, 
-                   labels = c("abc", "acb", "bac", "bca", "cab", "cba"))
    
-
    
-
    -sim_design(c(2,2,2,5), mu = 1:40, sd = 5, plot = FALSE) %>% 
-  plot_design("D", "B", "C", "A")
    
-
    
+                   labels = c("123", "132", "213", "231", "312", "321"))
    +

    +

    You can also plot a subset of the factors.

    +
    +# main effects, no interactions
+intercept <- 0
+effect_code <- list(a = -.5, b = .5)
+W1 <- lapply(effect_code, `*`, 2)
+W2 <- lapply(effect_code, `*`, -1)
+W3 <- lapply(effect_code, `*`, 0)
+
+mu <- list(W1a_W2a_W3a = intercept + W1$a + W2$a + W3$a,
+           W1b_W2a_W3a = intercept + W1$b + W2$a + W3$a,
+           W1a_W2b_W3a = intercept + W1$a + W2$b + W3$a,
+           W1b_W2b_W3a = intercept + W1$b + W2$b + W3$a,
+           W1a_W2a_W3b = intercept + W1$a + W2$a + W3$b,
+           W1b_W2a_W3b = intercept + W1$b + W2$a + W3$b,
+           W1a_W2b_W3b = intercept + W1$a + W2$b + W3$b,
+           W1b_W2b_W3b = intercept + W1$b + W2$b + W3$b)
+
+data <- sim_design(c(2,2,2), n = 50, mu = mu, sd = 2, plot = FALSE)
+
+# make plots
+geoms <- c("pointrangeSD")
+W1 <- plot_design(data, "W1", geoms = geoms) + ggtitle("W1")
+W2 <- plot_design(data, "W2", geoms = geoms) + ggtitle("W2")
+W3 <- plot_design(data, "W3", geoms = geoms) + ggtitle("W3")
+W12 <- plot_design(data, "W1", "W2", geoms = geoms) + ggtitle("W1 & W2")
+W13 <- plot_design(data, "W1", "W3", geoms = geoms) + ggtitle("W1 & W3")
+W23 <- plot_design(data, "W2", "W3", geoms = geoms) + ggtitle("W2 & W3")
+
+cowplot::plot_grid(W1, W2, W3, W12, W13, W23, nrow = 2)
    +

    +
    +
    +

    +Facet Labels

    +

    Set the labeller to “label_both” if you want facets to include the factor label.

    +
    +within <- list(
+  pet = c(cat = "Cats", dog = "Dogs"),
+  time = c(am = "Morning", pm = "Night"),
+  grp = c(ctl = "Control", exp = "Experimental"),
+  dose = 1:5
+)
+  
+factor_labels  <- list(pet = "Type of Pet",
+                       time = "Time of Day",
+                       grp = "Group",
+                       dose = "Treatment Dose")
+
+sim_design(within, vardesc = factor_labels,
+           mu = 1:40, sd = 5, plot = FALSE) %>% 
+  plot_design("dose", "pet", "time", "grp", labeller = "label_both", palette = "Spectral")
    +

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b/docs/articles/plots_files/header-attrs-2.10.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/random_reports.html b/docs/articles/random_reports.html new file mode 100644 index 00000000..e61e6cc3 --- /dev/null +++ b/docs/articles/random_reports.html @@ -0,0 +1,1278 @@ + + + + + + + +Randomised Reports • faux + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Randomised Reports

    + + + Source: vignettes/random_reports.Rmd + + +
    + + + +

    Have you ever wanted to create unique versions of datasets for each student in a class? This vignette walks through the process of setting up a simple study with a subject table and a questionnaire table.

    +

    The subjects have varying demographic attributes and are assigned to a counterbalanced condition and version of the study. Some of the demographic data is missing.

    +

    The questionnaire has a variable number of questions, answered on a 7-point Likert scale. Some of the questions are reverse-coded, indicated by the column names. There are some missing observations.

    +

    The data is set up with fixed effects for condition, version, and subject language that you can vary between students. The DV is created using a mixed effects model formula to simulate realistic random intercepts and slopes, so these data would be suitable for analysis with mixed effects models, or you can instruct students to aggregate the questionnaire data to analyse with single-level models.

    +

    A custom assignment is created for each student, where they are told the questionnaire contains scores on one of three topics, and are asked to determine if there is an effect of one factor for a subgroup of another factor. The categories chosen here are pretty arbitrary, but you can change this to make sense for your subject area.

    +

    Finally, the subject table, questionnaire table, and instructions are saved to a folder with the student ID for distribution to each student. The simulation is seeded with the student ID, ensuring that each student gets a unique, but reproducible assignment.

    +
    +

    +Setup

    +

    Some of the functions below require version 1.1.0 of faux (add_random(), add_between(), add_ranef() and add_contrast()). This version has been submitted to CRAN, and is available on github. Any feedback would be welcome.

    +
    +# devtools::install_github("debruine/faux")
+library(faux)
+library(dplyr)
+library(tidyr)
+library(purrr)
+library(ggplot2)
+library(readr)
+library(glue)
+theme_set(theme_minimal())
    +

    Start your script by setting a seed that is unique to each student. Below is a useful function you can use to generate a seed from a student ID, even if it contains letters.

    +
    +seed_from_string <- function(seed) {
+  if (is.numeric(seed)) return(set.seed(seed))
+  
+  # otherwise set from characters
+  seed %>%
+    openssl::md5() %>%       # turn into something with lots of numbers
+    gsub("[a-z]", "", .) %>% # get rid of letters
+    substr(1, 8) %>%         # select the first 8 numbers
+    set.seed()
+}
+
+student_id <- "8675309J"
+seed_from_string(student_id)
    +
    +
    +

    +Simulate subjects

    +

    Now, make a table of subjects.

    +
    +

    +Random factor

    +

    In this example, I’m going to choose a random number of subjects between 300 and 400.

    +
    +subj_n <- sample(300:400, 1)
    +

    The function add_random() creates the random effects structure.

    +
    +subjects <- add_random(subj_id = subj_n)
    + + + + + + + + + + + + + + + + + + + + + + + + +
    +subj_id +
    +s001 +
    +s002 +
    +s003 +
    +s004 +
    +s005 +
    +s006 +
    +
    +
    +

    +Gender

    +

    Now add a between-subject factor of gender with three levels: “female”, “male”, and “nonbinary”. If you set the probability of each as proportions, they will be randomly sampled.

    +
    +subjects <- subjects %>%
+  add_between(gender = c("female", "male", "nonbinary"),
+              .prob = c(.3, .6, .1))
    + + + + + + + + + + + + + + + + + + + +
    +gender + +n +
    +female + +129 +
    +male + +215 +
    +nonbinary + +52 +
    +

    It’s realistic for some people to not want to disclose their gender, so you can use messy() to set some proportion of the gender column to NAs.

    +
    +subjects <- subjects %>%
+  messy(prop = 0.05, "gender")
    + + + + + + + + + + + + + + + + + + + + + + + +
    +gender + +n +
    +female + +125 +
    +male + +202 +
    +nonbinary + +50 +
    +NA + +19 +
    +
    +
    +

    +Age

    +

    Now add an age column. I’m using 18 + rpois(nrow(.), 3) to make a distribution that’s pretty typical of undergraduate students, but you can choose a simulation function that is relevant to your questions. I’m also replacing 5% of the values with the word “missing”, which will convert this to a chaacter column because I want students to practice dealing with non-standard missing values.

    +
    +subjects <- subjects %>%
+  mutate(age = 18 + rpois(nrow(.), 3)) %>%
+  messy(prop = 0.05, "age", replace = "missing")
    +

    +
    +
    +

    +Language

    +

    Add a between-subjects factor of language, with 70% monolingual and 30% bilingual.

    +
    +subjects <- subjects %>%
+  add_between(language = c("monolingual", "bilingual"),
+              .prob = c(.7, .3))
    +
    +
    +

    +Experiment factors

    +

    I want condition and version to be crossed so that there’s an equal number of subjects in each cell. Specifying both in the same add_between() accomplishes this.

    +
    +subjects <- subjects %>%
+  add_between(condition = c("A", "B"),
+              version = c("V1", "V2"))
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +condition + +version + +n +
    +A + +V1 + +99 +
    +A + +V2 + +99 +
    +B + +V1 + +99 +
    +B + +V2 + +99 +
    +
    +
    +

    +Subject function

    +

    Now wrap the code for making the subject table in a function. This makes it easy to make more than one subject table for a simulated study, for example, to have students practice combining tables.

    +

    You can also add other arguments that you might want to vary, like the names of the condition and version levels. Set the defaults to what we used in the example above.

    +
    +make_subjects <- function(subj_n = sample(300:400, 1),
+                          cond_levels = c("A", "B"),
+                          vers_levels = c("V1", "V2")) {
+  add_random(subj_id = subj_n) %>%
+    add_between(gender = c("female", "male", "nonbinary"),
+                .prob = c(.3, .6, .1)) %>%
+    messy(prop = 0.05, "gender") %>%
+    mutate(age = 18 + rpois(nrow(.), 3)) %>%
+    messy(prop = 0.05, "age", replace = "missing") %>%
+    add_between(language = c("monolingual", "bilingual"),
+                .prob = c(.7, .3)) %>%
+    add_between(condition = cond_levels,
+                version = vers_levels)
+}
+
+subjects <- make_subjects()
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +subj_id + +gender + +age + +language + +condition + +version +
    +s001 + +female + +20 + +bilingual + +A + +V1 +
    +s002 + +female + +19 + +monolingual + +A + +V2 +
    +s003 + +male + +23 + +bilingual + +B + +V1 +
    +s004 + +female + +21 + +monolingual + +B + +V2 +
    +s005 + +male + +22 + +monolingual + +A + +V1 +
    +s006 + +male + +24 + +bilingual + +A + +V2 +
    +
    +
    +
    +

    +Simulate questions

    +

    Next, we need to set up the questionnaire. I’m going to do this by adding the question data to the subject table. We’ll extract the questions into a separate table later, but this will make it easier to set up the DV using a mixed effects model equation.

    +
    +

    +Random factor

    +

    First, we’ll add the random factor of question, randomly creating 5 to 15 question IDs. The code below adds a cross-classified random factor, where each subject sees all of the questions. See the Mixed Design Simulation tutorial to see how to add nested random factors.

    +
    +quest_n <- sample(5:15, 1)
+
+questions <- subjects %>%
+  add_random(quest_id = quest_n)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +subj_id + +gender + +age + +language + +condition + +version + +quest_id +
    +s001 + +female + +20 + +bilingual + +A + +V1 + +q1 +
    +s001 + +female + +20 + +bilingual + +A + +V1 + +q2 +
    +s001 + +female + +20 + +bilingual + +A + +V1 + +q3 +
    +s001 + +female + +20 + +bilingual + +A + +V1 + +q4 +
    +s001 + +female + +20 + +bilingual + +A + +V1 + +q5 +
    +s002 + +female + +19 + +monolingual + +A + +V2 + +q1 +
    +
    +
    +

    +Question reversing

    +

    I want some of the questions to be reverse-coded, so I’m adding a between-question factor of reversed with levels “F” and “R”. Here, we specify the .by = argument to show that this factor should vary by question. We didn’t need this in the code above because there was only one random factor to vary over, but by default this function varies over all random factors if there is more than one.

    +

    I set .shuffle = TRUE so that the questions are randomly assigned forward and reversed values, rather than odd questions being forward and even questions being reversed. This will require a more sophisticated method to wrangle the resulting data, and the randomisation means that different students’ datasets need different code.

    +
    +questions <- questions %>%
+  add_between(.by = "quest_id", reversed = c("F", "R"), .shuffle = TRUE)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +quest_id + +reversed +
    +q1 + +R +
    +q2 + +F +
    +q3 + +R +
    +q4 + +F +
    +q5 + +F +
    +
    +
    +

    +Random effects

    +

    Now we set the random effects structure. The function add_ranef() lets you add columns that sample values from a normal distribution with a mean of 0 and the specified SD. If you have example data, you can get realistic values for the random effects SDs from a mixed effects model (see this tutorial).

    +

    Here, we’re setting the SD for the subject’s random intercept to 2, and the SDs for the random slopes of language, condition, and version to 1. We set a small, positive correlation among all of these random effect. Each time you run this function, different values from this joint distribution will be sampled.

    +

    We also add a random intercept by question to account for the fact that some questions will have higher or lower values than the mean.

    +

    Finally add an overall error term, sigma, which will vary by subject and question (so you can skip the first .by argument).

    +
    +questions <- questions %>%
+  add_ranef(.by = "subj_id", 
+            s0i = 2, 
+            s_lang = 1,
+            s_cond = 1,
+            s_vers = 1, 
+            .cors = 0.2) %>%
+  add_ranef(.by = "quest_id", q0i = 1) %>%
+  add_ranef(sigma = 3)
    +
    +
    +

    +Fixed effects

    +

    Now we can calculate the DV. First, I want the fixed effects in each dataset to vary, so I’m setting the fixed effect values by sampling from -05., 0 and +0.5 for each of language, condition, and version.

    +
    +lang_effect <- sample(c(-0.5, 0, 0.5), 1)
+cond_effect <- sample(c(-0.5, 0, 0.5), 1)
+vers_effect <- sample(c(-0.5, 0, 0.5), 1)
    +
    +
    +

    +Factor contrasts

    +

    Next, we add contrasts to recode the fixed factors into numbers. See the contrasts vignette for more details. For a 2-level factor, this sets the first level of the factor to a value of -0.5 and the second level to +0.5.

    +
    +questions <- questions %>%
+  add_contrast("language", add_cols = T, colnames = "lang") %>%
+  add_contrast("condition", add_cols = T, colnames = "cond") %>%
+  add_contrast("version", add_cols = T, colnames = "vers")
    +
    +
    +

    +Calculate DV

    +

    Now, we can calculate the DV as the sum of the grand mean (0), plus the random intercepts (s0i and q0i). This simulates how some subjects tend to use higher or lower points on the scale, and some questions have higher or lower mean values.

    +

    Next, we add the coded versions of each fixed factor, multiplied by their effect size plus the subject’s random slope for that effect. This simulates how some subjects show systematically larger or smaller effects.

    +

    Finally, we add the error term (sigma).

    +
    +questions <- questions %>%
+  mutate(dv = 0 + s0i + q0i +
+           (s_lang + lang_effect) * lang + 
+           (s_cond + cond_effect) * cond + 
+           (s_vers + vers_effect) * vers +
+           sigma)
    +

    +
    +
    +

    +Recode DV

    +

    For this assignment, I want the DV to be from a Likert scale, so I’m going to convert dv from a normal distribution to a Likert scale with 7 points with the proportions below. The second mutate reverses the questions that should be reverse-coded.

    +
    +questions <- questions %>%
+  mutate(dv = norm2likert(dv, prob = c(1, 2, 4, 6, 10, 5, 1)),
+         dv = ifelse(reversed == "R", abs(dv - 8), dv)
+  )
    +

    +
    +
    +

    +Reshape data

    +

    Here, I’m creating a quest_name column from the quest_id and reversed columns. sample(2:3) returns these columns in a random order. I then unite the columns with a randomly sampled separator. This will produce question names like F-q04 or q10 R. While putting information about the questions in the name isn’t great practice for recording data, it’s unfortunately very common and dealing with it is a skill I want my students to practice.

    +
    +sep <- sample(c("_", "", "-", " "), 1)
+
+questions <- questions %>% 
+  select(subj_id, quest_id, reversed, dv) %>%
+  unite(col = quest_name, sample(2:3), sep = sep)
    +

    I then make 0.5% of the observations missing, pivot the table from long to wide format, and sample 95% of the subjects, so that the number of subjects in the subjects table doesn’t exactly match the number in the questions table, and is in a different order.

    +
    +questions <- questions %>% 
+  messy(prop = .005, "dv") %>%
+  pivot_wider(names_from = quest_name, values_from = dv) %>%
+  slice_sample(prop = .95)
    +
    +
    +

    +Questionnaire function

    +

    Now wrap the code for making the questionnaire table in a function. You can use this to make several questionnaires per student to practice joining tables.

    +

    This code needs the subjects table as an argument. You can add the other randomised values as arguments. I also added likert_prob as an argument so you can easily customise the number of points on the Likert scale and their relative proportions.

    +
    +# randomise between students
+make_quest <- function(subjects,
+                       quest_n = 5:15,
+                       sep = c("_", "", "-", " "),
+                       lang_effect = c(-0.5, 0, 0.5),
+                       cond_effect = c(-0.5, 0, 0.5),
+                       vers_effect = c(-0.5, 0, 0.5),
+                       likert_prob = c(.05, .1, .2, .3, .2, .1, .05)
+                       ) {
+  # sample values
+  quest_n <- sample(quest_n, 1)
+  sep <- sample(sep, 1)
+  lang_effect <- sample(lang_effect, 1)
+  cond_effect <- sample(cond_effect, 1)
+  vers_effect <- sample(vers_effect, 1)
+
+  subjects %>%
+    add_random(quest_id = quest_n) %>%
+    add_between("quest_id", reversed = c("F", "R")) %>%
+    add_ranef("subj_id", 
+              s0i = 2, 
+              s_lang = 1,
+              s_cond = 1,
+              s_vers = 1, 
+              .cors = 0.2) %>%
+    add_ranef("quest_id", q0i = 1) %>%
+    add_ranef(sigma = 3) %>%
+    add_contrast("language", add_cols = T, colnames = "lang") %>%
+    add_contrast("condition", add_cols = T, colnames = "cond") %>%
+    add_contrast("version", add_cols = T, colnames = "vers") %>%
+    mutate(dv = s0i + q0i + sigma +
+             (s_lang + lang_effect) * lang + 
+             (s_cond + cond_effect) * cond + 
+             (s_vers + vers_effect) * vers,
+           dv = norm2likert(dv, prob = likert_prob),
+           dv = ifelse(reversed == "R", abs(dv - 8), dv)
+    ) %>%
+    select(subj_id, quest_id, reversed, dv) %>%
+    unite(quest_name, sample(2:3), sep = sep) %>%
+    messy(prop = .005, "dv") %>%
+    pivot_wider(names_from = quest_name, values_from = dv) %>%
+    slice_sample(prop = .95)
+}
+
+questions <- make_quest(subjects)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +subj_id + +F-q1 + +R-q2 + +F-q3 + +R-q4 + +F-q5 + +R-q6 + +F-q7 +
    +s267 + +4 + +5 + +1 + +6 + +5 + +6 + +2 +
    +s098 + +5 + +1 + +6 + +2 + +4 + +3 + +5 +
    +s023 + +5 + +5 + +7 + +5 + +6 + +6 + +2 +
    +s259 + +5 + +1 + +3 + +4 + +4 + +7 + +3 +
    +s249 + +7 + +2 + +7 + +4 + +5 + +3 + +5 +
    +s008 + +6 + +5 + +6 + +3 + +6 + +5 + +3 +
    +
    +
    +
    +

    +Generate random assignment

    +

    I also want each student to have a different research question to answer with these data.

    +
    +

    +Write text

    +

    First, write the text you want to give to the students. You can use fancier methods for this, like parametrised R Markdown reports, but this is a simple and effective strategy. Put variables you want to randomise inside curly brackets, and we’ll sample those and insert the using glue::glue().

    +
    +research_question_text <- paste(sep = "\n\n",
+  "=== Reproducible Report for {student_id} ===",
+  
+  "Your questionnaire measures {topic}. Your research question is to determine if the {topic} score differs by {iv} for the subset of the sample where {subset} is {level}.",
+  
+  "The {quest_n} {topic} questionnaire items are on a scale from 1 (low) to 7 (high), but some of the items need to be reverse-coded. This is indicated by the letter R in the question column name. You create the {topic} score by summing the questions.",
+  
+  "Watch out for missing data. You can decide whether to omit subjects with missing data or replace the missing data; just explain your choice and its consequences in the report.",
+  
+  "The report should be written like a summary document for your PI. You don't need to show your code in the rendered report. Include summary tables and/or plots with subject demographics from both the full data set and the subset you'll analyse. Visualise the relationship of interest. Conduct a GLM analysis to answer the research question above. Finally, do a power analysis to determine the sample size you would need to have {power}% power to detect a 0.5-point difference between the levels of {iv} with an alpha criterion of {alpha}."
+)
    +

    You can save this to an external file if you want and read it from there. In this way, you can create more than one set of template instructions and also randomly assign that.

    +
    +# write to a template file
+write(research_question_text, "template_instructions.txt")
    +
    +
    +

    +Define random aspects

    +

    Now define the variables from the text above. I want the iv and subset to be two different factors from the list of “language”, “condition”, and “version”. Then I sample one of the levels of the subset factor.

    +

    I sample a topic from “stress”, “extroversion”, and “disgust”. You can choose relevant topics for your discipline, of course.

    +

    I also sample an alpha level and power for the power calculation aspect of the assessment. This reinforces a lesson I want to teach about alpha = 0.5 and target power = 0.8 being mindless defaults that we should question. Justify everything.

    +
    +factors <- c("language", "condition", "version")
+two_factors <- sample(factors, 2)
+iv <- two_factors[[1]]
+subset <- two_factors[[2]]
+level <- subjects[[subset]] %>% unique() %>% sample(1)
+topic <- sample(c("stress", "extroversion", "disgust"), 1)
+alpha <- sample(c(.005, .01, .05), 1)
+power <- sample(c(80, 85, 90), 1)
    +

    This will produce the following text.

    +
    +instructions <- readLines("template_instructions.txt") %>%
+  paste(collapse = "\n") %>%
+  glue::glue()
    +
    +

    === Reproducible Report for 8675309J ===

    +

    Your questionnaire measures extroversion. Your research question is to determine if the extroversion score differs by version for the subset of the sample where condition is A.

    +

    The 5 extroversion questionnaire items are on a scale from 1 (low) to 7 (high), but some of the items need to be reverse-coded. This is indicated by the letter R in the question column name. You create the extroversion score by summing the questions.

    +

    Watch out for missing data. You can decide whether to omit subjects with missing data or replace the missing data; just explain your choice and its consequences in the report.

    +

    The report should be written like a summary document for your PI. You don’t need to show your code in the rendered report. Include summary tables and/or plots with subject demographics from both the full data set and the subset you’ll analyse. Visualise the relationship of interest. Conduct a GLM analysis to answer the research question above. Finally, do a power analysis to determine the sample size you would need to have 80% power to detect a 0.5-point difference between the levels of version with an alpha criterion of 0.01.

    +
    +
    +
    +

    +Instructions function

    +

    As before, wrap this code in a function.

    +
    +make_instructions <- function(template, 
+                              factors = c("language", "condition", "version")) {
+  two_factors <- sample(factors, 2)
+  iv <- two_factors[[1]]
+  subset <- two_factors[[2]]
+  level <- subjects[[subset]] %>% unique() %>% sample(1)
+  topic <- sample(c("stress", "extroversion", "disgust"), 1)
+  alpha <- sample(c(.005, .01, .05), 1)
+  power <- sample(c(80, 85, 90), 1)
+
+  readLines(template) %>%
+    paste(collapse = "\n") %>%
+    glue::glue()
+}
    +
    +
    +
    +

    +Save files

    +

    Now you need to create a directory for the student’s files and save the subject table, question table, and instructions. I’m saving the files in a directory that contains the student ID so I can upload them to Moodle easily.

    +
    +dir <- paste0("exam_", student_id)
+dir.create(dir, showWarnings = FALSE)
+readr::write_csv(subjects, file.path(dir, "subjects.csv"))
+readr::write_csv(questions, file.path(dir, "questions.csv"))
+write(instructions, file.path(dir, "instructions.txt"))
    +
    +
    +

    +Simulation function

    +

    Once you’ve debugged your code above, put each step inside a function with the argument student_id.

    +
    +random_project <- function(student_id) {
+  # set seed using student ID
+  seed_from_string(student_id)
+  
+  # simulate subjects
+  subjects <- make_subjects()
+  
+  # simulate questionnaire
+  questions <- make_quest(subjects)
+  
+  # customise instructions
+  instructions <- make_instructions("template_instructions.txt")
+  
+  # write files to a new directory
+  dir <- paste0("exam_", student_id)
+  dir.create(dir, showWarnings = FALSE)
+  readr::write_csv(subjects, file.path(dir, "subjects.csv"))
+  readr::write_csv(questions, file.path(dir, "questions.csv"))
+  write(instructions, file.path(dir, "instructions.txt"))
+  
+  return(dir)
+}
    +

    Now you can map over a list of student IDs to create personalised data and research questions for each student. Here is the pattern if you have student IDs in a table in a column called Student ID.

    +
    +# load student IDs
+student_list <- tibble(
+  `Student ID` = 1:5
+)
+
+# iterate over the IDs to create a random project
+purrr::map_chr(student_list$`Student ID`, random_project)
    +
    +
    + + + +
    + + + +
    +

    Developed by Lisa DeBruine.

    +
    + +
    +

    Site built with pkgdown 1.6.1.

    +
    + +
    +
    + + + + + + diff --git a/docs/articles/random_reports_files/figure-html/unnamed-chunk-19-1.png b/docs/articles/random_reports_files/figure-html/unnamed-chunk-19-1.png new file mode 100644 index 00000000..e986bf38 Binary files /dev/null and b/docs/articles/random_reports_files/figure-html/unnamed-chunk-19-1.png differ diff --git a/docs/articles/random_reports_files/figure-html/unnamed-chunk-21-1.png b/docs/articles/random_reports_files/figure-html/unnamed-chunk-21-1.png new file mode 100644 index 00000000..abf63ff7 Binary files /dev/null and b/docs/articles/random_reports_files/figure-html/unnamed-chunk-21-1.png differ diff --git a/docs/articles/random_reports_files/figure-html/unnamed-chunk-8-1.png b/docs/articles/random_reports_files/figure-html/unnamed-chunk-8-1.png new file mode 100644 index 00000000..82b19d79 Binary files /dev/null and b/docs/articles/random_reports_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/docs/articles/random_reports_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/random_reports_files/header-attrs-2.10.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/random_reports_files/header-attrs-2.10.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/rnorm_multi.html b/docs/articles/rnorm_multi.html index 9bb9c824..6088a3ee 100644 --- a/docs/articles/rnorm_multi.html +++ b/docs/articles/rnorm_multi.html @@ -17,9 +17,9 @@ - + - + @@ -42,7 +42,7 @@ faux - 1.0.0 + 1.1.0 @@ -82,12 +82,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -111,13 +117,13 @@ -
    +

    Simulate Correlated Variables

    Lisa DeBruine

    -

    2021-03-27

    +

    2021-09-13

    Source: vignettes/rnorm_multi.Rmd @@ -137,8 +143,7 @@

    Quick example

    For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25.

    -
-dat <- rnorm_multi(n = 100, 
+dat <- rnorm_multi(n = 100, 
                   mu = c(0, 20, 20),
                   sd = c(1, 5, 5),
                   r = c(0.5, 0.5, 0.25), 
diff --git a/docs/articles/rnorm_multi_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/rnorm_multi_files/header-attrs-2.10.6/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/articles/rnorm_multi_files/header-attrs-2.10.6/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
diff --git a/docs/articles/sim_design.html b/docs/articles/sim_design.html
index 5e8251e5..31bcf32c 100644
--- a/docs/articles/sim_design.html
+++ b/docs/articles/sim_design.html
@@ -17,9 +17,9 @@
 
 
 
-
+
 
-
+
 
 
 
@@ -42,7 +42,7 @@
       
       
         faux
-        1.0.0
+        1.1.0
    @@ -82,12 +82,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -111,13 +117,13 @@ -
    +

    Simulate by Design

    Lisa DeBruine

    -

    2021-03-27

    +

    2021-09-13

    Source: vignettes/sim_design.Rmd @@ -143,9 +149,13 @@

    dog = c(10, 15, 20, 25), row.names = within$time ) +# add factor labels for plotting +vardesc <- c(pet = "Type of Pet", + time = "Time of Day") + df <- sim_design(within, between, n = 100, mu = mu, sd = 5, r = .5, - empirical = TRUE, plot = TRUE)

    + empirical = TRUE, vardesc = vardesc, plot = TRUE)

    @@ -220,73 +230,28 @@

    time = c(day = "Tested during the day", night = "Tested at night") ) df <- sim_design(within, between, mu = 1:4)

    -

    -
    - -
    -

    -check_design

    -

    The check_design() function converts this to the fully expanded version; it checks your design specification when you run sim_design() or you can run it on its own to create a validated design list. You can view this in JSON format using the function json_design().

    +

    #### Factor labels

    +

    Add factor labels with the vardesc argument. You can also customise the ID and DV column names (they default to c(id = "id") and c(y = "value")).

    -design <- check_design(within, between, n = 10, 
-                       mu = 1:4, sd = 1:4, r = 0.5, plot = FALSE)
-
-json_design(design, pretty = TRUE)
-#> {
-#>   "within": {
-#>     "time": {
-#>       "day": "Tested during the day",
-#>       "night": "Tested at night"
-#>     }
-#>   },
-#>   "between": {
-#>     "pet": {
-#>       "cat": "Is a cat person",
-#>       "dog": "Is a dog person"
-#>     }
-#>   },
-#>   "dv": {
-#>     "y": "value"
-#>   },
-#>   "id": {
-#>     "id": "id"
-#>   },
-#>   "n": {
-#>     "cat": 10,
-#>     "dog": 10
-#>   },
-#>   "mu": {
-#>     "cat": {
-#>       "day": 1,
-#>       "night": 2
-#>     },
-#>     "dog": {
-#>       "day": 3,
-#>       "night": 4
-#>     }
-#>   },
-#>   "sd": {
-#>     "cat": {
-#>       "day": 1,
-#>       "night": 2
-#>     },
-#>     "dog": {
-#>       "day": 3,
-#>       "night": 4
-#>     }
-#>   },
-#>   "r": {
-#>     "cat": [
-#>       [1, 0.5],
-#>       [0.5, 1]
-#>     ],
-#>     "dog": [
-#>       [1, 0.5],
-#>       [0.5, 1]
-#>     ]
-#>   },
-#>   "sep": "_"
-#> }
    +vardesc <- c(pet = "Type of Pet", + time = "Time of Day") + +df <- sim_design(within, between, mu = 1:4, + id = c(pet_id = "Pet ID"), + dv = c(score = "Score on the Test"), + vardesc = vardesc)
    +

    +

    If you have any within-subjects factors and your table is in wide format, you will only see the DV name in plots. But it will be used if you convert the table from wide to long.

    +
    +wide2long(df) %>% head()
+#>   pet_id pet time      score
+#> 1   S001 cat  day  2.1304574
+#> 2   S002 cat  day  2.1080727
+#> 3   S003 cat  day  0.6007963
+#> 4   S004 cat  day -0.6186662
+#> 5   S005 cat  day  0.1293905
+#> 6   S006 cat  day  1.1489877
    +

    @@ -296,75 +261,75 @@

    Single number

    You usually want to specify n as a single number. This is N per cell, not total sample size.

    -
    +
     n <- 20 # n per cell, not total
 design <- check_design(2, c(2,2), n = n, plot = FALSE)
 str(design$n)
 #> List of 4
-#>  $ B1_C1: num 20
-#>  $ B1_C2: num 20
-#>  $ B2_C1: num 20
-#>  $ B2_C2: num 20
    
+#>  $ B1a_B2a: num 20
+#>  $ B1a_B2b: num 20
+#>  $ B1b_B2a: num 20
+#>  $ B1b_B2b: num 20

    Named list

    You can also specify n as a named list of Ns per between-subject cell.

    -
    +
     n <- list(
-  B1_C1 = 10, 
-  B1_C2 = 20, 
-  B2_C1 = 30, 
-  B2_C2 = 40
+  B1a_B2a = 10, 
+  B1a_B2b = 20, 
+  B1b_B2a = 30, 
+  B1b_B2b = 40
 )
 design <- check_design(2, c(2,2), n = n, plot = FALSE)
 str(design$n)
 #> List of 4
-#>  $ B1_C1: num 10
-#>  $ B1_C2: num 20
-#>  $ B2_C1: num 30
-#>  $ B2_C2: num 40
    
+#>  $ B1a_B2a: num 10
+#>  $ B1a_B2b: num 20
+#>  $ B1b_B2a: num 30
+#>  $ B1b_B2b: num 40

    Dataframe

    Or as a data frame. You just need to get the row or column names right, but they don’t have to be in the right order.

    -
    +
     n <- data.frame(
-  B2_C2 = 40,
-  B1_C1 = 10, 
-  B1_C2 = 20, 
-  B2_C1 = 30
+  B1b_B2b = 40,
+  B1a_B2a = 10, 
+  B1a_B2b = 20, 
+  B1b_B2a = 30
 )
 design <- check_design(2, c(2,2), n = n, plot = FALSE)
 str(design$n)
 #> List of 4
-#>  $ B1_C1: num 10
-#>  $ B1_C2: num 20
-#>  $ B2_C1: num 30
-#>  $ B2_C2: num 40
    
+#>  $ B1a_B2a: num 10
+#>  $ B1a_B2b: num 20
+#>  $ B1b_B2a: num 30
+#>  $ B1b_B2b: num 40

    You can specify the cells as row names or column names and check_design() will fix them. Since n has to be the same for each within-subject factor, you can specify n as a single column with any name.

    -
    +
     n <- data.frame(n = c(10, 20, 30, 40),
-                row.names = c("B1_C1", "B1_C2", "B2_C1", "B2_C2"))
+                row.names = c("B1a_B2a", "B1a_B2b", "B1b_B2a", "B1b_B2b"))
 design <- check_design(2, c(2,2), n = n, plot = FALSE)
 str(design$n)
 #> List of 4
-#>  $ B1_C1: num 10
-#>  $ B1_C2: num 20
-#>  $ B2_C1: num 30
-#>  $ B2_C2: num 40
    
+#>  $ B1a_B2a: num 10
+#>  $ B1a_B2b: num 20
+#>  $ B1b_B2a: num 30
+#>  $ B1b_B2b: num 40

    Mu and SD

    -

    The specifications for mu and sd need both within-subect and between-subject cells. You can specify these as a single numbers, a vector, a named list of named vectors or a data frame.

    +

    The specifications for mu and sd need both within-subject and between-subject cells. You can specify these as a single numbers, a vector, a named list of named vectors or a data frame.

    Unnamed vector

    An unnamed vector is a quick way to specify means and SDs, but the order relative to your between- and within-subject cells can be confusing.

    -
    +
     between <- list(pet       = c("cat", "dog"), 
                 condition = c("A",   "B"))
 within  <- list(time      = c("day", "night"))
@@ -389,7 +354,7 @@ 
    
 
    
 Named list of named vectors
    
 
    A named list of named vectors prevents confusion due to order. The levels of the between-subject factors are the list names and the levels of the within-subject factors are the vector names, but their order doesn’t matter.
    
-
    +
     mu <- list(
   cat_B = c(night = 40, day = 30),
   cat_A = c(day = 10, night = 20),
@@ -412,7 +377,7 @@ 
    
 #>   ..$ day  : num 70
 #>   ..$ night: num 80
    
 
    Alternatively, you can specify them as data frames.
    
-
    +
     mu <- data.frame(
   cat_A = c(10, 20),
   cat_B = c(30, 40),
@@ -436,7 +401,7 @@ 
    
 #>   ..$ day  : num 70
 #>   ..$ night: num 80
    
 
    If you transpose the dataframe, this works out fine unless your within- and between-subject cells have identical names.
    
-
    +
     mu <- data.frame(
   day = c(10, 30, 50, 70),
   night = c(20, 40, 60, 80),
@@ -463,7 +428,7 @@ 
    
 
    
 Correlations
    
 
    If you have any within-subject factors, you need to set the correlation for each between-cell. Here, we only have two levels of one within-subject factor, so can only set one correlation per between-cell.
    
-
    +
     r <- list(
   cat_A = .5,
   cat_B = .5,
@@ -495,36 +460,36 @@ 
    
 
    
 Upper right triangle
    
 
    If you have more than 2 within-subject cells, you can specify each specific correlation in the upper right triangle of the correlation matrix as a vector.
    
-
    +
     r <- list(
-  B1 = c(.10, .20, .30, 
+  B1a = c(.10, .20, .30, 
               .40, .50, 
                    .60),
-  B2 = c(.15, .25, .35, 
+  B1b = c(.15, .25, .35, 
               .45, .55, 
                    .65)
 )
 design <- check_design(4, 2, r = r, plot = FALSE)
 design$r
-#> $B1
-#>     A1  A2  A3  A4
-#> A1 1.0 0.1 0.2 0.3
-#> A2 0.1 1.0 0.4 0.5
-#> A3 0.2 0.4 1.0 0.6
-#> A4 0.3 0.5 0.6 1.0
+#> $B1a
+#>     W1a W1b W1c W1d
+#> W1a 1.0 0.1 0.2 0.3
+#> W1b 0.1 1.0 0.4 0.5
+#> W1c 0.2 0.4 1.0 0.6
+#> W1d 0.3 0.5 0.6 1.0
 #> 
-#> $B2
-#>      A1   A2   A3   A4
-#> A1 1.00 0.15 0.25 0.35
-#> A2 0.15 1.00 0.45 0.55
-#> A3 0.25 0.45 1.00 0.65
-#> A4 0.35 0.55 0.65 1.00
    
+#> $B1b
+#>      W1a  W1b  W1c  W1d
+#> W1a 1.00 0.15 0.25 0.35
+#> W1b 0.15 1.00 0.45 0.55
+#> W1c 0.25 0.45 1.00 0.65
+#> W1d 0.35 0.55 0.65 1.00
    
 
    
 
    
 
    
 Correlation matrix
    
 
    You can also enter the correlation matrix from cor().
    
-
    +
     within <- list(cars = c("speed", "dist"))
 between <- list(half = c("first", "last"))
 r <- list(
@@ -549,7 +514,7 @@ 
    
 
    
 Empirical
    
 
    If you set empirical = TRUE, you will get the exact means, SDs and correlations you specified. If you set empirical = FALSE or omit that argument, your data will be sampled from a population with those parameters, but your dataset will not have exactly those values (just on average).
    
-
    +
     between <- list(pet  = c("cat", "dog"))
 within  <- list(time = c("day", "night"))
 mu <- list(
@@ -625,7 +590,7 @@ 
    
 
    Here is a 2w*3w*2b*2b example. When you have multiple within or between factors, you need to specify parameters by cell. Cell names are the level names, in the order they are listed in the within or between arguments, separated by underscores.
    
 
    Foe example, if you have one within-subject factor of condition with levels con and inc, and another within-subject factor of version with levels easy, med, and hard, your cell labels will be: con_easy, inc_easy, con_med, inc_med, con_hard, and inc_hard.
    
 
    If you have any characters in your level names except letters and numbers, they will be replaced by a full stop (e.g., my super-good level_name will become my.super.good.level.name).
    
-
    +
     within <- list(
   condition = c(con = "Mean of congruent trials", 
                 inc = "Mean of incongruent trials"),
@@ -648,7 +613,7 @@ 
    
   expert_night = 10:15
 )
    
 
    You can set the correlation for each between-cell to a single number.
    
-
    +
     r <- list(
   novice_day = 0.3,
   novice_night = 0.2,
@@ -656,7 +621,7 @@ 
    
   expert_night = 0.4
 )
    
 
    Or you can set the full correlation matrix with a vector or matrix. Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.
    
-
    +
     # upper right triangle correlation specification
 # inc and con have r = 0.5 within each difficultly level, 0.2 otherwise
 #          ce,  ie,  cm,  im,  ch,  ih
@@ -674,7 +639,7 @@ 
    
   expert_night = triangle
 )
    
 
    You can set long = TRUE to return the data frame in long format, which is usually easier for plotting.
    
-
    +
     df <- sim_design(within, between, n = 100, 
                  mu = mu, sd = 2, r = r, 
                  dv = c(rt = "Reaction Time"), 
@@ -682,19 +647,23 @@ 
    
 
 head(df)
 #>     id experience time condition version        rt
-#> 1 S001     novice  day       con    easy 11.009948
-#> 2 S002     novice  day       con    easy 10.514269
-#> 3 S003     novice  day       con    easy  8.175229
-#> 4 S004     novice  day       con    easy  6.696232
-#> 5 S005     novice  day       con    easy 10.703293
-#> 6 S006     novice  day       con    easy  8.694598
    
+#> 1 S001     novice  day       con    easy  9.612842
+#> 2 S002     novice  day       con    easy  9.339860
+#> 3 S003     novice  day       con    easy  8.146942
+#> 4 S004     novice  day       con    easy 11.587107
+#> 5 S005     novice  day       con    easy  7.536527
+#> 6 S006     novice  day       con    easy 11.726081
    
 
    
 
    
 
    
 Multiple datasets
    
-
    You can simulate multiple datasets by setting the rep argument of sim_design() to a number greater than 1. This will return a nested data frame with one column called rep and another column called data, that contains each individual data frame. This method is ** much** faster than repeatedly calling sim_design(), which will check the syntax of your design each time.
    
+
    You might want to make multiple datasets with the same design for simulations. It can be inefficient to just put sim_design() in an apply function or a for loop (especially if you leave plot = TRUE). The patterns below make this faster.
    
+
    
+
    
+rep argument
    
+
    You can simulate multiple datasets by setting the rep argument of sim_design() to a number greater than 1. This will return a nested data frame with one column called rep and another column called data, that contains each individual data frame. This method is faster than repeatedly calling sim_design(), which will check the syntax of your design each time, and returns a nested dataset that makes it easy to apply the same analysis to each replicate.
    
 
    The code below creates 5 data frames with a 2W*2B design.
    
-
    +
     df <- sim_design(within = 2, between = 2, 
                  n = 50, mu = c(1, 1, 1, 1.5), 
                  sd = 1, r = 0.5, plot = FALSE, 
@@ -707,17 +676,17 @@ 
    
 #> 3   3
 #> 4   4
 #> 5   5
-#>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          data
-#> 1                  S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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+#> 4                                  S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, 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1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.390996737022794, 0.514419279322078, 0.465219941640955, 3.11701542636532, 0.544508779491317, 1.26640342605438, 0.709468434016184, 0.484091606530019, 0.340539327010012, 2.12816657988586, 0.526209660745157, 4.11955918681923, 0.586072991553786, 0.445353388305301, 1.2873722573024, 2.42438100691483, 1.46376969611168, 0.244762961808145, 0.992138814536005, 1.60642717441871, 0.538123401841899, 0.0520964369671474, 2.3940425280377, -0.10812510122673, 2.69834325728841, 2.47191522038167, 1.70335072085337, 2.12887526887032, 0.109412156214895, 2.39913712451626, 0.422345527805536, 0.30639868683018, 1.62093820625479, 1.38401069001983, 1.38952311927667, 1.73279772123413, 1.48328138825603, 3.26329836805232, 3.04693384236406, 0.824473231486417, 0.885141996232264, 1.6606545062688, 2.59548844818765, 1.75364937350649, 1.38058410232143, 1.39653311665508, 1.07671098458765, -0.240137130147369, 2.33861232245748, 0.524135561249644, 0.821194691692955, 1.00171076830003, -0.788958644559531, 1.72205053035894, 1.95019581302341, -1.75211040818413, 1.67495167058991, 0.530876972948112, -0.773195598832142, 0.670528408487344, 0.530897768872627, 2.93232396847259, 1.18433869678288, 0.206317111442945, 0.627277699948352, -0.125484460442242, 2.13128060856378, 2.40178193039027, 0.14264046642039, 0.688007299088954, 1.50221960058724, 3.05611986163872, 0.507409391923066, 0.50014671867989, 0.329414866172453, 0.375249435746574, 1.99288730939544, 2.22757555228064, 1.25644770007495, -1.19489410365346, 1.9729220247366, 1.59073470778152, 0.112214778600658, 2.56296082138495, 2.40843012347995, 1.82161350871862, 2.49908459645729, -0.0660931867045258, 0.794847035690629, -0.0279214365944875, 1.49282759605946, 2.51887477996389, 0.303306762375942, 3.08205144041933, 0.965129427962902, 2.20452112879489, 0.675904589942106, 1.01629357199723, 0.0757747164006191, 2.87673218842204, 1.80706953821955, 0.112349601830626, 0.889576594319076, 3.23924763347067, 1.41234686276884, 2.91733964106671, -0.540123510184444, -0.0722300541322409, 0.242944347089705, 1.62368508335076, 1.71282694532638, 2.52503981722833, -0.469895996129148, -0.3399012383782, 0.821157755788413, 1.21211609539506, -0.656061212239198, 0.260009604272351, 0.585975452392078, 2.34563582174388, 0.241786060637767, -0.955628614675378, 0.286133672767774, 0.128647112456209, 2.66439296745249, 2.21504152597742, 1.85192628707628, 0.954779196420469, 0.851312800839909, 1.73615771942524, -0.259875702852075, 0.79563883733003, 1.96980049479768, 2.14284616963924, 0.740454361686355, 1.39041287644043, 2.21889790881344, 1.98804601079687, 1.62167543173107, 1.76704974044433, 0.68066488343528, 0.597515070229046, 2.26728221582283, 1.34308906106656, 1.52427451863171, 2.34570853078019, 1.16521984857001, 0.598396492444016, 2.6518136907934, 0.753180837150408, 1.98132637774831, 1.06056822187545, 0.496894518169738, 0.723168221627451, 2.3862145904786, 0.46598433377952, 2.61939620009976, 2.08246382528877, 1.12884724884493, 1.43479191708313, 0.56514850543195, 1.500884252108, 3.4389201262876, 2.22532226264786, -0.566105049094095, -0.400617203104187, 1.19544869077937, 2.58837268506061, 1.61630764483899, 2.80649984607692, 2.63043262439607, 1.05777389527377, 2.69908701788188, 1.32700894914053, 1.72148809914738, 0.703069282348873, 2.65255437281893, 2.67604032597864, 2.37044918275909, 0.332395475810734, 2.95554256560293, 2.61711303339079, 1.03952339983093, 2.48095970276917, 1.03064469012436, 3.26327110809061, 1.32943769209757, 0.24527908086878, 2.22153278482233, 0.169005762196623, -0.336406939332894, 3.08851131198831, 0.759081882205124, 2.24000933805869, 1.84716178368636, 2.29595154065351, 2.53493545171893, 2.11185580974179, 1.44669225916909, 1.99719258745106
+#> 5 S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, S001, S002, S003, S004, S005, S006, S007, S008, S009, S010, S011, S012, S013, S014, S015, S016, S017, S018, S019, S020, S021, S022, S023, S024, S025, S026, S027, S028, S029, S030, S031, S032, S033, S034, S035, S036, S037, S038, S039, S040, S041, S042, S043, S044, S045, S046, S047, S048, S049, S050, S051, S052, S053, S054, S055, S056, S057, S058, S059, S060, S061, S062, S063, S064, S065, S066, S067, S068, S069, S070, S071, S072, S073, S074, S075, S076, S077, S078, S079, S080, S081, S082, S083, S084, S085, S086, S087, S088, S089, S090, S091, S092, S093, S094, S095, S096, S097, S098, S099, S100, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0.486725659372221, 1.64357556082774, 0.263194494557383, 1.0296548031938, 0.811726875438496, 0.5976157581421, 1.90602786838901, 1.28057058318111, 1.56624339125893, 0.998155969775003, 0.00170289563711612, 0.509547462735587, -0.00706356580588374, -0.797435868547153, 1.13284344760021, 1.03035461094415, 1.10535438489287, -0.197824348260161, 1.75269621759939, 1.72800465262085, 1.76707520038246, 0.851881340610244, 1.54363125075404, 1.85215533890089, 0.594390665563255, 2.35970532918645, 0.947998324970383, 0.81567240073653, 0.332835046674711, 0.620404809444938, 1.78989750836414, 2.02759270156495, 2.54545630808485, -0.828374732911002, -0.801094510975028, 1.08107571453343, -1.21899807157319, 0.37450302180007, 1.18473667818687, -0.166122980632638, 0.159384167235982, -0.922610427995531, -0.832063998534347, 0.387313458504679, 0.670837122851231, 3.39890689453518, 0.685587046958218, 0.137996866146473, 2.75580651598626, 0.381296723364541, -0.571471926350737, 1.56372990933579, 0.811422647695064, 1.74820580179273, 1.06426592792395, 1.45976091883044, 0.0632732127200647, -0.656127063017977, 1.63700995639338, 2.74432467786927, 0.0881414374420042, 0.861670915448589, -1.48247489988499, 1.43163376124232, 2.14987611850804, 0.226800595386425, 0.901575222264706, 0.859722443799302, 1.00484933846561, 0.413386452147952, 1.50345968290061, -0.0209971611468218, 0.945624780088165, 1.39596168009528, 2.11555398525582, 0.969177721547276, 0.33167730810068, 0.848772768889905, 1.53101851479371, -1.04671261247307, 0.522985902823807, -0.222720595890356, 0.885180815996636, 2.93302537522913, 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2.71285713441115, -0.0316282020028198, 0.410455710338117, 0.173880619460838, 0.714667321650425, 1.03675268218041, -0.231031745450341, 3.14028996613381, -0.175721448321136, 0.930082019065776, 2.64735159540688, 1.18845643623468, -0.630265793191784, 0.935619809552062, 0.460192276556114, 2.8485302708645, 1.87192007126775, 1.59236896995704, 1.364872838976, -0.022774803626495, 1.71767714223523, 1.80575660305377, 2.54316072533052, 1.84593828861636, -1.66331966871552, 1.96958863560675, 2.52384276217459, 1.00190361237165, 1.65407460075167, 1.78667552036777, 1.33320784401231, 0.605333408261912, 3.45962136980245, 0.244187071485811, 2.10622652276742, 2.40770063778364, 2.2704631768497, 0.849843375257935, 0.774647774582052, 1.81455437244012, 1.89079024112148, 0.318784320738019, 1.74159036091936, 0.840666949567401, 1.81279632464054, 3.77251434407046, -0.346018184183341, 1.70069180204209, 0.834905078486252, 0.255960460856275, 1.31640195037946, 1.25880932188798, 1.14145511912438, 1.45723490194377, -0.0818208655607746, 1.13210993968314, 1.76896305566814, 3.48572911798513, 1.41233612186743, 2.82461287595284, 0.766380639932241, 2.04150889429403
    
 
    You can access an individual data frame using df$data[[1]] or run the same function on each data frame using a pattern like below:
    
-
    +
     # using tidyverse functions
 analyse <- function(data) {
-  stats::aov(y ~ B * A + Error(id/A), data = data) %>% 
+  stats::aov(y ~ B1 * W1 + Error(id/W1), data = data) %>% 
     broom::tidy()
 }
 
@@ -726,19 +695,94 @@ 
    
   dplyr::select(-data) %>%
   tidyr::unnest(analysis)
 #> # A tibble: 25 x 8
-#>      rep stratum term         df    sumsq meansq statistic   p.value
-#>    <int> <chr>   <chr>     <dbl>    <dbl>  <dbl>     <dbl>     <dbl>
-#>  1     1 id      B             1   0.0778 0.0778    0.0552  0.815   
-#>  2     1 id      Residuals    98 138.     1.41     NA      NA       
-#>  3     1 id:A    A             1   1.25   1.25      2.46    0.120   
-#>  4     1 id:A    B:A           1   6.70   6.70     13.2     0.000444
-#>  5     1 id:A    Residuals    98  49.7    0.507    NA      NA       
-#>  6     2 id      B             1   0.145  0.145     0.119   0.731   
-#>  7     2 id      Residuals    98 120.     1.22     NA      NA       
-#>  8     2 id:A    A             1   6.22   6.22     12.6     0.000606
-#>  9     2 id:A    B:A           1   4.60   4.60      9.29    0.00297 
-#> 10     2 id:A    Residuals    98  48.5    0.495    NA      NA       
+#>      rep stratum term         df   sumsq meansq statistic  p.value
+#>    <int> <chr>   <chr>     <dbl>   <dbl>  <dbl>     <dbl>    <dbl>
+#>  1     1 id      B1            1  11.9   11.9       8.52   0.00436
+#>  2     1 id      Residuals    98 137.     1.40     NA     NA      
+#>  3     1 id:W1   W1            1   0.550  0.550     1.01   0.316  
+#>  4     1 id:W1   B1:W1         1   0.464  0.464     0.855  0.357  
+#>  5     1 id:W1   Residuals    98  53.2    0.542    NA     NA      
+#>  6     2 id      B1            1  12.4   12.4       8.03   0.00558
+#>  7     2 id      Residuals    98 151.     1.54     NA     NA      
+#>  8     2 id:W1   W1            1   1.39   1.39      3.18   0.0776 
+#>  9     2 id:W1   B1:W1         1   0.266  0.266     0.611  0.436  
+#> 10     2 id:W1   Residuals    98  42.8    0.436    NA     NA      
 #> # … with 15 more rows
    
+
    
+
    
+
    
+Unnested reps
    
+
    You can also get an unnested data frame by setting nested = FALSE. This is a bit faster to return, but less useful for the pattern above.
    
+
    +df <- sim_design(within = 2, between = 2, 
+                 n = 2, mu = c(1, 1, 1, 1.5), 
+                 sd = 1, r = 0.5, plot = FALSE, 
+                 long = TRUE, rep = 2, nested = FALSE)
+
+df
+#>    rep id  B1  W1          y
+#> 1    1 S1 B1a W1a -0.6145438
+#> 2    1 S2 B1a W1a  1.5815508
+#> 3    1 S3 B1b W1a -0.9296229
+#> 4    1 S4 B1b W1a  0.9573383
+#> 5    1 S1 B1a W1b -0.2703416
+#> 6    1 S2 B1a W1b  1.4611508
+#> 7    1 S3 B1b W1b  2.0628431
+#> 8    1 S4 B1b W1b  2.5993118
+#> 9    2 S1 B1a W1a  0.7399179
+#> 10   2 S2 B1a W1a  1.5328323
+#> 11   2 S3 B1b W1a  0.2531167
+#> 12   2 S4 B1b W1a  2.1176671
+#> 13   2 S1 B1a W1b -0.3890839
+#> 14   2 S2 B1a W1b  1.6844656
+#> 15   2 S3 B1b W1b  1.9668646
+#> 16   2 S4 B1b W1b  2.5087846
    
+
    
+
    
+
    
+Simulate by design
    
+
    The check_design() function converts any abbreviated design specification to the fully expanded version; it checks your design specification when you run sim_design() or you can run it on its own to create a validated design list.
    
+
    +between <- list(pet = c("cat", "dog"))
+within <- list(time = c("day", "night"))
+vardesc <- c(pet = "Type of Pet",
+             time = "Time of Day")
+design <- check_design(within, between, n = 10, 
+                       mu = 1:4, sd = 1:4, r = 0.5, 
+                       vardesc = vardesc, plot = FALSE)
+
+design
    
+
      
+
    • [DV] y: value
      
+
      • 
+
    • [ID] id: id
      
+
      • 
+
    • Within-subject variables:
+
        
+
      • time: Time of Day:
+
          
+
        • day: day
          • 
+
        • night: night
          • 
+
        
+
        • 
+
      
+
      • 
+
    • Between-subject variables:
+
        
+
      • pet: Type of Pet:
+
          
+
        • cat: cat
          • 
+
        • dog: dog
          • 
+
        
+
        • 
+
      
+
      • 
+
    • Parameters: |pet |time | day| night| n| mu| sd| |:—|:—–|—:|—–:|–:|–:|–:| |cat |day | 1.0| 0.5| 10| 1| 1| |cat |night | 0.5| 1.0| 10| 2| 2| |dog |day | 1.0| 0.5| 10| 3| 3| |dog |night | 0.5| 1.0| 10| 4| 4|
      • 
+
    
+
    You can then use the design object to create simulated datasets. If you set the design for sim_design() with the design argument instead of specifying parameters, it will skip the time-consuming checks, which can speed up simulations by 30% or more.
    
+
    +data <- sim_design(design = design)
    
+
    
 
    
   
    
 
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diff --git a/docs/articles/sim_design_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/sim_design_files/figure-html/unnamed-chunk-5-1.png
new file mode 100644
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diff --git a/docs/articles/sim_design_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/sim_design_files/header-attrs-2.10.6/header-attrs.js
new file mode 100644
index 00000000..dd57d92e
--- /dev/null
+++ b/docs/articles/sim_design_files/header-attrs-2.10.6/header-attrs.js
@@ -0,0 +1,12 @@
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
+// be compatible with the behavior of Pandoc < 2.8).
+document.addEventListener('DOMContentLoaded', function(e) {
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
+  var i, h, a;
+  for (i = 0; i < hs.length; i++) {
+    h = hs[i];
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
+    a = h.attributes;
+    while (a.length > 0) h.removeAttribute(a[0].name);
+  }
+});
diff --git a/docs/articles/sim_df.html b/docs/articles/sim_df.html
index fbc99871..fa302763 100644
--- a/docs/articles/sim_df.html
+++ b/docs/articles/sim_df.html
@@ -17,9 +17,9 @@
 
 
 
-
+
 
-
+
 
 
 
@@ -42,7 +42,7 @@
       
       
         faux
-        1.0.0
+        1.1.0
       
     
    
 
@@ -82,12 +82,18 @@
     
  • 
       Mixed Design Simulation
     
    • 
+    
  • 
+      Randomised Reports
+    
    • 
     
  • 
       Distribution Conversions
     
    • 
     
  • 
       Codebook Demo
     
    • 
+    
  • 
+      Contrasts
+    
    •
  • @@ -111,13 +117,13 @@ -
    +

    Simulate from Existing Data

    Lisa DeBruine

    -

    2021-03-27

    +

    2021-09-13

    Source: vignettes/sim_df.Rmd @@ -209,65 +215,65 @@

    Missing data

    -

    As of faux 0.0.1.8, if you want to simulate missing data, set missing = TRUE and sim_df will simulate missing data with the same joint probabilities as your data. In the dataset below, in condition B1 30% of A1 values are missing and 60% of A2 values are missing. This is correlated so that there is a 100% chance that A2 is missing if A1 is. There is no missing data for condition B2.

    +

    As of faux 0.0.1.8, if you want to simulate missing data, set missing = TRUE and sim_df will simulate missing data with the same joint probabilities as your data. In the dataset below, in condition B1a, 30% of W1a values are missing and 60% of W1b values are missing. This is correlated so that there is a 100% chance that W1b is missing if W1a is. There is no missing data for condition B1b.

     data <- sim_design(2, 2, n = 10, plot = FALSE)
-data$A1[1:3] <- NA
-data$A2[1:6] <- NA
+data$W1a[1:3] <- NA
+data$W1b[1:6] <- NA
 data
-#>     id  B          A1          A2
-#> 1  S01 B1          NA          NA
-#> 2  S02 B1          NA          NA
-#> 3  S03 B1          NA          NA
-#> 4  S04 B1 -0.87577954          NA
-#> 5  S05 B1  0.27931928          NA
-#> 6  S06 B1  0.46277729          NA
-#> 7  S07 B1 -0.11678369 -0.66795544
-#> 8  S08 B1  1.34454748  2.30398889
-#> 9  S09 B1 -1.26771349  0.55738252
-#> 10 S10 B1 -0.71258675  0.09183975
-#> 11 S11 B2 -0.39607245  0.85023624
-#> 12 S12 B2 -1.15357665 -0.18013489
-#> 13 S13 B2 -0.21533313  1.08873598
-#> 14 S14 B2 -0.42370435  0.94812875
-#> 15 S15 B2 -0.05717782  0.63667947
-#> 16 S16 B2 -0.12733224 -1.47331528
-#> 17 S17 B2  0.21208884  0.69005635
-#> 18 S18 B2 -0.20401676 -1.01059002
-#> 19 S19 B2 -1.14885697 -0.50127757
-#> 20 S20 B2  0.87586766  1.47606428
    +#> id B1 W1a W1b +#> 1 S01 B1a NA NA +#> 2 S02 B1a NA NA +#> 3 S03 B1a NA NA +#> 4 S04 B1a -0.87577954 NA +#> 5 S05 B1a 0.27931928 NA +#> 6 S06 B1a 0.46277729 NA +#> 7 S07 B1a -0.11678369 -0.66795544 +#> 8 S08 B1a 1.34454748 2.30398889 +#> 9 S09 B1a -1.26771349 0.55738252 +#> 10 S10 B1a -0.71258675 0.09183975 +#> 11 S11 B1b -0.39607245 0.85023624 +#> 12 S12 B1b -1.15357665 -0.18013489 +#> 13 S13 B1b -0.21533313 1.08873598 +#> 14 S14 B1b -0.42370435 0.94812875 +#> 15 S15 B1b -0.05717782 0.63667947 +#> 16 S16 B1b -0.12733224 -1.47331528 +#> 17 S17 B1b 0.21208884 0.69005635 +#> 18 S18 B1b -0.20401676 -1.01059002 +#> 19 S19 B1b -1.14885697 -0.50127757 +#> 20 S20 B1b 0.87586766 1.47606428

    The simulated data will have the same pattern of missingness (sampled from the joint distribution, so it won’t be exact).

    -simdat <- sim_df(data, between = "B", n = 1000, 
+simdat <- sim_df(data, between = "B1", n = 1000, 
                  missing = TRUE)
    - - - + + + - + - + - + - + diff --git a/docs/articles/sim_df_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/sim_df_files/header-attrs-2.10.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/sim_df_files/header-attrs-2.10.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/articles/sim_mixed.html b/docs/articles/sim_mixed.html index 8b5b76d7..c6c296f1 100644 --- a/docs/articles/sim_mixed.html +++ b/docs/articles/sim_mixed.html @@ -17,9 +17,9 @@ - + - + @@ -42,7 +42,7 @@ faux - 1.0.0 + 1.1.0 @@ -82,12 +82,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -111,7 +117,7 @@ -
    +

    Mixed Design Simulation

    @@ -127,138 +133,871 @@

    Mixed Design Simulation

    -

    The mixed design functions are extremely basic. I plan to work on more useful functions, but this paper provides a tutorial and examples for more complex designs.

    -
    +

    This paper provides a tutorial and examples for more complex designs.

    +

    -sim_mixed_cc

    -

    This function produces a data table for a basic cross-classified design with random intercepts for subjects and items.

    -

    For example, the following code produces the data for 100 subjects responding to 50 items where the response has an overall mean (grand_i) of 10. Subjects vary in their average response with an SD of 1, items vary in their average response with an SD of 2, and the residual error term has an SD of 3.

    +Building multilevel designs +

    You can build up a mixed effects model by adding random factors in a stepwise fashion using add_random(). The example below simulates 3 schools with 2, 3 and 4 classes nested in each school.

    -dat_cc <- sim_mixed_cc(
-  sub_n = 100,   # subject sample size
-  item_n = 50,  # item sample size
-  grand_i = 10,  # overall mean of the score
-  sub_sd = 1,   # SD of subject random intercepts
-  item_sd = 2,  # SD of item random intercepts
-  error_sd = 3  # SD of residual error
-)
    -

    You can then see how changing these numbers affects the random effects in an intercept-only mixed effects model.

    +data <- add_random(school = 3) %>% + add_random(class = c(2, 3, 4), .nested_in = "school")
    +
    • BA1A2B1W1aW1b n
    B1B1a NA NA 0.31
    B1B1a not NA NA 0.31
    B1B1a not NA not NA 0.38
    B2B1b not NA not NA 1.00
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    schoolclass
    s1c1
    s1c2
    s2c3
    s2c4
    s2c5
    s3c6
    s3c7
    s3c8
    s3c9
    +

    A cross-classified design with 2 subjects and 3 items.

    -lme4::lmer(y ~ 1 + (1 | sub_id) + (1 | item_id), data = dat_cc) %>%
-  broom.mixed::tidy() %>%
-  knitr::kable(digits = 3)
    +data <- add_random(subj = 2, item = 3)

    - - - - - - + + - - - - - - + + - - - - - - + + - - - - - - + + - - - - - - + + + + + + + + + +
    effectgrouptermestimatestd.errorstatisticsubjitem
    fixedNA(Intercept)10.1290.26138.836s1i1
    ran_parssub_idsd__(Intercept)0.940NANAs1i2
    ran_parsitem_idsd__(Intercept)1.693NANAs1i3
    ran_parsResidualsd__Observation3.063NANAs2i1
    s2i2
    s2i3
    -

    For example, changing grand_i to 0 changes the estimate for the fixed effect of the intercept (Intercept). Changing sub_sd to 1.5 and item_sd to 3 change the estimate for the SD of their corresponding random effects. Changing error_sd to 10 changes the estimate from the Residual SD.

    +
    +
    +

    +Adding fixed factors

    +

    Add within factors with add_within().

    -dat_cc <- sim_mixed_cc(100, 50, 0, 1.5, 3, 10)
-
-lme4::lmer(y ~ 1 + (1 | sub_id) + (1 | item_id), data = dat_cc) %>%
-  broom.mixed::tidy() %>%
-  knitr::kable(digits = 3)
    +data <- add_random(subj = 2) %>% + add_within("subj", time = c("pre", "post"), + condition = c("control", "test"))
    - - - - - - + + + - - - - - - + + + - - - - - - + + + - - - - - - + + + - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + +
    effectgrouptermestimatestd.errorstatisticsubjtimecondition
    fixedNA(Intercept)0.6330.4561.387s1precontrol
    ran_parssub_idsd__(Intercept)1.537NANAs1pretest
    ran_parsitem_idsd__(Intercept)2.870NANAs1postcontrol
    ran_parsResidualsd__Observation9.950NANAs1posttest
    s2precontrol
    s2pretest
    s2postcontrol
    s2posttest
    +

    Add between factors with add_between(). If you have more than one factor, they will be crossed. If you set .shuffle = TRUE, the factors will be added randomly (not in “order”), and separately added factors may end up confounded. This simulates true random allocation.

    +
    +data <- add_random(subj = 4, item = 2) %>%
+  add_between("subj", cond = c("control", "test"), gen = c("X", "Z")) %>%
+  add_between("item", version = c("A", "B")) %>%
+  add_between(c("subj", "item"), shuffled = 1:4, .shuffle = TRUE) %>%
+  add_between(c("subj", "item"), not_shuffled = 1:4, .shuffle = FALSE)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjitemcondgenversionshufflednot_shuffled
    s1i1controlXA21
    s1i2controlXB32
    s2i1controlZA23
    s2i2controlZB14
    s3i1testXA31
    s3i2testXB42
    s4i1testZA43
    s4i2testZB14
    +
    +

    +Unequal cells

    +

    If the levels of a factor don’t have equal probability, set the probability with .prob. If the sum of the values equals the number of groups, you will always get those exact numbers (e.g., always 8 control and 2 test; set shuffle = TRUE to get them in a random order). Otherwise, the values are sampled and the exact proportions will change for each simulation.

    +
    +data <- add_random(subj = 10) %>%
+  add_between("subj", cond = c("control", "test"), .prob = c(8, 2)) %>%
+  add_between("subj", age = c("young", "old"), .prob = c(.8, .2))
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjcondage
    s01controlold
    s02controlyoung
    s03controlold
    s04controlyoung
    s05controlyoung
    s06controlyoung
    s07controlyoung
    s08controlold
    s09testyoung
    s10testyoung
    +

    If you have more than one between-subject factor, setting them together allows you to set joint proportions for each cell.

    +
    +data <- add_random(subj = 10) %>%
+  add_between("subj", 
+              cond = c("control", "test"),
+              age = c("young", "old"), 
+              .prob = c(2, 3, 4, 1))
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjcondage
    s01controlyoung
    s02controlyoung
    s03controlold
    s04controlold
    s05controlold
    s06testyoung
    s07testyoung
    s08testyoung
    s09testyoung
    s10testold
    +
    +
    +

    +Counterbalanced designs

    +

    You can use the filter() function from dplyr to create counterbalanced designs. For example, if your items are grouped into A and B counterbalanced groups, add a between-subjects and a between-items factor with the same levels and filter only rows where the subject value matches the item value. In the example below, odd-numbered subjects only respond to odd-numbered items.

    +
    +data <- add_random(subj = 4, item = 4) %>%
+  add_between("subj", subj_cb = c("odd", "even")) %>%
+  add_between("item", item_cb = c("odd", "even")) %>%
+  dplyr::filter(subj_cb == item_cb)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjitemsubj_cbitem_cb
    s1i1oddodd
    s1i3oddodd
    s2i2eveneven
    s2i4eveneven
    s3i1oddodd
    s3i3oddodd
    s4i2eveneven
    s4i4eveneven
    +
    +
    +

    +Recoding

    +

    To set up data for analysis, you often need to recode categorical variables. Use the helper function add_contrast() for this. The code below creates anova-coded and treatment-coded versions of “cond” and the two variables needed to treatment-code the 3-level variable “type”. See the contrasts vignette for more details.

    +

    You can add_recode() for setting a manual contrast that isn’t available in add_contrast(), such as weighted contrasts.

    +
    +data <- add_random(subj = 2, item = 3) %>%
+  add_between("subj", cond = c("A", "B")) %>%
+  add_between("item", type = c("X", "Y", "Z")) %>%
+  # add contrasts
+  add_contrast("cond", "anova", add_cols = TRUE) %>%
+  add_contrast("cond", "treatment", add_cols = TRUE) %>%
+  # you can change the default column names
+  add_contrast("type", "treatment", add_cols = TRUE, colnames = c("Y", "Z")) %>%
+  add_recode("type", "type.w", X = -1.3, Y = 0, Z = 0.92)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjitemcondtypecond.B-Acond.B-A.trYZtype.w
    s1i1AX-0.5000-1.30
    s1i2AY-0.50100.00
    s1i3AZ-0.50010.92
    s2i1BX0.5100-1.30
    s2i2BY0.51100.00
    s2i3BZ0.51010.92
    -
    +
    +

    -sim_mixed_df

    -

    This function uses lme4::lmer() to get subject, item and error SDs from an existing dataset and simulates a new dataset with the specified number of subjects and items with distributions drawn from the example data.

    -

    This example uses the fr4 dataset from this package to simulate 100 new subjects viewing 50 new faces.

    -
    -
-new_dat <- sim_mixed_df(fr4, 
-                        sub_n = 100, 
-                        item_n = 50, 
-                        dv = "rating", 
-                        sub_id = "rater_id", 
-                        item_id = "face_id")
    +Adding random effects +

    To simulate multilevel data, you need to add random intercepts and slopes for each random factor (or combination of random factors). These are randomly sampled each time you simulate a new sample, so you can only characterise them by their standard deviation. You can use the function add_ranef() to add random effects with specified SDs. If you add more than one random effect for the same group (e.g., a random intercept and a random slope), you can specify their correlation with .cors (you can specify correlations in the same way as for rnorm_multi()).

    +

    The code below sets up a simple cross-classified design where 2 subjects are crossed with 2 items. It adds a between-subject, within-item factor of “version”. Then it adds random effects by subject, item, and their interaction, as well as an error term (sigma).

    +
    +data <- add_random(subj = 4, item = 2) %>%
+  add_between("subj", version = 1:2) %>%
+  # add by-subject random intercept
+  add_ranef("subj", u0s = 1.3) %>%
+  # add by-item random intercept and slope
+  add_ranef("item", u0i = 1.5, u1i = 1.5, .cors = 0.3) %>%
+  # add by-subject:item random intercept
+  add_ranef(c("subj", "item"), u0si = 1.7) %>%
+  # add error term (by observation)
+  add_ranef(sigma = 2.2)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjitemversionu0su0iu1iu0sisigma
    s1i111.175-2.5490.952-1.5311.503
    s1i211.175-0.811-1.595-0.2561.518
    s2i12-2.014-2.5490.952-1.4071.174
    s2i22-2.014-0.811-1.5953.376-0.409
    s3i111.329-2.5490.9520.0750.842
    s3i211.329-0.811-1.595-0.6870.828
    s4i120.195-2.5490.952-0.8042.538
    s4i220.195-0.811-1.595-0.7053.465
    +
    +
    +

    +Simulating data

    +

    Now you can define your data-generating parameters and put everything together to simulate a dataset. In this example,subject are crossed with items, and there is a single treatment-coded between-subject, within-item fixed factor of condition with levels “control” and “test”. The intercept and effect of condition are both set to 0. The SDs of the random intercepts and slopes are all set to 1 (you will need pilot data to estimate realistic values for your design), the correlation between the random intercept and slope by items is set to 0. The SD of the error term is set to 2.

    +
    +# define parameters
+subj_n = 10  # number of subjects
+item_n = 10  # number of items
+b0 = 0       # intercept
+b1 = 0       # fixed effect of condition
+u0s_sd = 1   # random intercept SD for subjects
+u0i_sd = 1   # random intercept SD for items
+u1i_sd = 1   # random b1 slope SD for items
+r01i = 0     # correlation between random effects 0 and 1 for items
+sigma_sd = 2 # error SD
+  
+# set up data structure
+data <- add_random(subj = subj_n, item = item_n) %>%
+  # add and recode categorical variables
+  add_between("subj", cond = c("control", "test")) %>%
+  add_recode("cond", "cond.t", control = 0, test = 1) %>%
+  # add random effects 
+  add_ranef("subj", u0s = u0s_sd) %>%
+  add_ranef("item", u0i = u0i_sd, u1i = u1i_sd, .cors = r01i) %>%
+  add_ranef(sigma = sigma_sd) %>%
+  # calculate DV
+  mutate(dv = b0 + u0s + u0i + (b1 + u1i) * cond.t + sigma)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    subjitemcondcond.tu0su0iu1isigmadv
    s01i01control00.5892.1350.684-2.3510.373
    s01i02control00.589-0.214-0.252-0.2840.090
    s01i03control00.5890.947-0.1511.8023.337
    s01i04control00.5890.652-0.8750.7161.957
    s01i05control00.589-0.729-1.977-1.837-1.977
    s01i06control00.589-1.0500.238-2.644-3.105
    +
    +
    +

    +Analysing your multilevel data

    +

    You can analyse these data with lme4::lmer().

    +
    +m <- lmer(dv ~ cond.t + (1 | subj) + (1 + cond.t | item), data = data)
+
+summary(m)
+#> Linear mixed model fit by REML. t-tests use Satterthwaite's method [
+#> lmerModLmerTest]
+#> Formula: dv ~ cond.t + (1 | subj) + (1 + cond.t | item)
+#>    Data: data
+#> 
+#> REML criterion at convergence: 421
+#> 
+#> Scaled residuals: 
+#>      Min       1Q   Median       3Q      Max 
+#> -1.94554 -0.66137  0.03661  0.66655  2.39501 
+#> 
+#> Random effects:
+#>  Groups   Name        Variance Std.Dev. Corr 
+#>  subj     (Intercept) 0.3239   0.5691        
+#>  item     (Intercept) 0.7649   0.8746        
+#>           cond.t      1.1253   1.0608   -0.66
+#>  Residual             3.2817   1.8115        
+#> Number of obs: 100, groups:  subj, 10; item, 10
+#> 
+#> Fixed effects:
+#>             Estimate Std. Error       df t value Pr(>|t|)
+#> (Intercept) -0.05392    0.45486  9.66296  -0.119    0.908
+#> cond.t      -0.58770    0.61102  9.08258  -0.962    0.361
+#> 
+#> Correlation of Fixed Effects:
+#>        (Intr)
+#> cond.t -0.690
    +
    +
    +

    +Power simulation

    +

    Include this code in a function so you can easily change the Ns, fixed effects, and random effects to any values. Then run a mixed effects model on the data and return the model.

    +
    +sim <- function(subj_n = 10, item_n = 10,
+                b0 = 0, b1 = 0,         # fixed effects 
+                u0s_sd = 1, u0i_sd = 1, # random intercepts
+                u1i_sd = 1, r01i = 0,   # random slope and cor
+                sigma_sd = 2,           # error term
+                ... # helps the function work with pmap() below
+                ) {
+
+  # set up data structure
+  data <- add_random(subj = subj_n, item = item_n) %>%
+    # add and recode categorical variables
+    add_between("subj", cond = c("control", "test")) %>%
+    add_recode("cond", "cond.t", control = 0, test = 1) %>%
+    # add random effects 
+    add_ranef("subj", u0s = u0s_sd) %>%
+    add_ranef("item", u0i = u0i_sd, u1i = u1i_sd, .cors = r01i) %>%
+    add_ranef(sigma = sigma_sd) %>%
+    # calculate DV
+    mutate(dv = b0 + u0s + u0i + (b1 + u1i) * cond.t + sigma)
+
+  # run mixed effect model and return relevant values
+  m <- lmer(dv ~ cond.t + (1 | subj) + (1 + cond.t | item), data = data)
+
+  broom.mixed::tidy(m)
+}
    +

    Check the function. Here, we’re simulating a fixed effect of condition (b1) of 0.5 for 50 subjects and 40 items, with a correlation between the random intercept and slope for items of 0.2, and the default values for all other parameters that we set above.

    +
    +sim(subj_n = 50, item_n = 40, b1 = 0.5, r01i = 0.2)
+#> # A tibble: 7 x 8
+#>   effect   group   term               estimate std.error statistic    df p.value
+#>   <chr>    <chr>   <chr>                 <dbl>     <dbl>     <dbl> <dbl>   <dbl>
+#> 1 fixed    <NA>    (Intercept)         -0.0715     0.286    -0.250  71.4   0.803
+#> 2 fixed    <NA>    cond.t               0.499      0.395     1.27   68.1   0.210
+#> 3 ran_pars subj    sd__(Intercept)      1.19      NA        NA      NA    NA    
+#> 4 ran_pars item    sd__(Intercept)      0.913     NA        NA      NA    NA    
+#> 5 ran_pars item    cor__(Intercept).…   0.256     NA        NA      NA    NA    
+#> 6 ran_pars item    sd__cond.t           1.16      NA        NA      NA    NA    
+#> 7 ran_pars Residu… sd__Observation      2.08      NA        NA      NA    NA
    +

    Run the simulation repeatedly. I’m only running it 50 times per parameter combination here so my demo doesn’t take forever to run, but once you are done testing a range of parameters, you probably want to run the final simulation 100-1000 times. You might get a few warnings like “Model failed to converge” or “singular boundary”. You don’t need to worry too much if you only get a few of these. If most of your models have warnings, the simulation parameters are likely to be off.

    +
    +x <- crossing(
+  rep = 1:50, # number of replicates
+  subj_n = c(50, 100), # range of subject N
+  item_n = 25, # fixed item N
+  b1 = c(0.25, 0.5, 0.75), # range of effects
+  r01i = 0.2 # fixed correlation
+) %>%
+  mutate(analysis = pmap(., sim)) %>%
+  unnest(analysis)
    +

    Filter and/or group the resulting table and calculate the proportion of p.values above your alpha threshold to get power for the fixed effects.

    +
    +# calculate power for alpha = 0.05
+filter(x, effect == "fixed", term == "cond.t") %>%
+  group_by(b1, subj_n) %>% 
+  summarise(power = mean(p.value < .05), 
+            .groups = "drop") %>%
+  ggplot(aes(b1, subj_n, fill = power)) +
+  geom_tile() +
+  geom_text(aes(label = sprintf("%.2f", power)), color = "white", size = 10) +
+  scale_fill_viridis_c(limits = c(0, 1))
    +

    diff --git a/docs/articles/sim_mixed_files/figure-html/powerplot-1.png b/docs/articles/sim_mixed_files/figure-html/powerplot-1.png new file mode 100644 index 00000000..1003fb21 Binary files /dev/null and b/docs/articles/sim_mixed_files/figure-html/powerplot-1.png differ diff --git a/docs/articles/sim_mixed_files/header-attrs-2.10.6/header-attrs.js b/docs/articles/sim_mixed_files/header-attrs-2.10.6/header-attrs.js new file mode 100644 index 00000000..dd57d92e --- /dev/null +++ b/docs/articles/sim_mixed_files/header-attrs-2.10.6/header-attrs.js @@ -0,0 +1,12 @@ +// Pandoc 2.9 adds attributes on both header and div. We remove the former (to +// be compatible with the behavior of Pandoc < 2.8). +document.addEventListener('DOMContentLoaded', function(e) { + var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); + var i, h, a; + for (i = 0; i < hs.length; i++) { + h = hs[i]; + if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 + a = h.attributes; + while (a.length > 0) h.removeAttribute(a[0].name); + } +}); diff --git a/docs/authors.html b/docs/authors.html index 1f636fd2..9c99f40e 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -45,6 +45,8 @@ + + @@ -83,7 +85,7 @@ faux - 1.0.0 + 1.1.0
    @@ -123,12 +125,18 @@
  • Mixed Design Simulation
    • +
  • + Randomised Reports +
  • Distribution Conversions
  • Codebook Demo
    • +
  • + Contrasts +
  • @@ -159,14 +167,14 @@

    Citation

    Source: inst/CITATION -

    Lisa DeBruine, (2021). faux: Simulation for Factorial Designs R package version 1.0.0. Zenodo. http://doi.org/10.5281/zenodo.2669586

    +

    Lisa DeBruine, (2021). faux: Simulation for Factorial Designs R package version 1.1.0. Zenodo. http://doi.org/10.5281/zenodo.2669586

    @Manual{,
   title = {faux: Simulation for Factorial Designs},
   author = {Lisa DeBruine},
   doi = {10.5281/zenodo.2669586},
   publisher = {Zenodo},
   year = {2021},
-  note = {R package version 1.0.0},
+  note = {R package version 1.1.0},
   url = {https://debruine.github.io/faux/},
 }
    diff --git a/docs/extra.js b/docs/extra.js new file mode 100644 index 00000000..a7bf9fc1 --- /dev/null +++ b/docs/extra.js @@ -0,0 +1,166 @@ +/** + * adapted from rmarkdown/inst/rmd/h/navigation-1.1/tabsets.js + * + * jQuery Plugin: Sticky Tabs + * + * @author Aidan Lister + * adapted by Ruben Arslan to activate parent tabs too + * http://www.aidanlister.com/2014/03/persisting-the-tab-state-in-bootstrap/ + */ +(function($) { + "use strict"; + $.fn.rmarkdownStickyTabs = function() { + var context = this; + // Show the tab corresponding with the hash in the URL, or the first tab + var showStuffFromHash = function() { + var hash = window.location.hash; + var selector = hash ? 'a[href="' + hash + '"]' : 'li.active > a'; + var $selector = $(selector, context); + if($selector.data('toggle') === "tab") { + $selector.tab('show'); + // walk up the ancestors of this element, show any hidden tabs + $selector.parents('.section.tabset').each(function(i, elm) { + var link = $('a[href="#' + $(elm).attr('id') + '"]'); + if(link.data('toggle') === "tab") { + link.tab("show"); + } + }); + } + }; + + + // Set the correct tab when the page loads + showStuffFromHash(context); + + // Set the correct tab when a user uses their back/forward button + $(window).on('hashchange', function() { + showStuffFromHash(context); + }); + + // Change the URL when tabs are clicked + $('a', context).on('click', function(e) { + history.pushState(null, null, this.href); + showStuffFromHash(context); + }); + + return this; + }; +}(jQuery)); + +window.buildTabsets = function(tocID) { + // build a tabset from a section div with the .tabset class + function buildTabset(tabset) { + + // check for fade and pills options + var fade = tabset.hasClass("tabset-fade"); + var pills = tabset.hasClass("tabset-pills"); + var navClass = pills ? "nav-pills" : "nav-tabs"; + + // determine the heading level of the tabset and tabs + var match = tabset.attr('class').match(/level(\d) /); + if (match === null) + return; + var tabsetLevel = Number(match[1]); + var tabLevel = tabsetLevel + 1; + + // find all subheadings immediately below + var tabs = tabset.find("div.section.level" + tabLevel); + if (!tabs.length) + return; + + // create tablist and tab-content elements + var tabList = $('
      '); + $(tabs[0]).before(tabList); + var tabContent = $('
      '); + $(tabs[0]).before(tabContent); + + // build the tabset + var activeTab = 0; + tabs.each(function(i) { + + // get the tab div + var tab = $(tabs[i]); + + // get the id then sanitize it for use with bootstrap tabs + var id = tab.attr('id'); + + // see if this is marked as the active tab + if (tab.hasClass('active')) + activeTab = i; + + // remove any table of contents entries associated with + // this ID (since we'll be removing the heading element) + // TODO: the -1 at the end could be any -{digit}, use filter to select? + $("#" + tocID + " li a[href='#" + id + "-1']").parent().remove(); + + // sanitize the id for use with bootstrap tabs + id = id.replace(/[.\/?&!#<>]/g, '').replace(/\s/g, '_'); + tab.attr('id', id); + + // get the heading element within it, grab it's text, then remove it + var heading = tab.find('h' + tabLevel + ':first'); + var headingText = heading.text(); + heading.remove(); + + // build and append the tab list item + var a = $('' + headingText + ''); + a.attr('href', '#' + id); + a.attr('aria-controls', id); + var li = $('
    • '); + li.append(a); + tabList.append(li); + + // set it's attributes + tab.attr('role', 'tabpanel'); + tab.addClass('tab-pane'); + tab.addClass('tabbed-pane'); + if (fade) + tab.addClass('fade'); + + // move it into the tab content div + tab.detach().appendTo(tabContent); + }); + + // set active tab + $(tabList.children('li')[activeTab]).addClass('active'); + var active = $(tabContent.children('div.section')[activeTab]); + active.addClass('active'); + if (fade) + active.addClass('in'); + + if (tabset.hasClass("tabset-sticky")) + tabset.rmarkdownStickyTabs(); + } + + // convert section divs with the .tabset class to tabsets + var tabsets = $("div.section.tabset"); + tabsets.each(function(i) { + buildTabset($(tabsets[i])); + }); +}; + +$(document).ready(function () { + window.buildTabsets("toc"); + }); + + $(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open'); + }); +}); + +$(document).ready(function () { + /** + * The tabset creation above sometimes relies on empty headers to stop the + * tabbing. Though they shouldn't be included in the TOC in the first place, + * this will remove empty headers from the TOC after it's created. + */ + + // find all the empty elements and remove them (and their parents) + var empty_a = $("#toc").find("a").filter(":empty"); + empty_a.parent().remove(); + + // now find any empty
    • @@ -124,10 +130,10 @@

      Installation

      -

      You can install the released version of faux (1.0.0) from CRAN with:

      +

      You can install the released version of faux from CRAN with:

      -

      And the development version (1.0.0.9000) from GitHub with:

      +

      And the development version from GitHub with:

       # install.packages("devtools")
 devtools::install_github("debruine/faux")
      @@ -138,6 +144,7 @@

      Simulate data for a factorial design

      +

      See the Simulate by Design vignette for more details.

       between <- list(pet = c(cat = "Cat Owners", 
                         dog = "Dog Owners"))
@@ -164,10 +171,66 @@ 
      
 
      
 
      
 Simulate new data from an existing data table
      
+
      See the Simulate from Existing Data vignette for more details.
      
 
       new_iris <- sim_df(iris, 50, between = "Species") 
      
 
      Simulated iris dataset
      
 
      
+
      
+
      
+Simulate data for a mixed design
      
+
      You can build up a cross-classified or nested mixed effects design using piped functions. See the contrasts vignette for more details.
      
+
      +# simulate 20 classes with 20 to 30 students per class
+data <- add_random(class = 20) %>%
+  add_random(student = sample(20:30, 20, replace = TRUE), 
+             .nested_in = "class") %>%
+  add_between(.by = "class", 
+              school_type = c("private","public"), 
+              .prob = c(5, 15)) %>%
+  add_between(.by = "student",
+              gender = c("M", "F", "NB"),
+              .prob = c(.49, .49, .02))
      
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
      school_typegendern
      privateM66
      privateF54
      privateNB1
      publicM186
      publicF187
      publicNB8
      
+

      diff --git a/docs/news/index.html b/docs/news/index.html index e9fc53ae..b1d2a398 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -45,9 +45,11 @@ + + - + @@ -83,7 +85,7 @@ faux - 1.0.0 + 1.1.0

      @@ -123,12 +125,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • @@ -159,6 +167,116 @@

      Changelog

      Source: NEWS.md +
      +

      +faux 1.1.0 (2021-09-13) Unreleased +

      +
      +

      +Breaking changes

      +
        +
      • +sim_design() now names anonymous within and between factors like W and B or W1, W2, W3, …, and B1, B2, … instead of A, B, C, …
        • +
      +
      +
      +

      +New features

      + +
      +
      +

      +Minor improvements and fixes

      +
        +
      • +get_params() doesn’t need between, within, id, and dv set for date created by sim_design() +
        • +
      • +plot_design() can display a subset of factors
        • +
      • +sim_design() fixed a bug in when setting n with an unnamed vector and within-subjects factors
        • +
      +
      +
      +
      +

      +faux 1.0.0.9006 (2021-09-13) Unreleased +

      +
        +
      • Added a vignette about creating random reports using the mixed effects building functions.
        • +
      +
      +
      +

      +faux 1.0.0.9006 (2021-08-30) Unreleased +

      +
        +
      • fixed a bug in sim_design() when setting n with an unnamed vector and within-subjects factors (wouldn’t run before).
        • +
      • Updated add_between() and add_within() to make new columns factors with the same ordering as the specification
        • +
      • Updated contrast functions to work with non-factor columns
        • +
      • +add_between() .prob argument works as expected now (and has tests)
        • +
      +
      +
      +

      +faux 1.0.0.9005 (2021-08-11) Unreleased +

      + +
      +
      +

      +faux 1.0.0.9004 (2021-08-10) Unreleased +

      +
        +
      • Updated contrasts vignette
        • +
      • new add_contrast() function
        • +
      • renamed the contrast functions to all start with contr_code_ +
        • +
      +
      +
      +

      +faux 1.0.0.9003 (2021-08-09) Unreleased +

      +
        +
      • added new contrast functions and changed labelling of others
        • +
      • updated contrasts vignette
        • +
      +
      +
      +

      +faux 1.0.0.9002 (2021-08-08) Unreleased +

      +
        +
      • +plot_design() can display a subset of factors
        • +
      • updated plotting vignette to explain changing palettes
        • +
      • updated mixed effects builder functions to avoid column name clashes
        • +
      • added experimental contrast functions and vignette
        • +
      +
      +
      +

      +faux 1.0.0.9001 (2021-03-27) Unreleased +

      +
        +
      • added new mixed effect builder functions
        • +
      • updated mixed effects vignette
        • +
      • anonymous within and between factors in sim_design() are now named W and B or W1, W2, W3, …, B1, B2, … instead of A, B, C, … (and fixed relevant tests and vignette code)
        • +
      • fixed get_params() so it doesn’t need between, within, id, and dv set for date created by sim_design() +
        • +
      +

      faux 1.0.0 (2021-03-27) 2021-03-26 diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index faa395b5..c5fc690f 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -4,11 +4,16 @@ pkgdown_sha: ~ articles: codebook: codebook.html continuous: continuous.html + contrasts: contrasts.html distributions: distributions.html plots: plots.html + random_reports: random_reports.html rnorm_multi: rnorm_multi.html sim_design: sim_design.html sim_df: sim_df.html sim_mixed: sim_mixed.html -last_built: 2021-03-27T13:30Z +last_built: 2021-09-13T21:51Z +urls: + reference: https://debruine.github.io/faux/reference + article: https://debruine.github.io/faux/articles diff --git a/docs/reference/OR.html b/docs/reference/OR.html new file mode 100644 index 00000000..e8abd2a7 --- /dev/null +++ b/docs/reference/OR.html @@ -0,0 +1,228 @@ + + + + + + + + +Piped OR — OR • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Piped OR

      + Source: R/utils-pipe.R + +
      + +
      +

      LHS if not NULL, otherwise RHS

      +
      + + 
      l %||% r
      + +

      Arguments

      + + + + + + + + + + +
      l

      LHS.

      r

      RHS.

      + +

      Value

      + +

      LHS if not NULL, otherwise RHS.

      + +

      Examples

      + 
      x <- list(b = 2, c = 3)
+x$a %||% x$b %||% x$c
+
      #> [1] 2
      x$a %||% "default_value"
+
      #> [1] "default_value"
      
+
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png index 160eb6a0..cd64f695 100644 Binary files a/docs/reference/Rplot002.png and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/add_between.html b/docs/reference/add_between.html new file mode 100644 index 00000000..68f03858 --- /dev/null +++ b/docs/reference/add_between.html @@ -0,0 +1,249 @@ + + + + + + + + +Add between factors — add_between • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Add between factors

      + Source: R/add_ranef.R + +
      + +
      +

      Add between factors

      +
      + + 
      add_between(.data, .by = NULL, ..., .shuffle = FALSE, .prob = NULL)
      + +

      Arguments

      + + + + + + + + + + + + + + + + + + + + + + +
      .data

      the data frame

      .by

      the grouping column (groups by row if NULL)

      ...

      the names and levels of the new factors

      .shuffle

      whether to assign cells randomly or in "order"

      .prob

      probability of each level, equal if NULL

      + +

      Value

      + +

      data frame

      + +

      Examples

      + 
      add_random(subj = 4, item = 2) %>%
+  add_between("subj", condition = c("cntl", "test")) %>%
+  add_between("item", version = c("A", "B"))
+
      #> # A tibble: 8 x 4
+#>   subj  item  condition version
+#>   <chr> <chr> <fct>     <fct>  
+#> 1 s1    i1    cntl      A      
+#> 2 s1    i2    cntl      B      
+#> 3 s2    i1    test      A      
+#> 4 s2    i2    test      B      
+#> 5 s3    i1    cntl      A      
+#> 6 s3    i2    cntl      B      
+#> 7 s4    i1    test      A      
+#> 8 s4    i2    test      B
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/add_contrast.html b/docs/reference/add_contrast.html new file mode 100644 index 00000000..37f01edc --- /dev/null +++ b/docs/reference/add_contrast.html @@ -0,0 +1,273 @@ + + + + + + + + +Add a contrast to a data frame — add_contrast • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Add a contrast to a data frame

      + Source: R/contrasts.R + +
      + +
      +

      Add a contrast to a data frame

      +
      + + 
      add_contrast(
+  data,
+  col,
+  contrast = c("anova", "sum", "treatment", "helmert", "poly", "difference"),
+  levels = NULL,
+  ...,
+  add_cols = TRUE,
+  colnames = NULL
+)
      + +

      Arguments

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      data

      the data frame

      col

      the column to recode

      contrast

      the contrast to recode to

      levels

      the levels of the factor in order

      ...

      arguments to pass to the contrast function (base or omit)

      add_cols

      whether to just add the contrast to the existing column or also to create new explicit columns in the dataset (default)

      colnames

      optional list of column names for the added contrasts

      + +

      Value

      + +

      the data frame with the recoded column and added columns (if add_cols == TRUE)

      + +

      Examples

      + 
      df <- sim_design(between = list(time = 1:6), plot = FALSE) %>%
+   add_contrast("time", "poly")
+
+# test all polynomial contrasts
+lm(y ~ time, df) %>% broom::tidy()
+
      #> # A tibble: 6 x 5
+#>   term        estimate std.error statistic p.value
+#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
+#> 1 (Intercept)   0.0193    0.0416     0.465  0.642 
+#> 2 time^1       -0.152     0.102     -1.49   0.137 
+#> 3 time^2       -0.0521    0.102     -0.511  0.609 
+#> 4 time^3        0.0412    0.102      0.405  0.686 
+#> 5 time^4        0.0985    0.102      0.968  0.334 
+#> 6 time^5        0.222     0.102      2.18   0.0298
      
+# test only the linear and quadratic contrasts
+lm(y ~ `time^1` + `time^2`, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term        estimate std.error statistic p.value
+#>   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
+#> 1 (Intercept)   0.0193    0.0417     0.464   0.643
+#> 2 `time^1`     -0.152     0.102     -1.49    0.138
+#> 3 `time^2`     -0.0521    0.102     -0.510   0.610
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/add_random.html b/docs/reference/add_random.html new file mode 100644 index 00000000..9265f61d --- /dev/null +++ b/docs/reference/add_random.html @@ -0,0 +1,261 @@ + + + + + + + + +Add random factors to a data structure — add_random • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Add random factors to a data structure

      + Source: R/add_ranef.R + +
      + +
      +

      Add random factors to a data structure

      +
      + + 
      add_random(.data = NULL, ..., .nested_in = NULL)
      + +

      Arguments

      + + + + + + + + + + + + + + +
      .data

      the data frame

      ...

      the new random factor column name and the number of values of the random factor (if crossed) or the n per group (if nested); can be a vector of n per group if nested

      .nested_in

      the column(s) to nest in (if NULL, the factor is crossed with all columns)

      + +

      Value

      + +

      a data frame

      + +

      Examples

      + 
      # start a data frame
+data1 <- add_random(school = 3)
+# nest classes in schools (2 classes per school)
+data2 <- add_random(data1, class = 2, .nested_in = "school")
+# nest pupils in each class (different n per class)
+data3 <- add_random(data2, pupil = c(20, 24, 23, 21, 25, 24), .nested_in = "class")
+# cross each pupil with 10 questions
+data4 <- add_random(data3, question = 10)
+
+# compare nesting in 2 different factors
+data <- add_random(A = 2, B = 2)
+add_random(data, C = 2, .nested_in = "A")
+
      #> # A tibble: 8 x 3
+#>   A     B     C    
+#>   <chr> <chr> <chr>
+#> 1 A1    B1    C1   
+#> 2 A1    B1    C2   
+#> 3 A1    B2    C1   
+#> 4 A1    B2    C2   
+#> 5 A2    B1    C3   
+#> 6 A2    B1    C4   
+#> 7 A2    B2    C3   
+#> 8 A2    B2    C4   
      add_random(data, C = 2, .nested_in = "B")
+
      #> # A tibble: 8 x 3
+#>   A     B     C    
+#>   <chr> <chr> <chr>
+#> 1 A1    B1    C1   
+#> 2 A1    B1    C2   
+#> 3 A1    B2    C3   
+#> 4 A1    B2    C4   
+#> 5 A2    B1    C1   
+#> 6 A2    B1    C2   
+#> 7 A2    B2    C3   
+#> 8 A2    B2    C4
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/add_ranef.html b/docs/reference/add_ranef.html new file mode 100644 index 00000000..d1d59cda --- /dev/null +++ b/docs/reference/add_ranef.html @@ -0,0 +1,250 @@ + + + + + + + + +Add random effects to a data frame — add_ranef • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Add random effects to a data frame

      + Source: R/add_ranef.R + +
      + +
      +

      Add random effects to a data frame

      +
      + + 
      add_ranef(.data, .by = NULL, ..., .cors = 0, .empirical = FALSE)
      + +

      Arguments

      + + + + + + + + + + + + + + + + + + + + + + +
      .data

      the data frame

      .by

      the grouping column (groups by row if NULL)

      ...

      the name and standard deviation of each random effect

      .cors

      the correlations among multiple random effects, to be passed to rnorm_multi as r

      .empirical

      logical. To be passed to rnorm_multi as empirical

      + +

      Value

      + +

      data frame with new random effects columns

      + +

      Examples

      + 
      add_random(rater = 2, stimulus = 2, time = 2) %>%
+  add_ranef("rater", u0r = 1.5) %>%
+  add_ranef("stimulus", u0s = 2.2, u1s = 0.75, .cors = 0.5) %>%
+  add_ranef(c("rater", "stimulus"), u0sr = 1.2)
+
      #> # A tibble: 8 x 7
+#>   rater stimulus time    u0r    u0s    u1s   u0sr
+#>   <chr> <chr>    <chr> <dbl>  <dbl>  <dbl>  <dbl>
+#> 1 r1    s1       t1    0.354 -0.977  0.147 -1.14 
+#> 2 r1    s1       t2    0.354 -0.977  0.147 -1.14 
+#> 3 r1    s2       t1    0.354 -4.47  -0.111  0.572
+#> 4 r1    s2       t2    0.354 -4.47  -0.111  0.572
+#> 5 r2    s1       t1    0.943 -0.977  0.147 -0.954
+#> 6 r2    s1       t2    0.943 -0.977  0.147 -0.954
+#> 7 r2    s2       t1    0.943 -4.47  -0.111  0.281
+#> 8 r2    s2       t2    0.943 -4.47  -0.111  0.281
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/add_recode.html b/docs/reference/add_recode.html new file mode 100644 index 00000000..44171d83 --- /dev/null +++ b/docs/reference/add_recode.html @@ -0,0 +1,253 @@ + + + + + + + + +Recode a categorical column — add_recode • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Recode a categorical column

      + Source: R/add_ranef.R + +
      + +
      +

      Recode a categorical column

      +
      + + 
      add_recode(.data, .col, .newcol = paste0(col, ".c"), ...)
      + +

      Arguments

      + + + + + + + + + + + + + + + + + + +
      .data

      the data frame

      .col

      the column to recode

      .newcol

      the name of the recoded column (defaults to col.c)

      ...

      coding for categorical column

      + +

      Value

      + +

      data frame with new fixed effects columns

      + +

      Examples

      + 
      add_random(subj = 4, item = 4) %>%
+  add_between("subj", cond = c("cntl", "test")) %>%
+  add_recode("cond", "cond.t", cntl = 0, test = 1)
+
      #> # A tibble: 16 x 4
+#>    subj  item  cond  cond.t
+#>    <chr> <chr> <fct>  <dbl>
+#>  1 s1    i1    cntl       0
+#>  2 s1    i2    cntl       0
+#>  3 s1    i3    cntl       0
+#>  4 s1    i4    cntl       0
+#>  5 s2    i1    test       1
+#>  6 s2    i2    test       1
+#>  7 s2    i3    test       1
+#>  8 s2    i4    test       1
+#>  9 s3    i1    cntl       0
+#> 10 s3    i2    cntl       0
+#> 11 s3    i3    cntl       0
+#> 12 s3    i4    cntl       0
+#> 13 s4    i1    test       1
+#> 14 s4    i2    test       1
+#> 15 s4    i3    test       1
+#> 16 s4    i4    test       1
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/add_within.html b/docs/reference/add_within.html new file mode 100644 index 00000000..49dc9ee0 --- /dev/null +++ b/docs/reference/add_within.html @@ -0,0 +1,240 @@ + + + + + + + + +Add within factors — add_within • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Add within factors

      + Source: R/add_ranef.R + +
      + +
      +

      Add within factors

      +
      + + 
      add_within(.data, .by = NULL, ...)
      + +

      Arguments

      + + + + + + + + + + + + + + +
      .data

      the data frame

      .by

      the grouping column (groups by row if NULL)

      ...

      the names and levels of the new factors

      + +

      Value

      + +

      data frame

      + +

      Examples

      + 
      add_random(subj = 2, item =  2) %>%
+  add_within("subj", time = c("pre", "post"))
+
      #> # A tibble: 8 x 3
+#>   subj  item  time 
+#>   <chr> <chr> <fct>
+#> 1 s1    i1    pre  
+#> 2 s1    i1    post 
+#> 3 s1    i2    pre  
+#> 4 s1    i2    post 
+#> 5 s2    i1    pre  
+#> 6 s2    i1    post 
+#> 7 s2    i2    pre  
+#> 8 s2    i2    post
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/average_r2tau_0.html b/docs/reference/average_r2tau_0.html index 113b8134..8b00cf5c 100644 --- a/docs/reference/average_r2tau_0.html +++ b/docs/reference/average_r2tau_0.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0

      @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/beta2norm-1.png b/docs/reference/beta2norm-1.png index 0446df7b..1f52f84b 100644 Binary files a/docs/reference/beta2norm-1.png and b/docs/reference/beta2norm-1.png differ diff --git a/docs/reference/beta2norm.html b/docs/reference/beta2norm.html index d1067af6..518f36a2 100644 --- a/docs/reference/beta2norm.html +++ b/docs/reference/beta2norm.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -200,10 +208,10 @@

      Examp 
      
 x <- rbeta(10000, 2, 3)
 y <- beta2norm(x)
-
      #> shape1 was set to 2.02135934170718
      #> shape2 was set to 3.03809647001309
      g <- ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x, y))
+
      #> shape1 was set to 2.03064148701798
      #> shape2 was set to 3.06478809294635
      g <- ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x, y))
 ggExtra::ggMarginal(g, type = "histogram")
 
      
-
      + @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • diff --git a/docs/reference/cell_combos.html b/docs/reference/cell_combos.html index b5533836..4771c2db 100644 --- a/docs/reference/cell_combos.html +++ b/docs/reference/cell_combos.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/check_design-1.png b/docs/reference/check_design-1.png index 92f7e4e6..1a7e69ec 100644 Binary files a/docs/reference/check_design-1.png and b/docs/reference/check_design-1.png differ diff --git a/docs/reference/check_design-2.png b/docs/reference/check_design-2.png index a8019c0f..d5e6ff7b 100644 Binary files a/docs/reference/check_design-2.png and b/docs/reference/check_design-2.png differ diff --git a/docs/reference/check_design.html b/docs/reference/check_design.html index 3c2553b6..f951e5cb 100644 --- a/docs/reference/check_design.html +++ b/docs/reference/check_design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/check_mixed_design.html b/docs/reference/check_mixed_design.html index 81ce76c1..e7e62913 100644 --- a/docs/reference/check_mixed_design.html +++ b/docs/reference/check_mixed_design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/codebook.html b/docs/reference/codebook.html index dbc356e6..e7e9c8e4 100644 --- a/docs/reference/codebook.html +++ b/docs/reference/codebook.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -223,7 +231,7 @@

      Examp type = c("float", "float", "float", "float", "string") ) codebook(iris, vardesc = vardesc) -
      #> Warning: The following variable properties are not standard: type
      #> Sepal.Length set to dataType float
      #> Sepal.Width set to dataType float
      #> Petal.Length set to dataType float
      #> Petal.Width set to dataType float
      #> Species set to dataType string
      #> { +
      #> Warning: The following variable properties are not standard: type
      #> Sepal.Length set to dataType float
      #> Sepal.Width set to dataType float
      #> Petal.Length set to dataType float
      #> Petal.Width set to dataType float
      #> Species set to dataType string
      #> { #> "@context": "https://schema.org/", #> "@type": "Dataset", #> "name": "iris", @@ -273,7 +281,7 @@

      Examp #> ] #> } #>

      -
      + @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • diff --git a/docs/reference/contr_code_anova.html b/docs/reference/contr_code_anova.html new file mode 100644 index 00000000..088edf0d --- /dev/null +++ b/docs/reference/contr_code_anova.html @@ -0,0 +1,265 @@ + + + + + + + + +Anova code a factor — contr_code_anova • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Anova code a factor

      + Source: R/contrasts.R + +
      + +
      +

      Anova coding (also called deviation or simple coding) sets the grand mean as the intercept. +Each contrast compares one level with the reference level (base).

      +
      + + 
      contr_code_anova(fct, levels = NULL, base = 1)
      + +

      Arguments

      + + + + + + + + + + + + + + +
      fct

      the factor to contrast code (or a vector)

      levels

      the levels of the factor in order

      base

      the index of the level to use as baseline

      + +

      Value

      + +

      the factor with contrasts set

      + +

      Examples

      + 
      df <- sim_design(between = list(pet = c("cat", "dog")), 
+                 mu = c(10, 20), plot = FALSE)
+df$pet <- contr_code_anova(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 2 x 5
+#>   term        estimate std.error statistic   p.value
+#>   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)    15.1     0.0639     236.  1.74e-244
+#> 2 pet.dog-cat     9.84    0.128       77.0 1.61e-149
      
+df <- sim_design(between = list(pet = c("cat", "dog", "ferret")), 
+                 mu = c(2, 4, 9), empirical = TRUE, plot = FALSE)
+                 
+df$pet <- contr_code_anova(df$pet, base = 1)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        5.00    0.0577      86.6 8.45e-213
+#> 2 pet.dog-cat        2.00    0.141       14.1 4.53e- 35
+#> 3 pet.ferret-cat     7.00    0.141       49.5 1.67e-145
      
+df$pet <- contr_code_anova(df$pet, base = 2)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        5.00    0.0577      86.6 8.45e-213
+#> 2 pet.cat-dog       -2.00    0.141      -14.1 4.53e- 35
+#> 3 pet.ferret-dog     5       0.141       35.4 1.89e-108
      
+df$pet <- contr_code_anova(df$pet, base = "ferret")
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        5       0.0577      86.6 8.45e-213
+#> 2 pet.cat-ferret    -7.00    0.141      -49.5 1.67e-145
+#> 3 pet.dog-ferret    -5.00    0.141      -35.4 1.89e-108
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/contr_code_difference.html b/docs/reference/contr_code_difference.html new file mode 100644 index 00000000..bd2b2cbf --- /dev/null +++ b/docs/reference/contr_code_difference.html @@ -0,0 +1,237 @@ + + + + + + + + +Difference code a factor — contr_code_difference • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Difference code a factor

      + Source: R/contrasts.R + +
      + +
      +

      Difference coding sets the grand mean as the intercept. +Each contrast compares one level with the previous level.

      +
      + + 
      contr_code_difference(fct, levels = NULL)
      + +

      Arguments

      + + + + + + + + + + +
      fct

      the factor to contrast code (or a vector)

      levels

      the levels of the factor in order

      + +

      Value

      + +

      the factor with contrasts set

      + +

      Examples

      + 
      df <- sim_design(between = list(pet = c("cat", "dog", "ferret")), 
+                 mu = c(2, 4, 9), empirical = TRUE, plot = FALSE)
+                 
+df$pet <- contr_code_difference(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        5.0     0.0577      86.6 8.45e-213
+#> 2 pet.dog-cat        2.00    0.141       14.1 4.53e- 35
+#> 3 pet.ferret-dog     5       0.141       35.4 1.89e-108
      
+
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/contr_code_helmert.html b/docs/reference/contr_code_helmert.html new file mode 100644 index 00000000..06190475 --- /dev/null +++ b/docs/reference/contr_code_helmert.html @@ -0,0 +1,262 @@ + + + + + + + + +Helmert code a factor — contr_code_helmert • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Helmert code a factor

      + Source: R/contrasts.R + +
      + +
      +

      Helmert coding sets the grand mean as the intercept. +Each contrast compares one level with the mean of previous levels.

      +
      + + 
      contr_code_helmert(fct, levels = NULL)
      + +

      Arguments

      + + + + + + + + + + +
      fct

      the factor to contrast code (or a vector)

      levels

      the levels of the factor in order

      + +

      Value

      + +

      the factor with contrasts set

      + +

      Examples

      + 
      df <- sim_design(between = list(pet = c("cat", "dog")), 
+                 mu = c(10, 20), plot = FALSE)
+df$pet <- contr_code_helmert(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 2 x 5
+#>   term        estimate std.error statistic   p.value
+#>   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)    15.1     0.0731     206.  4.51e-233
+#> 2 pet.dog-cat     9.91    0.146       67.8 5.82e-139
      
+df <- sim_design(between = list(pet = c("cat", "dog", "ferret")), 
+                 mu = c(2, 4, 9), empirical = TRUE, plot = FALSE)
+                 
+df$pet <- contr_code_helmert(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term               estimate std.error statistic   p.value
+#>   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)            5.0     0.0577      86.6 8.45e-213
+#> 2 pet.dog-cat            2.00    0.141       14.1 4.53e- 35
+#> 3 pet.ferret-cat.dog     6       0.122       49.0 2.56e-144
      
+# reorder the levels to change the comparisons
+df$pet <- contr_code_helmert(df$pet, levels = c("dog", "cat", "ferret"))
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term               estimate std.error statistic   p.value
+#>   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)            5.0     0.0577      86.6 8.45e-213
+#> 2 pet.cat-dog           -2.00    0.141      -14.1 4.53e- 35
+#> 3 pet.ferret-dog.cat     6       0.122       49.0 2.56e-144
      
+df$pet <- contr_code_helmert(df$pet, levels = c("ferret", "dog", "cat"))
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term               estimate std.error statistic   p.value
+#>   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)            5       0.0577      86.6 8.45e-213
+#> 2 pet.dog-ferret        -5       0.141      -35.4 1.89e-108
+#> 3 pet.cat-ferret.dog    -4.50    0.122      -36.7 1.71e-112
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/contr_code_poly.html b/docs/reference/contr_code_poly.html new file mode 100644 index 00000000..fe898722 --- /dev/null +++ b/docs/reference/contr_code_poly.html @@ -0,0 +1,241 @@ + + + + + + + + +Polynomial code a factor — contr_code_poly • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Polynomial code a factor

      + Source: R/contrasts.R + +
      + +
      +

      Polynomial coding sets the grand mean as the intercept. +Each contrast tests a trend (linear, quadratic, cubic, etc.). This is only suitable for ordered factors.

      +
      + + 
      contr_code_poly(fct, levels = NULL)
      + +

      Arguments

      + + + + + + + + + + +
      fct

      the factor to contrast code (or a vector)

      levels

      the levels of the factor in order

      + +

      Value

      + +

      the factor with contrasts set

      + +

      Examples

      + 
      df <- sim_design(within = list(time = 1:6), 
+                 mu = 1:6 + (1:6-3.5)^2, 
+                 long = TRUE, plot = FALSE)
+                 
+df$time <- contr_code_poly(df$time)
+lm(y ~ time, df) %>% broom::tidy()
+
      #> # A tibble: 6 x 5
+#>   term         estimate std.error statistic   p.value
+#>   <chr>           <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)  6.38        0.0409 156.      0        
+#> 2 time^1       4.15        0.100   41.4     2.91e-177
+#> 3 time^2       6.17        0.100   61.6     5.45e-260
+#> 4 time^3      -0.0449      0.100   -0.448   6.55e-  1
+#> 5 time^4       0.00516     0.100    0.0515  9.59e-  1
+#> 6 time^5      -0.000218    0.100   -0.00217 9.98e-  1
      
+
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/contr_code_sum.html b/docs/reference/contr_code_sum.html new file mode 100644 index 00000000..20aa9564 --- /dev/null +++ b/docs/reference/contr_code_sum.html @@ -0,0 +1,259 @@ + + + + + + + + +Sum code a factor — contr_code_sum • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Sum code a factor

      + Source: R/contrasts.R + +
      + +
      +

      Sum coding sets the grand mean as the intercept. +Each contrast compares one level with the grand mean.

      +
      + + 
      contr_code_sum(fct, levels = NULL, omit = length(levels))
      + +

      Arguments

      + + + + + + + + + + + + + + +
      fct

      the factor to contrast code (or a vector)

      levels

      the levels of the factor in order

      omit

      the level to omit (defaults to the last level)

      + +

      Value

      + +

      the factor with contrasts set

      + +

      Examples

      + 
      df <- sim_design(between = list(pet = c("cat", "dog", "bird", "ferret")), 
+                 mu = c(2, 4, 9, 13), empirical = TRUE, plot = FALSE)
+
+df$pet <- contr_code_sum(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 4 x 5
+#>   term               estimate std.error statistic   p.value
+#>   <chr>                 <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)            7       0.0500     140.  0        
+#> 2 pet.cat-intercept     -5.00    0.0866     -57.7 6.13e-195
+#> 3 pet.dog-intercept     -3.00    0.0866     -34.6 6.42e-122
+#> 4 pet.bird-intercept     2.00    0.0866      23.1 2.33e- 75
      
+df$pet <- contr_code_sum(df$pet, omit = "cat")
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 4 x 5
+#>   term                 estimate std.error statistic   p.value
+#>   <chr>                   <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)              7       0.0500     140.  0        
+#> 2 pet.dog-intercept       -3.00    0.0866     -34.6 6.42e-122
+#> 3 pet.bird-intercept       2.00    0.0866      23.1 2.33e- 75
+#> 4 pet.ferret-intercept     6       0.0866      69.3 1.82e-223
      
+df$pet <- contr_code_sum(df$pet, omit = 1)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 4 x 5
+#>   term                 estimate std.error statistic   p.value
+#>   <chr>                   <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)              7       0.0500     140.  0        
+#> 2 pet.dog-intercept       -3.00    0.0866     -34.6 6.42e-122
+#> 3 pet.bird-intercept       2.00    0.0866      23.1 2.33e- 75
+#> 4 pet.ferret-intercept     6       0.0866      69.3 1.82e-223
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/contr_code_treatment.html b/docs/reference/contr_code_treatment.html new file mode 100644 index 00000000..84d4913f --- /dev/null +++ b/docs/reference/contr_code_treatment.html @@ -0,0 +1,265 @@ + + + + + + + + +Treatment code a factor — contr_code_treatment • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      +
      + + + + +
      + +
      +
      +
      +

      Treatment code a factor

      + Source: R/contrasts.R + +
      + +
      +

      Treatment coding sets the mean of the reference level (base) as the intercept. +Each contrast compares one level with the reference level.

      +
      + + 
      contr_code_treatment(fct, levels = NULL, base = 1)
      + +

      Arguments

      + + + + + + + + + + + + + + +
      fct

      the factor to contrast code (or a vector)

      levels

      the levels of the factor in order

      base

      the index of the level to use as baseline

      + +

      Value

      + +

      the factor with contrasts set

      + +

      Examples

      + 
      df <- sim_design(between = list(pet = c("cat", "dog")), 
+                 mu = c(10, 20), plot = FALSE)
+df$pet <- contr_code_treatment(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 2 x 5
+#>   term        estimate std.error statistic   p.value
+#>   <chr>          <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)    10.1      0.110      91.8 3.19e-164
+#> 2 pet.dog-cat     9.76     0.156      62.5 2.48e-132
      
+df <- sim_design(between = list(pet = c("cat", "dog", "ferret")), 
+                 mu = c(2, 4, 9), empirical = TRUE, plot = FALSE)
+                 
+df$pet <- contr_code_treatment(df$pet)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        2.00     0.100      20.0 5.87e- 57
+#> 2 pet.dog-cat        2.00     0.141      14.1 4.53e- 35
+#> 3 pet.ferret-cat     7.00     0.141      49.5 1.67e-145
      
+df$pet <- contr_code_treatment(df$pet, base = 2)
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        4.00     0.100      40.0 1.30e-121
+#> 2 pet.cat-dog       -2.00     0.141     -14.1 4.53e- 35
+#> 3 pet.ferret-dog     5.00     0.141      35.4 1.89e-108
      
+df$pet <- contr_code_treatment(df$pet, base = "ferret")
+lm(y ~ pet, df) %>% broom::tidy()
+
      #> # A tibble: 3 x 5
+#>   term           estimate std.error statistic   p.value
+#>   <chr>             <dbl>     <dbl>     <dbl>     <dbl>
+#> 1 (Intercept)        9.00     0.1        90.0 1.40e-217
+#> 2 pet.cat-ferret    -7.00     0.141     -49.5 1.67e-145
+#> 3 pet.dog-ferret    -5        0.141     -35.4 1.89e-108
      +
      + +
      + + +
      +
      +

      Developed by Lisa DeBruine.

      +
      + +
      +

      Site built with pkgdown 1.6.1.

      +
      + +
      +
      + + + + + + + + diff --git a/docs/reference/convert_param.html b/docs/reference/convert_param.html index 87b6bf0f..7c1542fe 100644 --- a/docs/reference/convert_param.html +++ b/docs/reference/convert_param.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/cormat.html b/docs/reference/cormat.html index 9e800e98..d3ce3b25 100644 --- a/docs/reference/cormat.html +++ b/docs/reference/cormat.html @@ -45,11 +45,13 @@ + + - + @@ -85,7 +87,7 @@ faux - 1.0.0 + 1.1.0 @@ -125,12 +127,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/cormat_from_triangle.html b/docs/reference/cormat_from_triangle.html index f5898106..59be3eb7 100644 --- a/docs/reference/cormat_from_triangle.html +++ b/docs/reference/cormat_from_triangle.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/faceratings.html b/docs/reference/faceratings.html index 43f4719b..2a1d9977 100644 --- a/docs/reference/faceratings.html +++ b/docs/reference/faceratings.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/faux.html b/docs/reference/faux.html index cd3b2c8c..dfedab5c 100644 --- a/docs/reference/faux.html +++ b/docs/reference/faux.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/faux_options.html b/docs/reference/faux_options.html index f9d3a4b1..a12a81d2 100644 --- a/docs/reference/faux_options.html +++ b/docs/reference/faux_options.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/figures/card.png b/docs/reference/figures/card.png index 2aaa216a..2308f870 100644 Binary files a/docs/reference/figures/card.png and b/docs/reference/figures/card.png differ diff --git a/docs/reference/figures/get-design-long-1.png b/docs/reference/figures/get-design-long-1.png index 07014458..7e4a2dd8 100644 Binary files a/docs/reference/figures/get-design-long-1.png and b/docs/reference/figures/get-design-long-1.png differ diff --git a/docs/reference/figures/plot-design-1.png b/docs/reference/figures/plot-design-1.png index 5b19b8d4..f9f00111 100644 Binary files a/docs/reference/figures/plot-design-1.png and b/docs/reference/figures/plot-design-1.png differ diff --git a/docs/reference/figures/plot-iris-sim-1.png b/docs/reference/figures/plot-iris-sim-1.png index 048a9fcd..5904a2dc 100644 Binary files a/docs/reference/figures/plot-iris-sim-1.png and b/docs/reference/figures/plot-iris-sim-1.png differ diff --git a/docs/reference/figures/plot-rnorm-pre-1.png b/docs/reference/figures/plot-rnorm-pre-1.png index ea6b2e82..3bcdc9f0 100644 Binary files a/docs/reference/figures/plot-rnorm-pre-1.png and b/docs/reference/figures/plot-rnorm-pre-1.png differ diff --git a/docs/reference/fix_name_labels.html b/docs/reference/fix_name_labels.html index 72373d53..07736306 100644 --- a/docs/reference/fix_name_labels.html +++ b/docs/reference/fix_name_labels.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/fr4.html b/docs/reference/fr4.html index e31a5e61..bd27e970 100644 --- a/docs/reference/fr4.html +++ b/docs/reference/fr4.html @@ -45,11 +45,13 @@ + + - + @@ -85,7 +87,7 @@ faux - 1.0.0 + 1.1.0 @@ -125,12 +127,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/gamma2norm-1.png b/docs/reference/gamma2norm-1.png index d6a1bb4f..3f0f30f7 100644 Binary files a/docs/reference/gamma2norm-1.png and b/docs/reference/gamma2norm-1.png differ diff --git a/docs/reference/gamma2norm.html b/docs/reference/gamma2norm.html index 87e9a0b7..8f649998 100644 --- a/docs/reference/gamma2norm.html +++ b/docs/reference/gamma2norm.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -204,10 +212,10 @@

      Examp 
      
 x <- rgamma(10000, 2)
 y <- gamma2norm(x)
-
      #> shape was set to 1.99715252111198
      g <- ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x, y))
+
      #> shape was set to 1.99570416249303
      g <- ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x, y))
 ggExtra::ggMarginal(g, type = "histogram")
 
      
-
      + @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • diff --git a/docs/reference/get_params.html b/docs/reference/get_params.html index 8efe56b3..15b15862 100644 --- a/docs/reference/get_params.html +++ b/docs/reference/get_params.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -170,7 +178,7 @@

      Get parameters from a data table

      between = NULL, within = NULL, dv = NULL, - id = "id", + id = NULL, digits = 2 ) @@ -179,7 +187,7 @@

      Get parameters from a data table

      between = NULL, within = NULL, dv = NULL, - id = "id", + id = NULL, digits = 2 ) diff --git a/docs/reference/getcols.html b/docs/reference/getcols.html index a90e453e..28295827 100644 --- a/docs/reference/getcols.html +++ b/docs/reference/getcols.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/index.html b/docs/reference/index.html index c9a14f73..93a0351b 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -45,9 +45,11 @@ + + - + @@ -83,7 +85,7 @@ faux - 1.0.0 + 1.1.0 @@ -123,12 +125,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -204,6 +212,55 @@

      sim_joint_dist()

      + +

      Simulate category joint distribution

      + + + + +

      Mixed effects functions

      +

      + + + + + + + + + + +

      add_random()

      + +

      Add random factors to a data structure

      + + + +

      add_within()

      + +

      Add within factors

      + + + +

      add_between()

      + +

      Add between factors

      + + + +

      add_recode()

      + +

      Recode a categorical column

      + + + +

      add_ranef()

      + +

      Add random effects to a data frame

      + +

      sim_mixed_cc()

      @@ -214,12 +271,61 @@

      sim_mixed_df()

      Generate a mixed design from existing data

      + + + + +

      Contrasts

      +

      + + + + + + + + + + +

      add_contrast()

      + +

      Add a contrast to a data frame

      -

      sim_joint_dist()

      +

      contr_code_treatment()

      -

      Simulate category joint distribution

      +

      Treatment code a factor

      + + + +

      contr_code_anova()

      + +

      Anova code a factor

      + + + +

      contr_code_sum()

      + +

      Sum code a factor

      + + + +

      contr_code_difference()

      + +

      Difference code a factor

      + + + +

      contr_code_helmert()

      + +

      Helmert code a factor

      + + + +

      contr_code_poly()

      + +

      Polynomial code a factor

      @@ -351,6 +457,12 @@

      get_design()

      + +

      Get design

      + +

      get_design_long()

      @@ -406,9 +518,9 @@

      sim_data()

      +

      set_design()

      -

      Simulate data from design (internal)

      +

      Set design

      diff --git a/docs/reference/interactive_design.html b/docs/reference/interactive_design.html index 68ff634a..fa0114ab 100644 --- a/docs/reference/interactive_design.html +++ b/docs/reference/interactive_design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • diff --git a/docs/reference/is_pos_def.html b/docs/reference/is_pos_def.html index f517dcce..39dc7804 100644 --- a/docs/reference/is_pos_def.html +++ b/docs/reference/is_pos_def.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/json_design-1.png b/docs/reference/json_design-1.png index 78a74cd9..0ccfcab3 100644 Binary files a/docs/reference/json_design-1.png and b/docs/reference/json_design-1.png differ diff --git a/docs/reference/json_design.html b/docs/reference/json_design.html index 62f46dc4..0dd717c5 100644 --- a/docs/reference/json_design.html +++ b/docs/reference/json_design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -199,18 +207,18 @@

      Value

      Examples

      des <- check_design(2,2)
 
      json_design(des)
-
      #> {"within":{"A":{"A1":"A1","A2":"A2"}},"between":{"B":{"B1":"B1","B2":"B2"}},"dv":{"y":"value"},"id":{"id":"id"},"n":{"B1":100,"B2":100},"mu":{"B1":{"A1":0,"A2":0},"B2":{"A1":0,"A2":0}},"sd":{"B1":{"A1":1,"A2":1},"B2":{"A1":1,"A2":1}},"r":{"B1":[[1,0],[0,1]],"B2":[[1,0],[0,1]]},"sep":"_"} 
      json_design(des, pretty = TRUE)
+
      #> {"within":{"W1":{"W1a":"W1a","W1b":"W1b"}},"between":{"B1":{"B1a":"B1a","B1b":"B1b"}},"dv":{"y":"value"},"id":{"id":"id"},"vardesc":{"W1":"W1","B1":"B1"},"n":{"B1a":100,"B1b":100},"mu":{"B1a":{"W1a":0,"W1b":0},"B1b":{"W1a":0,"W1b":0}},"sd":{"B1a":{"W1a":1,"W1b":1},"B1b":{"W1a":1,"W1b":1}},"r":{"B1a":[[1,0],[0,1]],"B1b":[[1,0],[0,1]]},"sep":"_"} 
      json_design(des, pretty = TRUE)
 
      #> {
 #>   "within": {
-#>     "A": {
-#>       "A1": "A1",
-#>       "A2": "A2"
+#>     "W1": {
+#>       "W1a": "W1a",
+#>       "W1b": "W1b"
 #>     }
 #>   },
 #>   "between": {
-#>     "B": {
-#>       "B1": "B1",
-#>       "B2": "B2"
+#>     "B1": {
+#>       "B1a": "B1a",
+#>       "B1b": "B1b"
 #>     }
 #>   },
 #>   "dv": {
@@ -219,36 +227,40 @@ 
      Examp
 #>   "id": {
 #>     "id": "id"
 #>   },
+#>   "vardesc": {
+#>     "W1": "W1",
+#>     "B1": "B1"
+#>   },
 #>   "n": {
-#>     "B1": 100,
-#>     "B2": 100
+#>     "B1a": 100,
+#>     "B1b": 100
 #>   },
 #>   "mu": {
-#>     "B1": {
-#>       "A1": 0,
-#>       "A2": 0
+#>     "B1a": {
+#>       "W1a": 0,
+#>       "W1b": 0
 #>     },
-#>     "B2": {
-#>       "A1": 0,
-#>       "A2": 0
+#>     "B1b": {
+#>       "W1a": 0,
+#>       "W1b": 0
 #>     }
 #>   },
 #>   "sd": {
-#>     "B1": {
-#>       "A1": 1,
-#>       "A2": 1
+#>     "B1a": {
+#>       "W1a": 1,
+#>       "W1b": 1
 #>     },
-#>     "B2": {
-#>       "A1": 1,
-#>       "A2": 1
+#>     "B1b": {
+#>       "W1a": 1,
+#>       "W1b": 1
 #>     }
 #>   },
 #>   "r": {
-#>     "B1": [
+#>     "B1a": [
 #>       [1, 0],
 #>       [0, 1]
 #>     ],
-#>     "B2": [
+#>     "B1b": [
 #>       [1, 0],
 #>       [0, 1]
 #>     ]
diff --git a/docs/reference/long2wide-1.png b/docs/reference/long2wide-1.png
index 78a74cd9..0ccfcab3 100644
Binary files a/docs/reference/long2wide-1.png and b/docs/reference/long2wide-1.png differ
diff --git a/docs/reference/long2wide.html b/docs/reference/long2wide.html
index 75de0ee8..beda5b39 100644
--- a/docs/reference/long2wide.html
+++ b/docs/reference/long2wide.html
@@ -45,10 +45,12 @@
 
 
 
+  
+  
 
 
 
-
+
 
 
 
@@ -84,7 +86,7 @@
       
       
         faux
-        1.0.0
+        1.1.0
       
     
      
 
@@ -124,12 +126,18 @@
     
    • 
       Mixed Design Simulation
     
      • 
+    
    • 
+      Randomised Reports
+    
      • 
     
    • 
       Distribution Conversions
     
      • 
     
    • 
       Codebook Demo
     
      • 
+    
    • 
+      Contrasts
+    
      •
    • @@ -199,207 +207,207 @@

      Value

      Examples

      df_long <- sim_design(2, 2, long = TRUE)
 
      long2wide(df_long, "A", "B")
-
      #>       id  B           A1            A2
-#> 1   S001 B1  0.613187230  1.9106195582
-#> 2   S002 B1 -0.179933367 -0.2969882692
-#> 3   S003 B1  1.133582984 -0.7703411434
-#> 4   S004 B1  0.635179949  1.3104291868
-#> 5   S005 B1  0.666901608  0.8611578173
-#> 6   S006 B1 -0.160632856 -0.6264580296
-#> 7   S007 B1 -1.671074830 -0.1398622943
-#> 8   S008 B1 -0.487399761 -0.8764930975
-#> 9   S009 B1  0.642124419 -1.3168408654
-#> 10  S010 B1  0.444649174  1.6731753010
-#> 11  S011 B1 -0.350526284  1.3976308713
-#> 12  S012 B1  0.282726109 -1.5419109096
-#> 13  S013 B1 -1.239881220  0.6092452612
-#> 14  S014 B1  2.012575597  1.0797085829
-#> 15  S015 B1  1.335759193  0.2311554997
-#> 16  S016 B1  0.926957478  1.4191247301
-#> 17  S017 B1  0.516930020 -0.6915881966
-#> 18  S018 B1  1.071716788 -0.5379507755
-#> 19  S019 B1 -2.249973680 -1.3387502864
-#> 20  S020 B1 -1.327947035 -0.1475615007
-#> 21  S021 B1 -0.758965683 -1.8100006453
-#> 22  S022 B1 -0.888389832 -1.9324943042
-#> 23  S023 B1 -2.060091001  1.3516303589
-#> 24  S024 B1 -0.224279977  0.1194188927
-#> 25  S025 B1  0.306530384 -0.7553464950
-#> 26  S026 B1 -1.194083986 -0.0770005965
-#> 27  S027 B1 -0.034152252 -0.0883556681
-#> 28  S028 B1  0.571040255 -0.2196270385
-#> 29  S029 B1  0.978230906  0.8092446120
-#> 30  S030 B1 -0.838369867  0.6445582746
-#> 31  S031 B1  0.582223479  0.0718035273
-#> 32  S032 B1 -0.830228190 -0.1990091894
-#> 33  S033 B1  1.441069732 -0.0199050432
-#> 34  S034 B1  0.272081976  1.2717371014
-#> 35  S035 B1  0.669618980  0.2588167568
-#> 36  S036 B1  1.421458242  1.0827317549
-#> 37  S037 B1 -0.960650891  1.3436242474
-#> 38  S038 B1  0.633370765 -1.3521061224
-#> 39  S039 B1 -0.675763344  0.6589390744
-#> 40  S040 B1 -1.000097015  1.5140795970
-#> 41  S041 B1  0.501855463  1.4268040198
-#> 42  S042 B1  1.799742979  0.6929177828
-#> 43  S043 B1 -0.122429288  0.1027531773
-#> 44  S044 B1 -0.004124262  0.8437954531
-#> 45  S045 B1  2.292894116 -1.0977438379
-#> 46  S046 B1 -0.447379299  0.6058856205
-#> 47  S047 B1 -1.113314289  0.1804840574
-#> 48  S048 B1 -1.259792428  0.1373741345
-#> 49  S049 B1  1.312047455 -0.9384119167
-#> 50  S050 B1 -0.306531380  1.2208777319
-#> 51  S051 B1  1.901949305  2.5092085898
-#> 52  S052 B1  1.291079359  0.4090430570
-#> 53  S053 B1  0.145287246  1.1649998335
-#> 54  S054 B1  0.729037097 -1.8848579760
-#> 55  S055 B1  1.631088688  2.3481862203
-#> 56  S056 B1  1.389249855 -1.6011408027
-#> 57  S057 B1 -1.233701500  0.3732923093
-#> 58  S058 B1  2.236463446  2.4294791289
-#> 59  S059 B1 -0.273403071 -0.2322175650
-#> 60  S060 B1  0.347461121 -0.6047093827
-#> 61  S061 B1 -0.616986773  0.1946852816
-#> 62  S062 B1  0.059350026 -0.7440694919
-#> 63  S063 B1 -0.331595959  0.1971806734
-#> 64  S064 B1  0.913824889 -1.2601021120
-#> 65  S065 B1  1.942621031 -1.6193215266
-#> 66  S066 B1 -1.617594450 -0.3818115542
-#> 67  S067 B1  3.394357751  1.6095799923
-#> 68  S068 B1 -1.101087059  0.3673612333
-#> 69  S069 B1 -0.701072124 -2.0182989323
-#> 70  S070 B1 -0.477678049  0.0634809454
-#> 71  S071 B1 -1.411992488  0.0146489700
-#> 72  S072 B1 -0.304166644  0.7067924707
-#> 73  S073 B1  1.567349754 -1.9071859671
-#> 74  S074 B1  2.019994913 -0.7075477194
-#> 75  S075 B1 -0.170600904  0.1342983226
-#> 76  S076 B1 -1.607758803  1.3368682549
-#> 77  S077 B1  0.976335296  1.2747550458
-#> 78  S078 B1 -0.363419932 -0.2979366371
-#> 79  S079 B1 -0.320870258  0.6587456664
-#> 80  S080 B1 -0.143224468 -1.2880518298
-#> 81  S081 B1  1.456105216  0.1648196815
-#> 82  S082 B1  1.205638012  0.3205917968
-#> 83  S083 B1 -0.960804860 -0.2511991442
-#> 84  S084 B1 -0.200748839 -0.2818844386
-#> 85  S085 B1  1.054191010  0.0447665278
-#> 86  S086 B1 -0.741105960  0.1468067364
-#> 87  S087 B1 -0.112561403  1.0193438528
-#> 88  S088 B1 -0.393942235  0.7781726783
-#> 89  S089 B1  1.386486320 -0.3434223399
-#> 90  S090 B1  1.317375445  0.0005007825
-#> 91  S091 B1 -0.808622443  0.3809080236
-#> 92  S092 B1 -0.176767498 -0.2026552675
-#> 93  S093 B1 -0.396235963  0.0978633753
-#> 94  S094 B1 -0.137863714 -1.8803685371
-#> 95  S095 B1  0.611704091  1.2583368889
-#> 96  S096 B1 -0.132440716 -0.8033285466
-#> 97  S097 B1  0.998092023  0.9195714166
-#> 98  S098 B1  0.611799641 -1.0327346213
-#> 99  S099 B1  0.720432614 -1.4067310036
-#> 100 S100 B1  1.432083120 -2.2786092611
-#> 101 S101 B2  0.827832110  1.0217776770
-#> 102 S102 B2  1.201613541  1.5896830453
-#> 103 S103 B2 -0.215900624 -1.3490153808
-#> 104 S104 B2 -0.204605125  2.1844782749
-#> 105 S105 B2 -0.053534741  1.5268502490
-#> 106 S106 B2  0.846052696  1.4442267147
-#> 107 S107 B2  1.501333706 -2.4592434349
-#> 108 S108 B2  1.365915719  0.4506918741
-#> 109 S109 B2 -0.674457846  0.2985255422
-#> 110 S110 B2  1.686417255  0.1378628106
-#> 111 S111 B2 -0.036679356  0.6879922515
-#> 112 S112 B2 -0.352235435 -0.6859268337
-#> 113 S113 B2  1.847360738 -0.1069480851
-#> 114 S114 B2 -0.500159015  0.2379403087
-#> 115 S115 B2 -0.166739453 -1.1796278805
-#> 116 S116 B2 -1.013832333 -1.0882386316
-#> 117 S117 B2  1.674486641 -1.2036422842
-#> 118 S118 B2  1.320279206  0.5083470751
-#> 119 S119 B2 -0.362841486 -0.1902117127
-#> 120 S120 B2 -0.402005941  1.1211146105
-#> 121 S121 B2 -2.119173921  0.0399752026
-#> 122 S122 B2  0.507692461  0.8932877812
-#> 123 S123 B2  1.498141072 -0.6264716142
-#> 124 S124 B2 -0.634933624  1.6515041872
-#> 125 S125 B2 -0.104848980 -0.3740912072
-#> 126 S126 B2 -0.849374474 -0.3589459810
-#> 127 S127 B2 -1.779499489 -0.0212239686
-#> 128 S128 B2 -1.280840874 -0.0946037378
-#> 129 S129 B2  0.756611260 -0.6666520564
-#> 130 S130 B2  0.142295158  0.2248327688
-#> 131 S131 B2  0.480657221  0.2753382148
-#> 132 S132 B2 -1.011170453 -0.4985572139
-#> 133 S133 B2  0.984260416 -1.8631237810
-#> 134 S134 B2 -1.780679803  1.3139224758
-#> 135 S135 B2  0.687780279  0.6067097311
-#> 136 S136 B2  1.823299662  0.5491306196
-#> 137 S137 B2 -0.016812687 -2.0242251569
-#> 138 S138 B2 -0.095992757  0.2115254223
-#> 139 S139 B2  1.026208950 -0.2651370096
-#> 140 S140 B2 -0.388606162 -1.8398867564
-#> 141 S141 B2  0.867638252  0.6785481394
-#> 142 S142 B2 -0.393649326  0.3739040657
-#> 143 S143 B2  0.018159936  2.2795316706
-#> 144 S144 B2  0.233680002 -0.7883332609
-#> 145 S145 B2  1.626963606 -0.2532943799
-#> 146 S146 B2  0.316153881  0.4872212163
-#> 147 S147 B2  1.239951591  0.6268803721
-#> 148 S148 B2 -1.651883491  0.0027024945
-#> 149 S149 B2 -0.248851030 -0.6030287879
-#> 150 S150 B2  0.043992264  0.6984731653
-#> 151 S151 B2  0.529422896 -0.9674002389
-#> 152 S152 B2  0.590721157 -1.2173738927
-#> 153 S153 B2  0.735728888  0.4768882554
-#> 154 S154 B2 -1.827579072 -1.2837823449
-#> 155 S155 B2  0.352266817  0.2847971593
-#> 156 S156 B2 -1.968809083  0.5641358567
-#> 157 S157 B2  0.306473238  0.4578477178
-#> 158 S158 B2 -0.726380240  0.5778987515
-#> 159 S159 B2 -0.894885643 -0.4120557246
-#> 160 S160 B2  1.055975589  1.1773076655
-#> 161 S161 B2 -0.903981521 -0.6887394616
-#> 162 S162 B2 -1.228789742  0.5471972925
-#> 163 S163 B2 -1.152545674 -1.0564953585
-#> 164 S164 B2  0.031078645  1.4214451134
-#> 165 S165 B2 -0.578520706 -0.9972059786
-#> 166 S166 B2  1.436787928 -0.8160310631
-#> 167 S167 B2 -1.067211695 -1.0555395515
-#> 168 S168 B2  0.387615229  0.4094187765
-#> 169 S169 B2 -0.119479061  0.6372446443
-#> 170 S170 B2 -1.420763432 -1.0947420842
-#> 171 S171 B2 -0.515818057  2.0544470226
-#> 172 S172 B2 -0.935398492  1.0875582196
-#> 173 S173 B2 -0.028935338 -0.9425638706
-#> 174 S174 B2  0.286621432  0.3677656922
-#> 175 S175 B2  0.014645020  0.4588224279
-#> 176 S176 B2 -0.324573918 -1.3499839450
-#> 177 S177 B2 -1.021553670  1.1857234833
-#> 178 S178 B2 -1.462921829  0.0564806542
-#> 179 S179 B2  2.335112884  1.0255712949
-#> 180 S180 B2 -2.702861145  0.6133630407
-#> 181 S181 B2 -0.763880437  0.4255899750
-#> 182 S182 B2 -0.197631969 -0.4470380392
-#> 183 S183 B2 -1.179044408 -1.3948932569
-#> 184 S184 B2 -0.841051451  1.1204255598
-#> 185 S185 B2  0.088078345 -0.5052734504
-#> 186 S186 B2 -0.026999250 -1.2207567921
-#> 187 S187 B2  1.539674518 -1.2417427938
-#> 188 S188 B2 -0.955715150 -0.2117128372
-#> 189 S189 B2 -0.897473964 -0.0463989645
-#> 190 S190 B2 -0.386334128 -0.8268994274
-#> 191 S191 B2 -0.146336790 -1.4186609837
-#> 192 S192 B2 -0.826905687  0.0400629021
-#> 193 S193 B2 -1.248554588  1.5506109399
-#> 194 S194 B2  0.170841823 -0.4252312968
-#> 195 S195 B2 -0.831916258 -0.1471970154
-#> 196 S196 B2 -0.967443161  1.0607757778
-#> 197 S197 B2 -1.127598190 -0.6505442531
-#> 198 S198 B2  1.141745362 -0.1810296810
-#> 199 S199 B2  0.989802027 -2.1983265069
-#> 200 S200 B2  0.379263182  3.2366617843
      
+
      #>       id  B1          W1a          W1b
+#> 1   S001 B1a -0.561044856 -0.067743796
+#> 2   S002 B1a -0.955580037 -0.061628242
+#> 3   S003 B1a  0.039318227 -0.327068341
+#> 4   S004 B1a -1.537200399 -0.586337201
+#> 5   S005 B1a -0.503352702  0.080772347
+#> 6   S006 B1a  1.495785769 -0.081205745
+#> 7   S007 B1a -2.208285546  0.080741605
+#> 8   S008 B1a -0.637660519 -0.435609700
+#> 9   S009 B1a -0.605023743  1.129986579
+#> 10  S010 B1a -1.610085543 -0.106612308
+#> 11  S011 B1a  0.883714238 -0.437467138
+#> 12  S012 B1a -0.348751360 -1.418810778
+#> 13  S013 B1a  0.271425938 -1.273201588
+#> 14  S014 B1a  0.403532799  0.631287576
+#> 15  S015 B1a -1.026214387  0.350247495
+#> 16  S016 B1a  1.345134981 -0.543296497
+#> 17  S017 B1a -0.554977264  0.544729665
+#> 18  S018 B1a  0.349426667  0.264547171
+#> 19  S019 B1a  0.970020764 -1.694445781
+#> 20  S020 B1a -0.914753543  0.211148382
+#> 21  S021 B1a  0.594447679 -0.646201647
+#> 22  S022 B1a  1.131169405  0.886762842
+#> 23  S023 B1a  1.053350198  1.113846198
+#> 24  S024 B1a  2.964111438 -0.962914615
+#> 25  S025 B1a -0.220933309 -1.933753037
+#> 26  S026 B1a  1.117488796 -0.295290976
+#> 27  S027 B1a -1.503175421  0.505101561
+#> 28  S028 B1a  0.444605007  0.695229906
+#> 29  S029 B1a -1.176259379 -0.113347824
+#> 30  S030 B1a  0.902264764  1.834801450
+#> 31  S031 B1a  0.598462839 -1.559696894
+#> 32  S032 B1a -0.214258960 -0.459248424
+#> 33  S033 B1a -1.840855418 -0.555029580
+#> 34  S034 B1a  1.926654901  0.400128103
+#> 35  S035 B1a  0.494739020  1.395495312
+#> 36  S036 B1a  0.234367155 -0.195856160
+#> 37  S037 B1a -2.361566186 -0.139955727
+#> 38  S038 B1a  0.555965086 -1.296048005
+#> 39  S039 B1a  1.291934073 -0.959045774
+#> 40  S040 B1a -0.344258968  0.693449207
+#> 41  S041 B1a -1.360185885  1.128387059
+#> 42  S042 B1a -0.127969092 -0.561140257
+#> 43  S043 B1a  0.588768575  0.275048939
+#> 44  S044 B1a -0.355222322  0.155088061
+#> 45  S045 B1a -0.228973924  0.123818994
+#> 46  S046 B1a  0.235617779 -0.035139669
+#> 47  S047 B1a  1.223832613  0.569432079
+#> 48  S048 B1a  0.282154083  0.777783370
+#> 49  S049 B1a -0.224043654 -0.395218519
+#> 50  S050 B1a  0.571548737  0.322016771
+#> 51  S051 B1a -0.199729322 -0.772685742
+#> 52  S052 B1a  0.084945040 -1.044849154
+#> 53  S053 B1a -0.700166306  0.630794954
+#> 54  S054 B1a -1.281917853 -0.145344815
+#> 55  S055 B1a -1.019882660  0.713118611
+#> 56  S056 B1a  0.538725643  0.462901352
+#> 57  S057 B1a -0.462920492 -0.856131394
+#> 58  S058 B1a -0.332265059 -2.592398585
+#> 59  S059 B1a  1.556430905  0.888614643
+#> 60  S060 B1a  0.499134263 -0.005280646
+#> 61  S061 B1a -1.070937430 -0.928316581
+#> 62  S062 B1a -0.585242910 -1.264616879
+#> 63  S063 B1a -3.393653115 -0.356325787
+#> 64  S064 B1a  0.097221897 -0.702354491
+#> 65  S065 B1a -0.696467446 -1.111686569
+#> 66  S066 B1a -1.395599647 -0.629743461
+#> 67  S067 B1a -0.750289772 -0.546170384
+#> 68  S068 B1a  0.978366236  1.339658777
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+#> 71  S071 B1a  0.113891672 -1.629810776
+#> 72  S072 B1a  0.602333314 -0.732086231
+#> 73  S073 B1a -1.048859576  0.519499951
+#> 74  S074 B1a  0.124395876 -0.305188122
+#> 75  S075 B1a -0.514549458 -0.111255936
+#> 76  S076 B1a -0.498468885  0.320241668
+#> 77  S077 B1a  0.399589722 -0.802742742
+#> 78  S078 B1a -0.179220424  2.768944265
+#> 79  S079 B1a  0.324444454 -1.059423476
+#> 80  S080 B1a -1.312568161 -0.423068492
+#> 81  S081 B1a -0.718698267 -0.108480840
+#> 82  S082 B1a -1.343676655  0.652918513
+#> 83  S083 B1a  0.234152237  1.876700646
+#> 84  S084 B1a  0.197814391 -0.432864653
+#> 85  S085 B1a  0.263662997  0.273858033
+#> 86  S086 B1a  0.753537630 -1.644339360
+#> 87  S087 B1a -0.235015368 -0.336246671
+#> 88  S088 B1a -1.957455012 -0.010633320
+#> 89  S089 B1a -1.630344565  0.307734180
+#> 90  S090 B1a  0.243616710 -0.015988482
+#> 91  S091 B1a  0.252004977  0.499460470
+#> 92  S092 B1a  0.309686069 -0.603364283
+#> 93  S093 B1a -1.170226874  1.278243191
+#> 94  S094 B1a -0.230998088  0.361813666
+#> 95  S095 B1a  0.237706649  0.448465695
+#> 96  S096 B1a  0.021477686 -0.494731193
+#> 97  S097 B1a  1.650199536 -0.810799484
+#> 98  S098 B1a  0.289963195 -0.761809449
+#> 99  S099 B1a -0.440366645  0.828295779
+#> 100 S100 B1a -0.278318312  0.711687575
+#> 101 S101 B1b  0.905925849 -0.859828141
+#> 102 S102 B1b -0.933821185  0.919848057
+#> 103 S103 B1b -0.489236621 -0.565824768
+#> 104 S104 B1b  0.335391647 -1.041792692
+#> 105 S105 B1b  1.002756033 -0.124056402
+#> 106 S106 B1b -1.088923841  0.730815113
+#> 107 S107 B1b -0.397576234 -0.207696326
+#> 108 S108 B1b -1.003661428  0.072046360
+#> 109 S109 B1b -0.035319701 -0.527011953
+#> 110 S110 B1b  0.428891351 -0.389977885
+#> 111 S111 B1b  0.976788426 -1.039323978
+#> 112 S112 B1b  0.277220683  1.670893083
+#> 113 S113 B1b  0.589028701  0.386994643
+#> 114 S114 B1b -0.107724484  1.846751430
+#> 115 S115 B1b -1.490353743 -1.352247754
+#> 116 S116 B1b -1.196542456  1.205712340
+#> 117 S117 B1b  1.737516335  2.699706099
+#> 118 S118 B1b -1.047905546  0.019411344
+#> 119 S119 B1b  0.147858623  0.955414897
+#> 120 S120 B1b -0.361711738  0.087925908
+#> 121 S121 B1b  0.471737781 -0.211673532
+#> 122 S122 B1b -1.306261717 -0.162167440
+#> 123 S123 B1b  0.740614625  1.424081988
+#> 124 S124 B1b  0.264646364  0.851511382
+#> 125 S125 B1b -0.076394021 -0.276438463
+#> 126 S126 B1b -0.305043660 -0.728834249
+#> 127 S127 B1b -0.329298434  0.415327295
+#> 128 S128 B1b -0.008202456 -0.890512852
+#> 129 S129 B1b  0.829182081  0.527159198
+#> 130 S130 B1b -1.152251014 -0.233007500
+#> 131 S131 B1b  0.549441693 -0.132153815
+#> 132 S132 B1b -0.500263259  1.398360973
+#> 133 S133 B1b -0.691583165 -0.845290792
+#> 134 S134 B1b -0.909173317  1.324094781
+#> 135 S135 B1b  1.401614765 -0.632392864
+#> 136 S136 B1b  1.239768017 -0.298730658
+#> 137 S137 B1b -0.404602320  0.440929160
+#> 138 S138 B1b -0.605443885 -1.260691284
+#> 139 S139 B1b  0.665241949  0.579530319
+#> 140 S140 B1b -0.271291637 -1.860578205
+#> 141 S141 B1b  1.008558073  0.173750589
+#> 142 S142 B1b -0.726037972 -1.103425898
+#> 143 S143 B1b -0.725390484 -0.100468046
+#> 144 S144 B1b  1.037987233  0.301444296
+#> 145 S145 B1b -0.081066695 -1.704065962
+#> 146 S146 B1b  0.097899190 -0.885581452
+#> 147 S147 B1b -0.259936995 -0.260309270
+#> 148 S148 B1b  0.177857608  1.962835179
+#> 149 S149 B1b  0.008903474 -0.607698936
+#> 150 S150 B1b -0.248527828  0.299987317
+#> 151 S151 B1b  0.817020939 -0.144736191
+#> 152 S152 B1b  1.104046808 -0.166373505
+#> 153 S153 B1b  0.595241633 -0.124459316
+#> 154 S154 B1b -0.139628054  1.671253386
+#> 155 S155 B1b  0.263667489  0.206824918
+#> 156 S156 B1b -1.072606970 -0.279648587
+#> 157 S157 B1b  0.096526046  0.278634512
+#> 158 S158 B1b  0.660922507 -1.107440570
+#> 159 S159 B1b  0.323955131 -1.628652288
+#> 160 S160 B1b -1.239748780  0.002250468
+#> 161 S161 B1b  0.095789936  0.481704787
+#> 162 S162 B1b -0.596132459 -1.089634943
+#> 163 S163 B1b  1.399395747 -0.883646757
+#> 164 S164 B1b  0.182832295  0.594609965
+#> 165 S165 B1b -0.252475349 -0.788684759
+#> 166 S166 B1b  0.325117678 -0.932985118
+#> 167 S167 B1b -0.807716432  1.862734497
+#> 168 S168 B1b -1.355430028  0.465117083
+#> 169 S169 B1b -0.162677528 -0.854586388
+#> 170 S170 B1b -0.853893116 -0.437564600
+#> 171 S171 B1b  0.166592447 -0.718701275
+#> 172 S172 B1b  1.158059104 -0.484888395
+#> 173 S173 B1b -1.655377733 -0.271242524
+#> 174 S174 B1b -0.175625648  0.528316135
+#> 175 S175 B1b -0.536672734 -0.161209762
+#> 176 S176 B1b -0.507832362 -0.251623271
+#> 177 S177 B1b  0.032870219 -0.010398814
+#> 178 S178 B1b -0.201284274  0.332947951
+#> 179 S179 B1b -1.271830844  1.083528054
+#> 180 S180 B1b  0.421794630  0.142100613
+#> 181 S181 B1b -0.757559590  1.334435262
+#> 182 S182 B1b -0.439114165  0.842600292
+#> 183 S183 B1b  1.233383244 -0.551539196
+#> 184 S184 B1b -0.236755474  0.916787383
+#> 185 S185 B1b -0.311255364 -0.085723443
+#> 186 S186 B1b  0.230902599  1.511008766
+#> 187 S187 B1b -0.387382898  0.861649259
+#> 188 S188 B1b -1.445034511 -0.697742751
+#> 189 S189 B1b -2.343398745  1.481552313
+#> 190 S190 B1b  0.177141129 -2.211246252
+#> 191 S191 B1b  0.458774227 -0.889326238
+#> 192 S192 B1b -1.479368394 -0.163205684
+#> 193 S193 B1b -0.107714666 -1.155713860
+#> 194 S194 B1b -0.697122390 -0.373243181
+#> 195 S195 B1b  0.625983395 -1.405725195
+#> 196 S196 B1b  0.662072731 -0.671037529
+#> 197 S197 B1b -0.231464090  1.669532484
+#> 198 S198 B1b  1.051310015  0.862072669
+#> 199 S199 B1b  1.532396909  0.442988792
+#> 200 S200 B1b -0.022095753  0.840473380
      @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/message.html b/docs/reference/message.html index c3fd9cac..391c90b1 100644 --- a/docs/reference/message.html +++ b/docs/reference/message.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/messy.html b/docs/reference/messy.html index b401e1c3..91d57501 100644 --- a/docs/reference/messy.html +++ b/docs/reference/messy.html @@ -45,11 +45,13 @@ + + - + @@ -85,7 +87,7 @@ faux - 1.0.0 + 1.1.0 @@ -125,12 +127,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -208,8 +216,8 @@

      Examp #> 9 4.4 2.9 1.4 0.2 setosa #> 10 4.9 3.1 1.5 0.1 setosa #> 11 5.4 3.7 1.5 0.2 setosa -#> 12 4.8 3.4 1.6 0.2 setosa -#> 13 4.8 3.0 1.4 0.1 setosa +#> 12 4.8 3.4 1.6 0.2 NO SPECIES +#> 13 4.8 3.0 1.4 0.1 NO SPECIES #> 14 4.3 3.0 1.1 0.1 setosa #> 15 5.8 4.0 1.2 0.2 setosa #> 16 5.7 4.4 1.5 0.4 setosa @@ -219,23 +227,23 @@

      Examp #> 20 5.1 3.8 1.5 0.3 setosa #> 21 5.4 3.4 1.7 0.2 setosa #> 22 5.1 3.7 1.5 0.4 setosa -#> 23 4.6 3.6 1.0 0.2 NO SPECIES +#> 23 4.6 3.6 1.0 0.2 setosa #> 24 5.1 3.3 1.7 0.5 setosa #> 25 4.8 3.4 1.9 0.2 setosa -#> 26 5.0 3.0 1.6 0.2 NO SPECIES +#> 26 5.0 3.0 1.6 0.2 setosa #> 27 5.0 3.4 1.6 0.4 setosa -#> 28 5.2 3.5 1.5 0.2 NO SPECIES +#> 28 5.2 3.5 1.5 0.2 setosa #> 29 5.2 3.4 1.4 0.2 setosa #> 30 4.7 3.2 1.6 0.2 setosa #> 31 4.8 3.1 1.6 0.2 setosa -#> 32 5.4 3.4 1.5 0.4 setosa -#> 33 5.2 4.1 1.5 0.1 NO SPECIES +#> 32 5.4 3.4 1.5 0.4 NO SPECIES +#> 33 5.2 4.1 1.5 0.1 setosa #> 34 5.5 4.2 1.4 0.2 setosa #> 35 4.9 3.1 1.5 0.2 setosa #> 36 5.0 3.2 1.2 0.2 setosa #> 37 5.5 3.5 1.3 0.2 setosa #> 38 4.9 3.6 1.4 0.1 setosa -#> 39 4.4 3.0 1.3 0.2 setosa +#> 39 4.4 3.0 1.3 0.2 NO SPECIES #> 40 5.1 3.4 1.5 0.2 setosa #> 41 5.0 3.5 1.3 0.3 setosa #> 42 4.5 2.3 1.3 0.3 setosa @@ -243,7 +251,7 @@

      Examp #> 44 5.0 3.5 1.6 0.6 setosa #> 45 5.1 3.8 1.9 0.4 setosa #> 46 4.8 3.0 1.4 0.3 setosa -#> 47 5.1 3.8 1.6 0.2 NO SPECIES +#> 47 5.1 3.8 1.6 0.2 setosa #> 48 4.6 3.2 1.4 0.2 setosa #> 49 5.3 3.7 1.5 0.2 setosa #> 50 5.0 3.3 1.4 0.2 setosa @@ -252,9 +260,9 @@

      Examp #> 53 6.9 3.1 4.9 1.5 versicolor #> 54 5.5 2.3 4.0 1.3 versicolor #> 55 6.5 2.8 4.6 1.5 NO SPECIES -#> 56 5.7 2.8 4.5 1.3 NO SPECIES -#> 57 6.3 3.3 4.7 1.6 NO SPECIES -#> 58 4.9 2.4 3.3 1.0 versicolor +#> 56 5.7 2.8 4.5 1.3 versicolor +#> 57 6.3 3.3 4.7 1.6 versicolor +#> 58 4.9 2.4 3.3 1.0 NO SPECIES #> 59 6.6 2.9 4.6 1.3 versicolor #> 60 5.2 2.7 3.9 1.4 versicolor #> 61 5.0 2.0 3.5 1.0 versicolor @@ -266,7 +274,7 @@

      Examp #> 67 5.6 3.0 4.5 1.5 versicolor #> 68 5.8 2.7 4.1 1.0 versicolor #> 69 6.2 2.2 4.5 1.5 versicolor -#> 70 5.6 2.5 3.9 1.1 versicolor +#> 70 5.6 2.5 3.9 1.1 NO SPECIES #> 71 5.9 3.2 4.8 1.8 versicolor #> 72 6.1 2.8 4.0 1.3 versicolor #> 73 6.3 2.5 4.9 1.5 versicolor @@ -283,7 +291,7 @@

      Examp #> 84 6.0 2.7 5.1 1.6 versicolor #> 85 5.4 3.0 4.5 1.5 versicolor #> 86 6.0 3.4 4.5 1.6 versicolor -#> 87 6.7 3.1 4.7 1.5 versicolor +#> 87 6.7 3.1 4.7 1.5 NO SPECIES #> 88 6.3 2.3 4.4 1.3 versicolor #> 89 5.6 3.0 4.1 1.3 versicolor #> 90 5.5 2.5 4.0 1.3 versicolor @@ -291,36 +299,36 @@

      Examp #> 92 6.1 3.0 4.6 1.4 versicolor #> 93 5.8 2.6 4.0 1.2 versicolor #> 94 5.0 2.3 3.3 1.0 versicolor -#> 95 5.6 2.7 4.2 1.3 NO SPECIES -#> 96 5.7 3.0 4.2 1.2 NO SPECIES +#> 95 5.6 2.7 4.2 1.3 versicolor +#> 96 5.7 3.0 4.2 1.2 versicolor #> 97 5.7 2.9 4.2 1.3 versicolor #> 98 6.2 2.9 4.3 1.3 versicolor #> 99 5.1 2.5 3.0 1.1 versicolor #> 100 5.7 2.8 4.1 1.3 NO SPECIES -#> 101 6.3 3.3 6.0 2.5 virginica +#> 101 6.3 3.3 6.0 2.5 NO SPECIES #> 102 5.8 2.7 5.1 1.9 virginica #> 103 7.1 3.0 5.9 2.1 virginica #> 104 6.3 2.9 5.6 1.8 virginica #> 105 6.5 3.0 5.8 2.2 virginica -#> 106 7.6 3.0 6.6 2.1 virginica +#> 106 7.6 3.0 6.6 2.1 NO SPECIES #> 107 4.9 2.5 4.5 1.7 virginica #> 108 7.3 2.9 6.3 1.8 virginica #> 109 6.7 2.5 5.8 1.8 virginica -#> 110 7.2 3.6 6.1 2.5 virginica +#> 110 7.2 3.6 6.1 2.5 NO SPECIES #> 111 6.5 3.2 5.1 2.0 virginica #> 112 6.4 2.7 5.3 1.9 virginica -#> 113 6.8 3.0 5.5 2.1 virginica +#> 113 6.8 3.0 5.5 2.1 NO SPECIES #> 114 5.7 2.5 5.0 2.0 virginica #> 115 5.8 2.8 5.1 2.4 virginica #> 116 6.4 3.2 5.3 2.3 virginica #> 117 6.5 3.0 5.5 1.8 virginica #> 118 7.7 3.8 6.7 2.2 virginica -#> 119 7.7 2.6 6.9 2.3 NO SPECIES +#> 119 7.7 2.6 6.9 2.3 virginica #> 120 6.0 2.2 5.0 1.5 virginica #> 121 6.9 3.2 5.7 2.3 virginica #> 122 5.6 2.8 4.9 2.0 virginica #> 123 7.7 2.8 6.7 2.0 virginica -#> 124 6.3 2.7 4.9 1.8 NO SPECIES +#> 124 6.3 2.7 4.9 1.8 virginica #> 125 6.7 3.3 5.7 2.1 virginica #> 126 7.2 3.2 6.0 1.8 virginica #> 127 6.2 2.8 4.8 1.8 virginica @@ -340,164 +348,164 @@

      Examp #> 141 6.7 3.1 5.6 2.4 virginica #> 142 6.9 3.1 5.1 2.3 virginica #> 143 5.8 2.7 5.1 1.9 virginica -#> 144 6.8 3.2 5.9 2.3 NO SPECIES +#> 144 6.8 3.2 5.9 2.3 virginica #> 145 6.7 3.3 5.7 2.5 virginica #> 146 6.7 3.0 5.2 2.3 virginica #> 147 6.3 2.5 5.0 1.9 virginica -#> 148 6.5 3.0 5.2 2.0 virginica +#> 148 6.5 3.0 5.2 2.0 NO SPECIES #> 149 6.2 3.4 5.4 2.3 virginica #> 150 5.9 3.0 5.1 1.8 virginica
      messy(iris, 0.5, 1:4)
      #> Sepal.Length Sepal.Width Petal.Length Petal.Width Species -#> 1 5.1 NA 1.4 NA setosa -#> 2 NA 3.0 NA NA setosa -#> 3 4.7 NA NA NA setosa -#> 4 4.6 3.1 NA 0.2 setosa +#> 1 NA NA NA NA setosa +#> 2 4.9 3.0 NA 0.2 setosa +#> 3 NA NA NA 0.2 setosa +#> 4 NA 3.1 NA NA setosa #> 5 5.0 3.6 NA 0.2 setosa -#> 6 5.4 NA 1.7 NA setosa -#> 7 4.6 3.4 NA 0.3 setosa -#> 8 5.0 3.4 1.5 0.2 setosa -#> 9 NA 2.9 NA NA setosa -#> 10 4.9 NA NA 0.1 setosa -#> 11 NA NA 1.5 NA setosa -#> 12 4.8 3.4 1.6 NA setosa -#> 13 NA 3.0 NA NA setosa -#> 14 NA 3.0 NA 0.1 setosa -#> 15 5.8 4.0 1.2 NA setosa -#> 16 NA NA NA NA setosa -#> 17 NA 3.9 NA NA setosa -#> 18 NA 3.5 NA NA setosa +#> 6 5.4 3.9 1.7 0.4 setosa +#> 7 4.6 NA NA NA setosa +#> 8 NA 3.4 NA 0.2 setosa +#> 9 NA 2.9 NA 0.2 setosa +#> 10 4.9 3.1 NA NA setosa +#> 11 NA NA 1.5 0.2 setosa +#> 12 NA NA NA 0.2 setosa +#> 13 NA 3.0 1.4 NA setosa +#> 14 4.3 NA NA NA setosa +#> 15 5.8 NA 1.2 0.2 setosa +#> 16 5.7 4.4 NA 0.4 setosa +#> 17 5.4 3.9 1.3 0.4 setosa +#> 18 NA 3.5 1.4 0.3 setosa #> 19 NA NA NA 0.3 setosa -#> 20 NA NA 1.5 0.3 setosa -#> 21 NA NA 1.7 0.2 setosa -#> 22 NA NA 1.5 NA setosa -#> 23 4.6 3.6 NA 0.2 setosa -#> 24 5.1 3.3 1.7 NA setosa -#> 25 NA 3.4 NA 0.2 setosa +#> 20 5.1 NA NA 0.3 setosa +#> 21 5.4 3.4 NA NA setosa +#> 22 NA NA NA 0.4 setosa +#> 23 NA NA 1.0 NA setosa +#> 24 NA NA NA 0.5 setosa +#> 25 NA 3.4 1.9 NA setosa #> 26 NA NA 1.6 NA setosa -#> 27 NA 3.4 1.6 NA setosa -#> 28 5.2 NA NA NA setosa -#> 29 5.2 NA NA 0.2 setosa -#> 30 4.7 NA 1.6 0.2 setosa -#> 31 NA 3.1 NA 0.2 setosa -#> 32 5.4 3.4 NA 0.4 setosa -#> 33 5.2 4.1 1.5 NA setosa -#> 34 5.5 4.2 NA NA setosa -#> 35 NA NA 1.5 0.2 setosa -#> 36 NA NA NA 0.2 setosa -#> 37 5.5 3.5 1.3 0.2 setosa -#> 38 4.9 NA NA 0.1 setosa -#> 39 4.4 NA NA NA setosa -#> 40 5.1 3.4 NA NA setosa -#> 41 NA 3.5 1.3 0.3 setosa +#> 27 5.0 NA NA NA setosa +#> 28 5.2 3.5 NA 0.2 setosa +#> 29 NA 3.4 1.4 0.2 setosa +#> 30 4.7 3.2 NA 0.2 setosa +#> 31 4.8 NA 1.6 NA setosa +#> 32 NA NA 1.5 0.4 setosa +#> 33 5.2 NA 1.5 NA setosa +#> 34 NA 4.2 NA NA setosa +#> 35 NA 3.1 1.5 NA setosa +#> 36 5.0 NA NA 0.2 setosa +#> 37 5.5 3.5 1.3 NA setosa +#> 38 NA 3.6 NA NA setosa +#> 39 NA NA 1.3 NA setosa +#> 40 5.1 NA 1.5 0.2 setosa +#> 41 5.0 3.5 NA 0.3 setosa #> 42 NA 2.3 1.3 0.3 setosa -#> 43 4.4 NA NA 0.2 setosa -#> 44 5.0 3.5 NA NA setosa +#> 43 4.4 3.2 1.3 NA setosa +#> 44 5.0 NA 1.6 0.6 setosa #> 45 NA 3.8 NA NA setosa -#> 46 4.8 NA NA NA setosa -#> 47 NA 3.8 NA NA setosa -#> 48 4.6 3.2 NA 0.2 setosa -#> 49 5.3 NA NA 0.2 setosa +#> 46 NA 3.0 NA NA setosa +#> 47 NA NA 1.6 0.2 setosa +#> 48 4.6 NA NA NA setosa +#> 49 NA 3.7 1.5 0.2 setosa #> 50 5.0 NA 1.4 NA setosa -#> 51 NA 3.2 NA NA versicolor -#> 52 NA NA NA NA versicolor -#> 53 6.9 3.1 NA NA versicolor -#> 54 5.5 2.3 NA NA versicolor -#> 55 NA 2.8 4.6 NA versicolor -#> 56 NA NA NA 1.3 versicolor -#> 57 6.3 NA NA 1.6 versicolor -#> 58 NA NA 3.3 1.0 versicolor -#> 59 6.6 NA NA NA versicolor -#> 60 NA NA 3.9 1.4 versicolor -#> 61 NA 2.0 3.5 1.0 versicolor +#> 51 7.0 NA 4.7 NA versicolor +#> 52 6.4 NA 4.5 NA versicolor +#> 53 6.9 3.1 4.9 1.5 versicolor +#> 54 NA 2.3 NA 1.3 versicolor +#> 55 6.5 NA 4.6 1.5 versicolor +#> 56 5.7 NA NA NA versicolor +#> 57 6.3 3.3 NA NA versicolor +#> 58 NA 2.4 3.3 NA versicolor +#> 59 6.6 2.9 NA 1.3 versicolor +#> 60 5.2 NA 3.9 NA versicolor +#> 61 NA NA 3.5 1.0 versicolor #> 62 5.9 NA 4.2 1.5 versicolor -#> 63 NA 2.2 4.0 NA versicolor -#> 64 NA 2.9 NA 1.4 versicolor -#> 65 NA NA NA 1.3 versicolor -#> 66 NA NA 4.4 NA versicolor -#> 67 NA 3.0 4.5 1.5 versicolor -#> 68 NA 2.7 4.1 NA versicolor -#> 69 NA 2.2 NA 1.5 versicolor -#> 70 5.6 2.5 NA 1.1 versicolor -#> 71 NA 3.2 4.8 NA versicolor -#> 72 NA 2.8 NA 1.3 versicolor -#> 73 NA 2.5 NA 1.5 versicolor -#> 74 6.1 NA 4.7 NA versicolor -#> 75 6.4 2.9 4.3 1.3 versicolor -#> 76 6.6 3.0 4.4 1.4 versicolor -#> 77 NA 2.8 NA 1.4 versicolor -#> 78 NA 3.0 NA 1.7 versicolor -#> 79 6.0 2.9 4.5 1.5 versicolor -#> 80 5.7 NA NA NA versicolor -#> 81 5.5 2.4 NA 1.1 versicolor -#> 82 5.5 NA 3.7 1.0 versicolor +#> 63 6.0 NA NA 1.0 versicolor +#> 64 6.1 NA NA 1.4 versicolor +#> 65 NA 2.9 3.6 NA versicolor +#> 66 NA NA NA NA versicolor +#> 67 NA NA NA NA versicolor +#> 68 5.8 2.7 4.1 NA versicolor +#> 69 6.2 NA 4.5 NA versicolor +#> 70 NA NA NA 1.1 versicolor +#> 71 NA NA NA NA versicolor +#> 72 NA 2.8 NA NA versicolor +#> 73 NA NA NA 1.5 versicolor +#> 74 NA NA 4.7 NA versicolor +#> 75 6.4 NA 4.3 NA versicolor +#> 76 6.6 NA NA 1.4 versicolor +#> 77 6.8 2.8 NA 1.4 versicolor +#> 78 6.7 NA NA 1.7 versicolor +#> 79 NA 2.9 4.5 1.5 versicolor +#> 80 5.7 NA NA 1.0 versicolor +#> 81 NA NA NA NA versicolor +#> 82 NA NA 3.7 NA versicolor #> 83 5.8 NA NA NA versicolor -#> 84 NA NA NA NA versicolor -#> 85 NA NA NA 1.5 versicolor -#> 86 6.0 3.4 NA NA versicolor -#> 87 NA NA 4.7 NA versicolor -#> 88 6.3 2.3 NA NA versicolor -#> 89 NA NA NA NA versicolor -#> 90 5.5 2.5 4.0 NA versicolor -#> 91 NA 2.6 4.4 NA versicolor -#> 92 6.1 3.0 4.6 NA versicolor -#> 93 5.8 NA NA NA versicolor -#> 94 NA 2.3 3.3 NA versicolor -#> 95 5.6 2.7 4.2 1.3 versicolor +#> 84 6.0 2.7 5.1 NA versicolor +#> 85 5.4 NA NA NA versicolor +#> 86 NA 3.4 NA NA versicolor +#> 87 6.7 NA 4.7 1.5 versicolor +#> 88 6.3 2.3 4.4 NA versicolor +#> 89 5.6 3.0 4.1 1.3 versicolor +#> 90 NA 2.5 NA NA versicolor +#> 91 NA 2.6 4.4 1.2 versicolor +#> 92 6.1 3.0 NA 1.4 versicolor +#> 93 5.8 2.6 4.0 1.2 versicolor +#> 94 NA NA 3.3 NA versicolor +#> 95 NA 2.7 NA NA versicolor #> 96 NA 3.0 4.2 1.2 versicolor -#> 97 5.7 2.9 NA NA versicolor -#> 98 6.2 NA 4.3 NA versicolor -#> 99 NA NA NA 1.1 versicolor -#> 100 NA NA 4.1 NA versicolor -#> 101 NA 3.3 6.0 2.5 virginica -#> 102 5.8 NA NA NA virginica -#> 103 NA NA 5.9 2.1 virginica -#> 104 NA NA NA 1.8 virginica -#> 105 NA NA NA 2.2 virginica -#> 106 NA NA 6.6 2.1 virginica +#> 97 NA 2.9 4.2 1.3 versicolor +#> 98 NA NA NA 1.3 versicolor +#> 99 5.1 NA 3.0 NA versicolor +#> 100 NA NA 4.1 1.3 versicolor +#> 101 6.3 NA NA 2.5 virginica +#> 102 5.8 2.7 5.1 NA virginica +#> 103 NA 3.0 NA NA virginica +#> 104 6.3 2.9 5.6 NA virginica +#> 105 6.5 3.0 NA 2.2 virginica +#> 106 NA 3.0 NA 2.1 virginica #> 107 4.9 2.5 4.5 1.7 virginica -#> 108 NA NA 6.3 NA virginica -#> 109 NA NA 5.8 1.8 virginica -#> 110 NA NA 6.1 NA virginica -#> 111 6.5 NA 5.1 NA virginica -#> 112 6.4 2.7 5.3 1.9 virginica -#> 113 6.8 3.0 5.5 2.1 virginica -#> 114 NA NA NA 2.0 virginica -#> 115 5.8 2.8 5.1 NA virginica +#> 108 NA NA 6.3 1.8 virginica +#> 109 NA 2.5 NA NA virginica +#> 110 NA 3.6 6.1 2.5 virginica +#> 111 NA 3.2 5.1 NA virginica +#> 112 NA 2.7 5.3 NA virginica +#> 113 NA NA 5.5 NA virginica +#> 114 5.7 2.5 NA NA virginica +#> 115 NA NA 5.1 2.4 virginica #> 116 NA NA NA NA virginica -#> 117 6.5 3.0 5.5 NA virginica -#> 118 7.7 3.8 6.7 NA virginica +#> 117 6.5 3.0 5.5 1.8 virginica +#> 118 NA NA 6.7 2.2 virginica #> 119 7.7 NA 6.9 NA virginica -#> 120 NA NA 5.0 1.5 virginica -#> 121 6.9 NA NA NA virginica -#> 122 5.6 2.8 4.9 2.0 virginica -#> 123 7.7 2.8 6.7 2.0 virginica -#> 124 6.3 2.7 4.9 1.8 virginica +#> 120 6.0 NA NA 1.5 virginica +#> 121 NA 3.2 5.7 NA virginica +#> 122 5.6 2.8 NA NA virginica +#> 123 NA NA NA NA virginica +#> 124 NA 2.7 NA 1.8 virginica #> 125 NA 3.3 NA NA virginica -#> 126 7.2 3.2 NA NA virginica -#> 127 6.2 2.8 4.8 NA virginica -#> 128 NA NA NA NA virginica -#> 129 NA NA NA NA virginica -#> 130 7.2 NA 5.8 1.6 virginica -#> 131 NA 2.8 NA 1.9 virginica -#> 132 NA NA NA 2.0 virginica -#> 133 NA 2.8 5.6 NA virginica -#> 134 6.3 NA 5.1 1.5 virginica -#> 135 6.1 NA 5.6 1.4 virginica -#> 136 NA 3.0 6.1 2.3 virginica -#> 137 NA NA NA NA virginica -#> 138 6.4 NA 5.5 1.8 virginica -#> 139 NA NA 4.8 1.8 virginica -#> 140 6.9 NA NA 2.1 virginica -#> 141 6.7 3.1 5.6 2.4 virginica -#> 142 NA 3.1 NA NA virginica -#> 143 NA NA NA 1.9 virginica -#> 144 NA NA 5.9 NA virginica -#> 145 6.7 NA NA NA virginica -#> 146 6.7 NA 5.2 NA virginica -#> 147 6.3 NA 5.0 NA virginica -#> 148 6.5 NA 5.2 NA virginica -#> 149 NA NA 5.4 2.3 virginica -#> 150 NA NA NA 1.8 virginica
      +#> 126 NA NA NA NA virginica +#> 127 6.2 NA 4.8 NA virginica +#> 128 NA 3.0 4.9 1.8 virginica +#> 129 NA NA NA 2.1 virginica +#> 130 7.2 NA 5.8 NA virginica +#> 131 NA NA 6.1 NA virginica +#> 132 NA NA 6.4 2.0 virginica +#> 133 NA NA NA 2.2 virginica +#> 134 6.3 2.8 NA NA virginica +#> 135 6.1 2.6 5.6 1.4 virginica +#> 136 NA 3.0 NA NA virginica +#> 137 NA 3.4 NA NA virginica +#> 138 6.4 3.1 NA 1.8 virginica +#> 139 6.0 3.0 4.8 1.8 virginica +#> 140 6.9 NA 5.4 2.1 virginica +#> 141 6.7 NA NA 2.4 virginica +#> 142 6.9 NA NA NA virginica +#> 143 NA 2.7 5.1 1.9 virginica +#> 144 6.8 3.2 NA NA virginica +#> 145 NA 3.3 5.7 2.5 virginica +#> 146 6.7 3.0 5.2 NA virginica +#> 147 NA NA NA 1.9 virginica +#> 148 6.5 3.0 NA NA virginica +#> 149 6.2 NA 5.4 2.3 virginica +#> 150 5.9 NA 5.1 NA virginica @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • @@ -201,7 +209,7 @@

      Examp f = "single item vector", g = list() ) -nested_list(x) +nested_list(x)
      #> * a: #> * a1: Named #> * a2: List diff --git a/docs/reference/norm2beta-1.png b/docs/reference/norm2beta-1.png index dff08791..5272d900 100644 Binary files a/docs/reference/norm2beta-1.png and b/docs/reference/norm2beta-1.png differ diff --git a/docs/reference/norm2beta.html b/docs/reference/norm2beta.html index 06db4626..29e679fb 100644 --- a/docs/reference/norm2beta.html +++ b/docs/reference/norm2beta.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0
      @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • diff --git a/docs/reference/norm2binom-1.png b/docs/reference/norm2binom-1.png index 74798c06..128599fa 100644 Binary files a/docs/reference/norm2binom-1.png and b/docs/reference/norm2binom-1.png differ diff --git a/docs/reference/norm2binom.html b/docs/reference/norm2binom.html index c4659148..67a69320 100644 --- a/docs/reference/norm2binom.html +++ b/docs/reference/norm2binom.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/norm2gamma-1.png b/docs/reference/norm2gamma-1.png index 0d496d67..c3700695 100644 Binary files a/docs/reference/norm2gamma-1.png and b/docs/reference/norm2gamma-1.png differ diff --git a/docs/reference/norm2gamma.html b/docs/reference/norm2gamma.html index 6a996e97..378ca3cb 100644 --- a/docs/reference/norm2gamma.html +++ b/docs/reference/norm2gamma.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/norm2likert-1.png b/docs/reference/norm2likert-1.png index 0ba9693e..82246862 100644 Binary files a/docs/reference/norm2likert-1.png and b/docs/reference/norm2likert-1.png differ diff --git a/docs/reference/norm2likert.html b/docs/reference/norm2likert.html index d0908a9c..b201b504 100644 --- a/docs/reference/norm2likert.html +++ b/docs/reference/norm2likert.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/norm2pois-1.png b/docs/reference/norm2pois-1.png index 95ad1df3..391e955d 100644 Binary files a/docs/reference/norm2pois-1.png and b/docs/reference/norm2pois-1.png differ diff --git a/docs/reference/norm2pois.html b/docs/reference/norm2pois.html index f23d396d..b40113bb 100644 --- a/docs/reference/norm2pois.html +++ b/docs/reference/norm2pois.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/norm2trunc-1.png b/docs/reference/norm2trunc-1.png index 3de025db..995230a7 100644 Binary files a/docs/reference/norm2trunc-1.png and b/docs/reference/norm2trunc-1.png differ diff --git a/docs/reference/norm2trunc.html b/docs/reference/norm2trunc.html index be78ecee..bac1a6a1 100644 --- a/docs/reference/norm2trunc.html +++ b/docs/reference/norm2trunc.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/norm2unif-1.png b/docs/reference/norm2unif-1.png index bd8f9cd8..b0ca6fc2 100644 Binary files a/docs/reference/norm2unif-1.png and b/docs/reference/norm2unif-1.png differ diff --git a/docs/reference/norm2unif.html b/docs/reference/norm2unif.html index 6283f5e0..00f450ce 100644 --- a/docs/reference/norm2unif.html +++ b/docs/reference/norm2unif.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 9e9f107d..754df4b5 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/plot_design-2.png b/docs/reference/plot_design-2.png index 6d640715..e1191790 100644 Binary files a/docs/reference/plot_design-2.png and b/docs/reference/plot_design-2.png differ diff --git a/docs/reference/plot_design.html b/docs/reference/plot_design.html index f16958fd..e2728ce7 100644 --- a/docs/reference/plot_design.html +++ b/docs/reference/plot_design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -165,7 +173,7 @@

      Plot design

      Plots the specified within and between design. See vignette("plots", package = "faux") for examples and details.

      - 
      plot_design(x, ..., geoms = NULL, palette = "Dark2")
+    
      plot_design(x, ..., geoms = NULL, palette = "Dark2", labeller = "label_value")
 
 # S3 method for design
 plot(x, ...)
@@ -182,7 +190,7 @@ 
      Arg
     
     
       ...
-      
      A list of factor names to determine visualisation (see vignette)
      
+      
      A list of factor names to determine visualisation (see vignette) in the order color, x, facet row(s), facet col(s)
      
     
     
       geoms
@@ -190,7 +198,11 @@ 
      Arg
     
     
       palette
-      
      A brewer palette, defaults to "Dark2"
      
+      
      A brewer palette, defaults to "Dark2" (see ggplot2::scale_colour_brewer)
      
+    
+    
+      labeller
+      
      How to label the facets (see ggplot2::facet_grid). "label_value" is used by default.
      
     
     
 
diff --git a/docs/reference/pos_def_limits.html b/docs/reference/pos_def_limits.html
index 5d320b1a..5345d9db 100644
--- a/docs/reference/pos_def_limits.html
+++ b/docs/reference/pos_def_limits.html
@@ -45,10 +45,12 @@
 
 
 
+  
+  
 
 
 
-
+
 
 
 
@@ -84,7 +86,7 @@
       
       
         faux
-        1.0.0
+        1.1.0
       
     
 
@@ -124,12 +126,18 @@
     
    • 
       Mixed Design Simulation
     
      • 
+    
    • 
+      Randomised Reports
+    
      • 
     
    • 
       Distribution Conversions
     
      • 
     
    • 
       Codebook Demo
     
      • 
+    
    • 
+      Contrasts
+    
      •
    • diff --git a/docs/reference/print.design.html b/docs/reference/print.design.html index 9c5f43be..68646344 100644 --- a/docs/reference/print.design.html +++ b/docs/reference/print.design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/print.nested_list.html b/docs/reference/print.nested_list.html index 51280764..ac788372 100644 --- a/docs/reference/print.nested_list.html +++ b/docs/reference/print.nested_list.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/print.psychds_codebook.html b/docs/reference/print.psychds_codebook.html index b4835ba0..6d1ed8d2 100644 --- a/docs/reference/print.psychds_codebook.html +++ b/docs/reference/print.psychds_codebook.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/readline_check.html b/docs/reference/readline_check.html index 1c2c917b..6e02bd08 100644 --- a/docs/reference/readline_check.html +++ b/docs/reference/readline_check.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/rnorm_multi.html b/docs/reference/rnorm_multi.html index 03332e7e..cf0b1b9c 100644 --- a/docs/reference/rnorm_multi.html +++ b/docs/reference/rnorm_multi.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -225,26 +233,26 @@

      Value

      Examples

      # 4 10-item vectors each correlated r = .5
 rnorm_multi(10, 4, r = 0.5)
-
      #>             X1         X2          X3          X4
-#> 1   0.45739527  0.8994046 -0.96948437  0.03884443
-#> 2   0.22708900  1.1959369  1.45527042  2.29606358
-#> 3  -0.06813446 -0.2700352  0.01537602 -0.26163959
-#> 4  -0.57440414 -1.7155499 -1.20389427 -1.50454873
-#> 5  -0.70123083 -1.2190659  0.21736094 -0.40004893
-#> 6   1.48536823  1.6847372  1.00499651  0.20763006
-#> 7  -0.34937463  0.1976291 -0.75879876  0.09282251
-#> 8  -1.15331203 -0.3250870 -0.29305162 -2.21427244
-#> 9   0.07559776  0.6463190  0.91047591  0.78581872
-#> 10 -0.67463688  1.2230990 -0.62793740 -0.34783835
      
+
      #>             X1          X2         X3          X4
+#> 1   0.61095292 -1.26859860 -1.0473425 -0.93833481
+#> 2   1.00786700 -0.01045278  0.8011351  0.07257914
+#> 3   0.82667232  1.04795522  0.4644770 -0.28091812
+#> 4  -0.05394007 -1.44029737 -0.6811783 -0.30994736
+#> 5  -1.41605036 -1.64280302 -0.4406393 -1.01325211
+#> 6   0.46223117  2.18896422  1.5182443  0.46917235
+#> 7   0.78039022  0.39089482 -0.2903739 -0.13676273
+#> 8   0.63844483  0.07108052  0.5376613 -0.53491812
+#> 9   0.72890749 -1.52370263  0.8834245  0.55849515
+#> 10 -0.04290407 -0.28051289 -0.5307138  1.53489728
      
 # set r with the upper right triangle
 b <- rnorm_multi(100, 3, c(0, .5, 1), 1, 
                  r = c(0.2, -0.5, 0.5), 
                  varnames=c("A", "B", "C"))
 cor(b)
-
      #>           A         B          C
-#> A  1.000000 0.4183250 -0.2670150
-#> B  0.418325 1.0000000  0.4560207
-#> C -0.267015 0.4560207  1.0000000
      
+
      #>            A         B          C
+#> A  1.0000000 0.3994863 -0.5147966
+#> B  0.3994863 1.0000000  0.3896981
+#> C -0.5147966 0.3896981  1.0000000
      
 # set r with a correlation matrix and column names from mu names
 c <- rnorm_multi(
   n = 100, 
@@ -255,9 +263,9 @@ 
      Examp
 )
 cor(c)
 
      #>            A         B          C
-#> A  1.0000000 0.1866261 -0.6052581
-#> B  0.1866261 1.0000000  0.3732939
-#> C -0.6052581 0.3732939  1.0000000
      
+#> A  1.0000000 0.1615419 -0.5165878
+#> B  0.1615419 1.0000000  0.5451974
+#> C -0.5165878 0.5451974  1.0000000
      @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -204,14 +212,14 @@

      Examp 
      v1 <- rnorm(10)
 v2 <- rnorm_pre(v1, 0, 1, 0.5)
 cor(v1, v2)
-
      #> [1] 0.320861
      
+
      #> [1] 0.7712965
      
 x <- rnorm_multi(50, 2, .5)
 x$y <- rnorm_pre(x, r = c(0.5, 0.25))
 cor(x)
 
      #>            X1         X2         y
-#> X1  1.0000000 -0.2845362 0.4339307
-#> X2 -0.2845362  1.0000000 0.1814306
-#> y   0.4339307  0.1814306 1.0000000
      +#> X1 1.00000000 0.01447404 0.5566679 +#> X2 0.01447404 1.00000000 0.2687557 +#> y 0.55666790 0.26875572 1.0000000 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • @@ -195,13 +203,13 @@

      Value

      Examples

      sample_from_pop(10)
 
      #> $mu
-#> [1] 0.4999385
+#> [1] 0.1417399
 #> 
 #> $sd
-#> [1] 0.9422441
+#> [1] 1.131078
 #> 
 #> $r
-#> [1] 0.2655221
+#> [1] -0.3914457
 #>
      @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -165,7 +173,14 @@

      Simulate data from design (internal)

      Simulate data from design (internal)

      - 
      sim_data(design, empirical = FALSE, long = FALSE, rep = 1, seed = NULL)
      + 
      sim_data(
+  design,
+  empirical = FALSE,
+  long = FALSE,
+  rep = 1,
+  nested = TRUE,
+  seed = NULL
+)

      Arguments

      @@ -186,6 +201,10 @@

      Arg

      + + + + diff --git a/docs/reference/sim_design.html b/docs/reference/sim_design.html index fa1e2bc9..096f37a8 100644 --- a/docs/reference/sim_design.html +++ b/docs/reference/sim_design.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -162,7 +170,7 @@

      Simulate data from design

      -

      Generates a data table with a specified within and between design. See vignette("sim_design", package = "faux") for examples and details.

      +

      Generates a data table with a specified within and between design. See vignette("sim_design", package = "faux") for examples and details.

      sim_design(
@@ -176,10 +184,12 @@ 
      Simulate data from design
      
   long = FALSE,
   dv = list(y = "value"),
   id = list(id = "id"),
+  vardesc = list(),
   plot = faux_options("plot"),
   interactive = FALSE,
   design = NULL,
   rep = 1,
+  nested = TRUE,
   seed = NULL,
   sep = faux_options("sep")
 )
      @@ -209,7 +219,7 @@

      Arg

      • - + @@ -227,6 +237,10 @@

      Arg

      + + + + @@ -237,11 +251,15 @@

      Arg

      - + - + + + + + diff --git a/docs/reference/sim_df.html b/docs/reference/sim_df.html index de096931..195e083b 100644 --- a/docs/reference/sim_df.html +++ b/docs/reference/sim_df.html @@ -45,11 +45,13 @@ + + - + @@ -85,7 +87,7 @@ faux - 1.0.0 + 1.1.0 @@ -125,12 +127,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/sim_joint_dist.html b/docs/reference/sim_joint_dist.html index 9e43de38..db6cf8e2 100644 --- a/docs/reference/sim_joint_dist.html +++ b/docs/reference/sim_joint_dist.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -194,19 +202,19 @@

      Value

      Examples

      sim_joint_dist(ggplot2::diamonds, cut, color, n = 10)
-
      #> # A tibble: 10 x 2
+
      #> # A tibble: 10 x 2
 #>    cut       color
-#>    <ord>     <ord>
-#>  1 Ideal     E    
-#>  2 Very Good I    
-#>  3 Ideal     F    
-#>  4 Very Good H    
-#>  5 Very Good I    
-#>  6 Ideal     G    
-#>  7 Ideal     H    
-#>  8 Premium   J    
-#>  9 Ideal     F    
-#> 10 Ideal     E
      +#> <ord> <ord> +#> 1 Ideal D +#> 2 Very Good H +#> 3 Ideal F +#> 4 Ideal H +#> 5 Premium D +#> 6 Premium E +#> 7 Premium E +#> 8 Ideal G +#> 9 Premium E +#> 10 Ideal I @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -220,107 +228,107 @@

      Value

      Examples

      
 sim_mixed_cc(10, 10)
-
      #>     sub_id item_id           y grand_i        sub_i      item_i          err
-#> 1      S01     I01  0.89827035       0  1.048229287 -0.52749074  0.377531800
-#> 15     S01     I02  2.47217957       0  1.048229287  1.31276628  0.111183999
-#> 29     S01     I03  1.05929178       0  1.048229287 -0.17919260  0.190255093
-#> 31     S01     I04  2.15097086       0  1.048229287  0.08841686  1.014324708
-#> 44     S01     I05  0.56495004       0  1.048229287 -0.89309805  0.409818809
-#> 58     S01     I06  1.95621377       0  1.048229287  0.41655671  0.491427768
-#> 61     S01     I07  0.87490660       0  1.048229287  0.21130684 -0.384629521
-#> 73     S01     I08  3.03999440       0  1.048229287  0.01882627  1.972938844
-#> 87     S01     I09  1.75630202       0  1.048229287  0.89066536 -0.182592624
-#> 91     S01     I10 -2.66525581       0  1.048229287 -1.30699432 -2.406490772
-#> 7      S02     I01 -0.13923821       0  1.154815105 -0.52749074 -0.766562581
-#> 17     S02     I02  1.42489922       0  1.154815105  1.31276628 -1.042682165
-#> 30     S02     I03  0.63421037       0  1.154815105 -0.17919260 -0.341412133
-#> 36     S02     I04  1.51462496       0  1.154815105  0.08841686  0.271392995
-#> 46     S02     I05  0.96941465       0  1.154815105 -0.89309805  0.707697596
-#> 59     S02     I06  2.23799364       0  1.154815105  0.41655671  0.666621818
-#> 69     S02     I07  0.68983159       0  1.154815105  0.21130684 -0.676290351
-#> 75     S02     I08  1.42758730       0  1.154815105  0.01882627  0.253945923
-#> 88     S02     I09  2.22227749       0  1.154815105  0.89066536  0.176797026
-#> 98     S02     I10  1.65926688       0  1.154815105 -1.30699432  1.811446100
-#> 4      S03     I01 -1.41759580       0 -0.033129970 -0.52749074 -0.856975100
-#> 18     S03     I02  0.95539174       0 -0.033129970  1.31276628 -0.324244567
-#> 23     S03     I03 -0.41709532       0 -0.033129970 -0.17919260 -0.204772748
-#> 33     S03     I04 -1.92889127       0 -0.033129970  0.08841686 -1.984178161
-#> 47     S03     I05 -0.18995863       0 -0.033129970 -0.89309805  0.736269389
-#> 60     S03     I06  1.56481856       0 -0.033129970  0.41655671  1.181391813
-#> 62     S03     I07  0.73548655       0 -0.033129970  0.21130684  0.557309683
-#> 76     S03     I08 -1.49415277       0 -0.033129970  0.01882627 -1.479849077
-#> 89     S03     I09  1.66076221       0 -0.033129970  0.89066536  0.803226825
-#> 95     S03     I10 -1.50938413       0 -0.033129970 -1.30699432 -0.169259840
-#> 5      S04     I01 -0.14256186       0  0.083385100 -0.52749074  0.301543775
-#> 19     S04     I02  0.94031877       0  0.083385100  1.31276628 -0.455832607
-#> 21     S04     I03 -1.69108481       0  0.083385100 -0.17919260 -1.595277311
-#> 34     S04     I04 -2.24301539       0  0.083385100  0.08841686 -2.414817352
-#> 48     S04     I05 -2.75963581       0  0.083385100 -0.89309805 -1.949922857
-#> 51     S04     I06  0.60833909       0  0.083385100  0.41655671  0.108397276
-#> 63     S04     I07  0.94168449       0  0.083385100  0.21130684  0.646992551
-#> 77     S04     I08 -1.57671213       0  0.083385100  0.01882627 -1.678923499
-#> 90     S04     I09  1.50857829       0  0.083385100  0.89066536  0.534527830
-#> 92     S04     I10 -1.83683229       0  0.083385100 -1.30699432 -0.613223062
-#> 2      S05     I01 -2.64653523       0 -2.275104427 -0.52749074  0.156059929
-#> 16     S05     I02 -0.83811458       0 -2.275104427  1.31276628  0.124223563
-#> 22     S05     I03 -2.39899884       0 -2.275104427 -0.17919260  0.055298184
-#> 35     S05     I04 -0.96313400       0 -2.275104427  0.08841686  1.223553567
-#> 45     S05     I05 -3.26866748       0 -2.275104427 -0.89309805 -0.100464996
-#> 52     S05     I06 -0.31017921       0 -2.275104427  0.41655671  1.548368507
-#> 64     S05     I07 -1.22899982       0 -2.275104427  0.21130684  0.834797766
-#> 74     S05     I08 -3.18863932       0 -2.275104427  0.01882627 -0.932361163
-#> 81     S05     I09 -1.13500449       0 -2.275104427  0.89066536  0.249434583
-#> 93     S05     I10 -4.05573998       0 -2.275104427 -1.30699432 -0.473641226
-#> 8      S06     I01 -0.02984267       0  1.246547000 -0.52749074 -0.748898930
-#> 20     S06     I02  3.12396856       0  1.246547000  1.31276628  0.564655280
-#> 27     S06     I03  1.98674858       0  1.246547000 -0.17919260  0.919394185
-#> 37     S06     I04  1.32638050       0  1.246547000  0.08841686 -0.008583359
-#> 49     S06     I05  1.87239055       0  1.246547000 -0.89309805  1.518941602
-#> 56     S06     I06  0.95842246       0  1.246547000  0.41655671 -0.704681259
-#> 66     S06     I07  1.48125405       0  1.246547000  0.21130684  0.023400212
-#> 79     S06     I08  2.41723359       0  1.246547000  0.01882627  1.151860320
-#> 85     S06     I09  1.51485686       0  1.246547000  0.89066536 -0.622355502
-#> 99     S06     I10 -0.10136916       0  1.246547000 -1.30699432 -0.040921834
-#> 9      S07     I01 -0.83750414       0  0.006142866 -0.52749074 -0.316156270
-#> 11     S07     I02  0.11777263       0  0.006142866  1.31276628 -1.201136514
-#> 24     S07     I03 -2.05910492       0  0.006142866 -0.17919260 -1.886055183
-#> 38     S07     I04 -0.99545727       0  0.006142866  0.08841686 -1.090017001
-#> 50     S07     I05 -0.97913941       0  0.006142866 -0.89309805 -0.092184224
-#> 53     S07     I06  1.46091601       0  0.006142866  0.41655671  1.038216432
-#> 67     S07     I07  0.87899112       0  0.006142866  0.21130684  0.661541419
-#> 80     S07     I08  0.90819538       0  0.006142866  0.01882627  0.883226238
-#> 82     S07     I09  0.72309500       0  0.006142866  0.89066536 -0.173713227
-#> 96     S07     I10 -1.00720629       0  0.006142866 -1.30699432  0.293645170
-#> 6      S08     I01 -0.40259237       0 -0.059806157 -0.52749074  0.184704522
-#> 12     S08     I02  0.96079823       0 -0.059806157  1.31276628 -0.292161891
-#> 25     S08     I03 -0.97600570       0 -0.059806157 -0.17919260 -0.737006942
-#> 39     S08     I04 -0.20732179       0 -0.059806157  0.08841686 -0.235932500
-#> 41     S08     I05 -0.15902016       0 -0.059806157 -0.89309805  0.793884053
-#> 54     S08     I06  0.39902196       0 -0.059806157  0.41655671  0.042271403
-#> 68     S08     I07  2.03180820       0 -0.059806157  0.21130684  1.880307518
-#> 78     S08     I08  0.95222401       0 -0.059806157  0.01882627  0.993203894
-#> 83     S08     I09  0.33756329       0 -0.059806157  0.89066536 -0.493295910
-#> 97     S08     I10 -2.65417971       0 -0.059806157 -1.30699432 -1.287379227
-#> 3      S09     I01  0.30722379       0  1.501903884 -0.52749074 -0.667189357
-#> 13     S09     I02  2.62403210       0  1.501903884  1.31276628 -0.190638062
-#> 26     S09     I03  0.46762070       0  1.501903884 -0.17919260 -0.855090584
-#> 32     S09     I04 -0.48025629       0  1.501903884  0.08841686 -2.070577042
-#> 42     S09     I05  0.13182078       0  1.501903884 -0.89309805 -0.476985050
-#> 55     S09     I06  2.15578084       0  1.501903884  0.41655671  0.237320245
-#> 65     S09     I07  0.76725645       0  1.501903884  0.21130684 -0.945954269
-#> 71     S09     I08  1.79184922       0  1.501903884  0.01882627  0.271119065
-#> 84     S09     I09  2.55216550       0  1.501903884  0.89066536  0.159596260
-#> 94     S09     I10  1.09434151       0  1.501903884 -1.30699432  0.899431949
-#> 10     S10     I01  0.60173217       0  0.830053002 -0.52749074  0.299169903
-#> 14     S10     I02  1.70552946       0  0.830053002  1.31276628 -0.437289821
-#> 28     S10     I03 -1.07579437       0  0.830053002 -0.17919260 -1.726654769
-#> 40     S10     I04  0.43708661       0  0.830053002  0.08841686 -0.481383257
-#> 43     S10     I05 -0.13807019       0  0.830053002 -0.89309805 -0.075025139
-#> 57     S10     I06  1.69410135       0  0.830053002  0.41655671  0.447491634
-#> 70     S10     I07  2.23529582       0  0.830053002  0.21130684  1.193935978
-#> 72     S10     I08  0.68539020       0  0.830053002  0.01882627 -0.163489074
-#> 86     S10     I09  1.65446208       0  0.830053002  0.89066536 -0.066256281
-#> 100    S10     I10 -0.68229054       0  0.830053002 -1.30699432 -0.205349219
      +
      #> sub_id item_id y grand_i sub_i item_i err +#> 1 S01 I01 -3.251555640 0 -1.0933128 -2.2805280 0.12228514 +#> 15 S01 I02 -1.851005204 0 -1.0933128 -1.0255685 0.26787615 +#> 29 S01 I03 -1.390906708 0 -1.0933128 -0.5102049 0.21261102 +#> 31 S01 I04 -1.617827203 0 -1.0933128 -0.3906218 -0.13389262 +#> 44 S01 I05 -0.306424397 0 -1.0933128 0.2110268 0.57586158 +#> 58 S01 I06 -1.192460606 0 -1.0933128 -0.1854311 0.08628335 +#> 61 S01 I07 -4.025276824 0 -1.0933128 -2.1994373 -0.73252676 +#> 73 S01 I08 -0.343640393 0 -1.0933128 -0.1822684 0.93194080 +#> 87 S01 I09 -2.429076596 0 -1.0933128 0.1812328 -1.51699658 +#> 91 S01 I10 0.944345854 0 -1.0933128 1.2116043 0.82605439 +#> 7 S02 I01 -0.773433000 0 1.2269399 -2.2805280 0.28015505 +#> 17 S02 I02 1.532269630 0 1.2269399 -1.0255685 1.33089825 +#> 30 S02 I03 1.631832238 0 1.2269399 -0.5102049 0.91509724 +#> 36 S02 I04 1.944916820 0 1.2269399 -0.3906218 1.10859867 +#> 46 S02 I05 3.134732788 0 1.2269399 0.2110268 1.69676604 +#> 59 S02 I06 -1.486422043 0 1.2269399 -0.1854311 -2.52793082 +#> 69 S02 I07 0.163382637 0 1.2269399 -2.1994373 1.13587997 +#> 75 S02 I08 2.060254032 0 1.2269399 -0.1822684 1.01558250 +#> 88 S02 I09 1.215851593 0 1.2269399 0.1812328 -0.19232112 +#> 98 S02 I10 2.324614560 0 1.2269399 1.2116043 -0.11392964 +#> 4 S03 I01 -0.721495163 0 -0.1957095 -2.2805280 1.75474230 +#> 18 S03 I02 -2.151125422 0 -0.1957095 -1.0255685 -0.92984739 +#> 23 S03 I03 -1.336808367 0 -0.1957095 -0.5102049 -0.63089395 +#> 33 S03 I04 -0.003187314 0 -0.1957095 -0.3906218 0.58314395 +#> 47 S03 I05 0.859461960 0 -0.1957095 0.2110268 0.84414462 +#> 60 S03 I06 -0.662850779 0 -0.1957095 -0.1854311 -0.28171014 +#> 62 S03 I07 -0.642091135 0 -0.1957095 -2.1994373 1.75305561 +#> 76 S03 I08 -0.771932591 0 -0.1957095 -0.1822684 -0.39395471 +#> 89 S03 I09 -1.053161622 0 -0.1957095 0.1812328 -1.03868492 +#> 95 S03 I10 2.074628263 0 -0.1957095 1.2116043 1.05873348 +#> 5 S04 I01 -0.230073329 0 1.4196822 -2.2805280 0.63077248 +#> 19 S04 I02 1.271973590 0 1.4196822 -1.0255685 0.87785997 +#> 21 S04 I03 0.893478213 0 1.4196822 -0.5102049 -0.01599903 +#> 34 S04 I04 0.521050191 0 1.4196822 -0.3906218 -0.50801020 +#> 48 S04 I05 2.728176842 0 1.4196822 0.2110268 1.09746785 +#> 51 S04 I06 0.252287855 0 1.4196822 -0.1854311 -0.98196316 +#> 63 S04 I07 -1.702166936 0 1.4196822 -2.1994373 -0.92241184 +#> 77 S04 I08 1.210144474 0 1.4196822 -0.1822684 -0.02726930 +#> 90 S04 I09 1.863358807 0 1.4196822 0.1812328 0.26244385 +#> 92 S04 I10 1.398374433 0 1.4196822 1.2116043 -1.23291200 +#> 2 S05 I01 -3.068392143 0 -0.9949279 -2.2805280 0.20706376 +#> 16 S05 I02 -2.736549670 0 -0.9949279 -1.0255685 -0.71605320 +#> 22 S05 I03 -2.069097882 0 -0.9949279 -0.5102049 -0.56396503 +#> 35 S05 I04 0.015326650 0 -0.9949279 -0.3906218 1.40087635 +#> 45 S05 I05 -1.735127692 0 -0.9949279 0.2110268 -0.95122659 +#> 52 S05 I06 -2.490137013 0 -0.9949279 -0.1854311 -1.30977793 +#> 64 S05 I07 -2.763086707 0 -0.9949279 -2.1994373 0.43127848 +#> 74 S05 I08 -1.353850058 0 -0.9949279 -0.1822684 -0.17665374 +#> 81 S05 I09 -0.171143537 0 -0.9949279 0.1812328 0.64255160 +#> 93 S05 I10 0.381921817 0 -0.9949279 1.2116043 0.16524548 +#> 8 S06 I01 -2.661141564 0 -0.5320612 -2.2805280 0.15144762 +#> 20 S06 I02 -0.910032228 0 -0.5320612 -1.0255685 0.64759753 +#> 27 S06 I03 -1.762424505 0 -0.5320612 -0.5102049 -0.72015837 +#> 37 S06 I04 0.003285302 0 -0.5320612 -0.3906218 0.92596829 +#> 49 S06 I05 -0.164212061 0 -0.5320612 0.2110268 0.15682232 +#> 56 S06 I06 0.335041751 0 -0.5320612 -0.1854311 1.05253411 +#> 66 S06 I07 -2.510777609 0 -0.5320612 -2.1994373 0.22072086 +#> 79 S06 I08 -3.403794678 0 -0.5320612 -0.1822684 -2.68946508 +#> 85 S06 I09 -0.754394318 0 -0.5320612 0.1812328 -0.40356590 +#> 99 S06 I10 1.029510980 0 -0.5320612 1.2116043 0.34996792 +#> 9 S07 I01 -2.593790416 0 -0.4006895 -2.2805280 0.08742706 +#> 11 S07 I02 -0.304845036 0 -0.4006895 -1.0255685 1.12141301 +#> 24 S07 I03 0.619818589 0 -0.4006895 -0.5102049 1.53071301 +#> 38 S07 I04 -2.480214929 0 -0.4006895 -0.3906218 -1.68890365 +#> 50 S07 I05 -1.479370173 0 -0.4006895 0.2110268 -1.28970750 +#> 53 S07 I06 0.515777993 0 -0.4006895 -0.1854311 1.10189864 +#> 67 S07 I07 -3.143974999 0 -0.4006895 -2.1994373 -0.54384824 +#> 80 S07 I08 -0.407739052 0 -0.4006895 -0.1822684 0.17521884 +#> 82 S07 I09 -0.155472015 0 -0.4006895 0.1812328 0.06398470 +#> 96 S07 I10 0.485831518 0 -0.4006895 1.2116043 -0.32508325 +#> 6 S08 I01 -0.935827145 0 0.4811886 -2.2805280 0.86351220 +#> 12 S08 I02 -0.649548158 0 0.4811886 -1.0255685 -0.10516825 +#> 25 S08 I03 -0.086549245 0 0.4811886 -0.5102049 -0.05753295 +#> 39 S08 I04 1.453692551 0 0.4811886 -0.3906218 1.36312569 +#> 41 S08 I05 2.225776944 0 0.4811886 0.2110268 1.53356148 +#> 54 S08 I06 0.019908610 0 0.4811886 -0.1854311 -0.27584887 +#> 68 S08 I07 -1.520430713 0 0.4811886 -2.1994373 0.19781791 +#> 78 S08 I08 0.855809028 0 0.4811886 -0.1822684 0.55688879 +#> 83 S08 I09 0.841050376 0 0.4811886 0.1812328 0.17862895 +#> 97 S08 I10 0.853546501 0 0.4811886 1.2116043 -0.83924640 +#> 3 S09 I01 -1.036473841 0 1.3491860 -2.2805280 -0.10513183 +#> 13 S09 I02 0.552036698 0 1.3491860 -1.0255685 0.22841928 +#> 26 S09 I03 0.790027261 0 1.3491860 -0.5102049 -0.04895378 +#> 32 S09 I04 1.264072873 0 1.3491860 -0.3906218 0.30550868 +#> 42 S09 I05 0.547848354 0 1.3491860 0.2110268 -1.01236444 +#> 55 S09 I06 1.399430774 0 1.3491860 -0.1854311 0.23567596 +#> 65 S09 I07 0.305820358 0 1.3491860 -2.1994373 1.15607166 +#> 71 S09 I08 0.619861481 0 1.3491860 -0.1822684 -0.54705609 +#> 84 S09 I09 1.983678167 0 1.3491860 0.1812328 0.45325941 +#> 94 S09 I10 4.128354187 0 1.3491860 1.2116043 1.56756395 +#> 10 S10 I01 -2.163925260 0 0.3304327 -2.2805280 -0.21382994 +#> 14 S10 I02 -0.521211412 0 0.3304327 -1.0255685 0.17392447 +#> 28 S10 I03 -0.675178314 0 0.3304327 -0.5102049 -0.49540605 +#> 40 S10 I04 -0.180856589 0 0.3304327 -0.3906218 -0.12066747 +#> 43 S10 I05 0.097784937 0 0.3304327 0.2110268 -0.44367455 +#> 57 S10 I06 1.341901847 0 0.3304327 -0.1854311 1.19690034 +#> 70 S10 I07 -1.388569829 0 0.3304327 -2.1994373 0.48043477 +#> 72 S10 I08 -2.191840031 0 0.3304327 -0.1822684 -2.34000430 +#> 86 S10 I09 0.913213917 0 0.3304327 0.1812328 0.40154847 +#> 100 S10 I10 2.065417563 0 0.3304327 1.2116043 0.52338063
      @@ -125,12 +127,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -211,107 +219,107 @@

      Value

      Examples

      sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")
-
      #>     sub_id item_id         y  grand_i       sub_i      item_i         err
-#> 1      S01     I01 6.3646902 3.018906  1.48120549  1.19133200  0.67324705
-#> 15     S01     I02 4.8794404 3.018906  1.48120549  0.14091305  0.23841620
-#> 29     S01     I03 2.8839789 3.018906  1.48120549  0.06485664 -1.68098887
-#> 31     S01     I04 2.1128697 3.018906  1.48120549 -0.42635348 -1.96088794
-#> 44     S01     I05 6.7938000 3.018906  1.48120549  1.12736729  1.16632159
-#> 58     S01     I06 5.6298822 3.018906  1.48120549  1.10087374  0.02889740
-#> 61     S01     I07 5.0537244 3.018906  1.48120549 -0.29432480  0.84793813
-#> 73     S01     I08 4.9907879 3.018906  1.48120549  0.66207708 -0.17140028
-#> 87     S01     I09 5.1491113 3.018906  1.48120549 -0.06026755  0.70926774
-#> 91     S01     I10 5.0168336 3.018906  1.48120549  0.35539173  0.16133080
-#> 7      S02     I01 2.8445029 3.018906  0.14436494  1.19133200 -1.51009968
-#> 17     S02     I02 2.8657735 3.018906  0.14436494  0.14091305 -0.43841007
-#> 30     S02     I03 1.1268162 3.018906  0.14436494  0.06485664 -2.10131101
-#> 36     S02     I04 3.6991602 3.018906  0.14436494 -0.42635348  0.96224313
-#> 46     S02     I05 4.7060601 3.018906  0.14436494  1.12736729  0.41542222
-#> 59     S02     I06 4.7605146 3.018906  0.14436494  1.10087374  0.49637031
-#> 69     S02     I07 3.1690952 3.018906  0.14436494 -0.29432480  0.30014947
-#> 75     S02     I08 5.0680639 3.018906  0.14436494  0.66207708  1.24271622
-#> 88     S02     I09 3.2088190 3.018906  0.14436494 -0.06026755  0.10581605
-#> 98     S02     I10 3.8651679 3.018906  0.14436494  0.35539173  0.34650563
-#> 4      S03     I01 2.9498962 3.018906  0.10403004  1.19133200 -1.36437142
-#> 18     S03     I02 3.6717427 3.018906  0.10403004  0.14091305  0.40789402
-#> 23     S03     I03 5.2679292 3.018906  0.10403004  0.06485664  2.08013692
-#> 33     S03     I04 2.4907094 3.018906  0.10403004 -0.42635348 -0.20587274
-#> 47     S03     I05 4.0098255 3.018906  0.10403004  1.12736729 -0.24047743
-#> 60     S03     I06 5.7845975 3.018906  0.10403004  1.10087374  1.56078814
-#> 62     S03     I07 4.2883106 3.018906  0.10403004 -0.29432480  1.45969972
-#> 76     S03     I08 2.3639133 3.018906  0.10403004  0.66207708 -1.42109948
-#> 89     S03     I09 2.4388567 3.018906  0.10403004 -0.06026755 -0.62381139
-#> 95     S03     I10 3.5708095 3.018906  0.10403004  0.35539173  0.09248213
-#> 5      S04     I01 3.3127892 3.018906 -0.72126824  1.19133200 -0.17618017
-#> 19     S04     I02 2.1062678 3.018906 -0.72126824  0.14091305 -0.33228260
-#> 21     S04     I03 2.6418236 3.018906 -0.72126824  0.06485664  0.27932960
-#> 34     S04     I04 0.5971781 3.018906 -0.72126824 -0.42635348 -1.27410579
-#> 48     S04     I05 2.2582697 3.018906 -0.72126824  1.12736729 -1.16673493
-#> 51     S04     I06 4.7742557 3.018906 -0.72126824  1.10087374  1.37574462
-#> 63     S04     I07 1.7274217 3.018906 -0.72126824 -0.29432480 -0.27589082
-#> 77     S04     I08 3.0309383 3.018906 -0.72126824  0.66207708  0.07122382
-#> 90     S04     I09 1.7091782 3.018906 -0.72126824 -0.06026755 -0.52819164
-#> 92     S04     I10 1.0661287 3.018906 -0.72126824  0.35539173 -1.58690035
-#> 2      S05     I01 4.7306599 3.018906 -0.09666313  1.19133200  0.61708542
-#> 16     S05     I02 1.8091947 3.018906 -0.09666313  0.14091305 -1.25396080
-#> 22     S05     I03 2.3451278 3.018906 -0.09666313  0.06485664 -0.64197132
-#> 35     S05     I04 2.3940620 3.018906 -0.09666313 -0.42635348 -0.10182698
-#> 45     S05     I05 4.5573163 3.018906 -0.09666313  1.12736729  0.50770650
-#> 52     S05     I06 3.9619034 3.018906 -0.09666313  1.10087374 -0.06121280
-#> 64     S05     I07 3.0178613 3.018906 -0.09666313 -0.29432480  0.38994363
-#> 74     S05     I08 4.9287422 3.018906 -0.09666313  0.66207708  1.34442260
-#> 81     S05     I09 2.0351907 3.018906 -0.09666313 -0.06026755 -0.82678427
-#> 93     S05     I10 4.0721195 3.018906 -0.09666313  0.35539173  0.79448526
-#> 8      S06     I01 8.5191887 3.018906  0.27574486  1.19133200  4.03320622
-#> 20     S06     I02 4.5425418 3.018906  0.27574486  0.14091305  1.10697827
-#> 27     S06     I03 5.3935785 3.018906  0.27574486  0.06485664  2.03407141
-#> 37     S06     I04 2.5990446 3.018906  0.27574486 -0.42635348 -0.26925242
-#> 49     S06     I05 6.1673410 3.018906  0.27574486  1.12736729  1.74532329
-#> 56     S06     I06 2.9396400 3.018906  0.27574486  1.10087374 -1.45588420
-#> 66     S06     I07 3.5754168 3.018906  0.27574486 -0.29432480  0.57509110
-#> 79     S06     I08 5.9921398 3.018906  0.27574486  0.66207708  2.03541222
-#> 85     S06     I09 3.4217117 3.018906  0.27574486 -0.06026755  0.18732875
-#> 99     S06     I10 3.7023838 3.018906  0.27574486  0.35539173  0.05234157
-#> 9      S07     I01 4.8096390 3.018906  1.66441057  1.19133200 -1.06500917
-#> 11     S07     I02 5.0047442 3.018906  1.66441057  0.14091305  0.18051494
-#> 24     S07     I03 5.2533366 3.018906  1.66441057  0.06485664  0.50516381
-#> 38     S07     I04 5.8410663 3.018906  1.66441057 -0.42635348  1.58410357
-#> 50     S07     I05 6.3028370 3.018906  1.66441057  1.12736729  0.49215349
-#> 53     S07     I06 4.3370986 3.018906  1.66441057  1.10087374 -1.44709132
-#> 67     S07     I07 5.3543573 3.018906  1.66441057 -0.29432480  0.96536587
-#> 80     S07     I08 6.7314783 3.018906  1.66441057  0.66207708  1.38608500
-#> 82     S07     I09 4.1947866 3.018906  1.66441057 -0.06026755 -0.42826206
-#> 96     S07     I10 4.0377783 3.018906  1.66441057  0.35539173 -1.00092957
-#> 6      S08     I01 3.1992472 3.018906 -0.64871111  1.19133200 -0.36227934
-#> 12     S08     I02 2.3541248 3.018906 -0.64871111  0.14091305 -0.15698273
-#> 25     S08     I03 3.5737760 3.018906 -0.64871111  0.06485664  1.13872487
-#> 39     S08     I04 2.4713875 3.018906 -0.64871111 -0.42635348  0.52754647
-#> 41     S08     I05 3.8748313 3.018906 -0.64871111  1.12736729  0.37726954
-#> 54     S08     I06 3.2499115 3.018906 -0.64871111  1.10087374 -0.22115670
-#> 68     S08     I07 1.7740479 3.018906 -0.64871111 -0.29432480 -0.30182180
-#> 78     S08     I08 4.5566479 3.018906 -0.64871111  0.66207708  1.52437635
-#> 83     S08     I09 1.0542787 3.018906 -0.64871111 -0.06026755 -1.25564825
-#> 97     S08     I10 3.2486453 3.018906 -0.64871111  0.35539173  0.52305908
-#> 3      S09     I01 7.0228290 3.018906  0.76380851  1.19133200  2.04878290
-#> 13     S09     I02 3.9522030 3.018906  0.76380851  0.14091305  0.02857582
-#> 26     S09     I03 1.7458393 3.018906  0.76380851  0.06485664 -2.10173145
-#> 32     S09     I04 4.7553102 3.018906  0.76380851 -0.42635348  1.39894955
-#> 42     S09     I05 6.6840208 3.018906  0.76380851  1.12736729  1.77393934
-#> 55     S09     I06 6.3029382 3.018906  0.76380851  1.10087374  1.41935035
-#> 65     S09     I07 3.0003536 3.018906  0.76380851 -0.29432480 -0.48803575
-#> 71     S09     I08 5.5210688 3.018906  0.76380851  0.66207708  1.07627757
-#> 84     S09     I09 3.3168697 3.018906  0.76380851 -0.06026755 -0.40557691
-#> 94     S09     I10 3.9206237 3.018906  0.76380851  0.35539173 -0.21748212
-#> 10     S10     I01 5.3804615 3.018906  0.39888947  1.19133200  0.77133444
-#> 14     S10     I02 2.5611011 3.018906  0.39888947  0.14091305 -0.99760699
-#> 28     S10     I03 3.1531022 3.018906  0.39888947  0.06485664 -0.32954948
-#> 40     S10     I04 2.8808465 3.018906  0.39888947 -0.42635348 -0.11059510
-#> 43     S10     I05 2.8139884 3.018906  0.39888947  1.12736729 -1.73117400
-#> 57     S10     I06 4.3653339 3.018906  0.39888947  1.10087374 -0.15333491
-#> 70     S10     I07 3.7041212 3.018906  0.39888947 -0.29432480  0.58065095
-#> 72     S10     I08 5.8502180 3.018906  0.39888947  0.66207708  1.77034588
-#> 86     S10     I09 3.7997745 3.018906  0.39888947 -0.06026755  0.44224694
-#> 100    S10     I10 4.6078044 3.018906  0.39888947  0.35539173  0.83461757
      +
      #> sub_id item_id y grand_i sub_i item_i err +#> 1 S01 I01 4.1152324 3.018906 0.56998287 0.06372689 0.4626170223 +#> 15 S01 I02 4.4224176 3.018906 0.56998287 -0.61913239 1.4526614811 +#> 29 S01 I03 3.8339794 3.018906 0.56998287 -0.47084975 0.7159406247 +#> 31 S01 I04 3.6693181 3.018906 0.56998287 -0.79426537 0.8746950317 +#> 44 S01 I05 4.1682274 3.018906 0.56998287 0.04751609 0.5318228254 +#> 58 S01 I06 4.5589567 3.018906 0.56998287 -0.03988620 1.0099544077 +#> 61 S01 I07 3.3139561 3.018906 0.56998287 -0.15429743 -0.1206349769 +#> 73 S01 I08 3.4466844 3.018906 0.56998287 -0.10140898 -0.0407951098 +#> 87 S01 I09 4.1150472 3.018906 0.56998287 -0.44323124 0.9693899904 +#> 91 S01 I10 3.7546416 3.018906 0.56998287 -0.41773369 0.5834868363 +#> 7 S02 I01 4.4331212 3.018906 0.15562574 0.06372689 1.1948629436 +#> 17 S02 I02 2.6376456 3.018906 0.15562574 -0.61913239 0.0822465999 +#> 30 S02 I03 1.7765036 3.018906 0.15562574 -0.47084975 -0.9271780136 +#> 36 S02 I04 2.0848855 3.018906 0.15562574 -0.79426537 -0.2953805179 +#> 46 S02 I05 2.7319999 3.018906 0.15562574 0.04751609 -0.4900475211 +#> 59 S02 I06 2.6284272 3.018906 0.15562574 -0.03988620 -0.5062179545 +#> 69 S02 I07 2.3668419 3.018906 0.15562574 -0.15429743 -0.6533920233 +#> 75 S02 I08 3.4096195 3.018906 0.15562574 -0.10140898 0.3364971592 +#> 88 S02 I09 2.0566689 3.018906 0.15562574 -0.44323124 -0.6746311797 +#> 98 S02 I10 2.3720810 3.018906 0.15562574 -0.41773369 -0.3847166733 +#> 4 S03 I01 4.7030774 3.018906 0.03692411 0.06372689 1.5835208395 +#> 18 S03 I02 0.8793870 3.018906 0.03692411 -0.61913239 -1.5573102959 +#> 23 S03 I03 2.0037907 3.018906 0.03692411 -0.47084975 -0.5811892504 +#> 33 S03 I04 1.0014127 3.018906 0.03692411 -0.79426537 -1.2601516722 +#> 47 S03 I05 3.2211549 3.018906 0.03692411 0.04751609 0.1178091041 +#> 60 S03 I06 1.3304699 3.018906 0.03692411 -0.03988620 -1.6854735746 +#> 62 S03 I07 3.1215507 3.018906 0.03692411 -0.15429743 0.2200184260 +#> 76 S03 I08 5.3608647 3.018906 0.03692411 -0.10140898 2.4064439566 +#> 89 S03 I09 2.5733421 3.018906 0.03692411 -0.44323124 -0.0392563757 +#> 95 S03 I10 3.3314952 3.018906 0.03692411 -0.41773369 0.6933991949 +#> 5 S04 I01 2.6792280 3.018906 0.19476739 0.06372689 -0.5981719043 +#> 19 S04 I02 1.8084207 3.018906 0.19476739 -0.61913239 -0.7861199053 +#> 21 S04 I03 3.9052572 3.018906 0.19476739 -0.47084975 1.1624339153 +#> 34 S04 I04 0.2667572 3.018906 0.19476739 -0.79426537 -2.1526504501 +#> 48 S04 I05 2.0248756 3.018906 0.19476739 0.04751609 -1.2363135229 +#> 51 S04 I06 1.6265464 3.018906 0.19476739 -0.03988620 -1.5472403653 +#> 63 S04 I07 1.7877199 3.018906 0.19476739 -0.15429743 -1.2716556202 +#> 77 S04 I08 2.5907081 3.018906 0.19476739 -0.10140898 -0.5215558932 +#> 90 S04 I09 2.0019422 3.018906 0.19476739 -0.44323124 -0.7684995082 +#> 92 S04 I10 3.9799185 3.018906 0.19476739 -0.41773369 1.1839792246 +#> 2 S05 I01 3.5292700 3.018906 -0.75807487 0.06372689 1.2047123566 +#> 16 S05 I02 1.3561640 3.018906 -0.75807487 -0.61913239 -0.2855343293 +#> 22 S05 I03 3.2562826 3.018906 -0.75807487 -0.47084975 1.4663015969 +#> 35 S05 I04 2.2140772 3.018906 -0.75807487 -0.79426537 0.7475118666 +#> 45 S05 I05 3.8999286 3.018906 -0.75807487 0.04751609 1.5915817696 +#> 52 S05 I06 4.4560178 3.018906 -0.75807487 -0.03988620 2.2350733124 +#> 64 S05 I07 3.5746937 3.018906 -0.75807487 -0.15429743 1.4681603850 +#> 74 S05 I08 0.3091243 3.018906 -0.75807487 -0.10140898 -1.8502974398 +#> 81 S05 I09 2.2126284 3.018906 -0.75807487 -0.44323124 0.3950288774 +#> 93 S05 I10 0.5045372 3.018906 -0.75807487 -0.41773369 -1.3385598100 +#> 8 S06 I01 3.0560640 3.018906 0.48363404 0.06372689 -0.5102025526 +#> 20 S06 I02 3.8891499 3.018906 0.48363404 -0.61913239 1.0057426556 +#> 27 S06 I03 4.8701173 3.018906 0.48363404 -0.47084975 1.8384274413 +#> 37 S06 I04 1.4001795 3.018906 0.48363404 -0.79426537 -1.3080948287 +#> 49 S06 I05 2.0216018 3.018906 0.48363404 0.04751609 -1.5284539771 +#> 56 S06 I06 3.7873565 3.018906 0.48363404 -0.03988620 0.3247030803 +#> 66 S06 I07 3.2805782 3.018906 0.48363404 -0.15429743 -0.0676640293 +#> 79 S06 I08 4.7155581 3.018906 0.48363404 -0.10140898 1.3144273905 +#> 85 S06 I09 3.2717270 3.018906 0.48363404 -0.44323124 0.2124186328 +#> 99 S06 I10 3.2642692 3.018906 0.48363404 -0.41773369 0.1794631937 +#> 9 S07 I01 3.6939536 3.018906 0.54553255 0.06372689 0.0657885048 +#> 11 S07 I02 1.2170700 3.018906 0.54553255 -0.61913239 -1.7282357430 +#> 24 S07 I03 4.2110089 3.018906 0.54553255 -0.47084975 1.1174204576 +#> 38 S07 I04 2.1033565 3.018906 0.54553255 -0.79426537 -0.6668162783 +#> 50 S07 I05 3.7363105 3.018906 0.54553255 0.04751609 0.1243562894 +#> 53 S07 I06 3.6785335 3.018906 0.54553255 -0.03988620 0.1539815503 +#> 67 S07 I07 4.7664235 3.018906 0.54553255 -0.15429743 1.3562827924 +#> 80 S07 I08 3.0045458 3.018906 0.54553255 -0.10140898 -0.4584834292 +#> 82 S07 I09 3.8416183 3.018906 0.54553255 -0.44323124 0.7204114222 +#> 96 S07 I10 2.8716905 3.018906 0.54553255 -0.41773369 -0.2750140285 +#> 6 S08 I01 2.2301931 3.018906 -0.40144258 0.06372689 -0.4509967811 +#> 12 S08 I02 2.3133628 3.018906 -0.40144258 -0.61913239 0.3150321147 +#> 25 S08 I03 2.0169064 3.018906 -0.40144258 -0.47084975 -0.1297068522 +#> 39 S08 I04 2.0436948 3.018906 -0.40144258 -0.79426537 0.2204971325 +#> 41 S08 I05 0.9715504 3.018906 -0.40144258 0.04751609 -1.6934287350 +#> 54 S08 I06 1.8520727 3.018906 -0.40144258 -0.03988620 -0.7255041591 +#> 68 S08 I07 2.4390171 3.018906 -0.40144258 -0.15429743 -0.0241485454 +#> 78 S08 I08 1.0305279 3.018906 -0.40144258 -0.10140898 -1.4855261434 +#> 83 S08 I09 1.9171300 3.018906 -0.40144258 -0.44323124 -0.2571018196 +#> 97 S08 I10 1.6690544 3.018906 -0.40144258 -0.41773369 -0.5306749508 +#> 3 S09 I01 2.9555480 3.018906 0.57304625 0.06372689 -0.7001307111 +#> 13 S09 I02 2.2941068 3.018906 0.57304625 -0.61913239 -0.6787127129 +#> 26 S09 I03 0.7553089 3.018906 0.57304625 -0.47084975 -2.3657931602 +#> 32 S09 I04 3.7013706 3.018906 0.57304625 -0.79426537 0.9036840992 +#> 42 S09 I05 4.5290509 3.018906 0.57304625 0.04751609 0.8895829209 +#> 55 S09 I06 3.7888207 3.018906 0.57304625 -0.03988620 0.2367550325 +#> 65 S09 I07 3.4651418 3.018906 0.57304625 -0.15429743 0.0274874126 +#> 71 S09 I08 4.5560139 3.018906 0.57304625 -0.10140898 1.0654710594 +#> 84 S09 I09 2.9545448 3.018906 0.57304625 -0.44323124 -0.1941758110 +#> 94 S09 I10 2.7663826 3.018906 0.57304625 -0.41773369 -0.4078355519 +#> 10 S10 I01 4.9049963 3.018906 0.42651506 0.06372689 1.3958487131 +#> 14 S10 I02 2.2826745 3.018906 0.42651506 -0.61913239 -0.5436137684 +#> 28 S10 I03 3.0489810 3.018906 0.42651506 -0.47084975 0.0744100611 +#> 40 S10 I04 2.2858762 3.018906 0.42651506 -0.79426537 -0.3652791241 +#> 43 S10 I05 2.1215325 3.018906 0.42651506 0.04751609 -1.3714042494 +#> 57 S10 I06 4.4669262 3.018906 0.42651506 -0.03988620 1.0613917740 +#> 70 S10 I07 3.0090947 3.018906 0.42651506 -0.15429743 -0.2820285529 +#> 72 S10 I08 4.0185082 3.018906 0.42651506 -0.10140898 0.6744965044 +#> 86 S10 I09 1.0367855 3.018906 0.42651506 -0.44323124 -1.9654039006 +#> 100 S10 I10 3.0267011 3.018906 0.42651506 -0.41773369 -0.0009858907
      @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/trunc2norm-1.png b/docs/reference/trunc2norm-1.png index ef4e139d..3f9376e6 100644 Binary files a/docs/reference/trunc2norm-1.png and b/docs/reference/trunc2norm-1.png differ diff --git a/docs/reference/trunc2norm.html b/docs/reference/trunc2norm.html index 2bbc9d84..4fc7c065 100644 --- a/docs/reference/trunc2norm.html +++ b/docs/reference/trunc2norm.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • diff --git a/docs/reference/unif2norm-1.png b/docs/reference/unif2norm-1.png index d17a49fa..0a66a3f7 100644 Binary files a/docs/reference/unif2norm-1.png and b/docs/reference/unif2norm-1.png differ diff --git a/docs/reference/unif2norm.html b/docs/reference/unif2norm.html index 486544d7..eeca34a7 100644 --- a/docs/reference/unif2norm.html +++ b/docs/reference/unif2norm.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -200,10 +208,10 @@

      Examp 
      
 x <- runif(10000)
 y <- unif2norm(x)
-
      #> min was set to -9.99357387423515e-05
      #> max was set to 0.999908634631336
      g <- ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x, y))
+
      #> min was set to -7.23436776548624e-05
      #> max was set to 1.00006860069223
      g <- ggplot2::ggplot() + ggplot2::geom_point(ggplot2::aes(x, y))
 ggExtra::ggMarginal(g, type = "histogram")
 
      
-
      + @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +

    • diff --git a/docs/reference/wide2long.html b/docs/reference/wide2long.html index bf7440d7..dc75798b 100644 --- a/docs/reference/wide2long.html +++ b/docs/reference/wide2long.html @@ -45,10 +45,12 @@ + + - + @@ -84,7 +86,7 @@ faux - 1.0.0 + 1.1.0 @@ -124,12 +126,18 @@
    • Mixed Design Simulation
      • +
    • + Randomised Reports +
    • Distribution Conversions
    • Codebook Demo
      • +
    • + Contrasts +
    • @@ -209,607 +217,607 @@

      Value

      Examples

      wide2long(iris, c("Feature", "Measure"), 1:4, sep = "\\.")
-
      #>          id    Species Feature Measure   y
-#> S001.1 S001     setosa   Sepal  Length 5.1
-#> S002.1 S002     setosa   Sepal  Length 4.9
-#> S003.1 S003     setosa   Sepal  Length 4.7
-#> S004.1 S004     setosa   Sepal  Length 4.6
-#> S005.1 S005     setosa   Sepal  Length 5.0
-#> S006.1 S006     setosa   Sepal  Length 5.4
-#> S007.1 S007     setosa   Sepal  Length 4.6
-#> S008.1 S008     setosa   Sepal  Length 5.0
-#> S009.1 S009     setosa   Sepal  Length 4.4
-#> S010.1 S010     setosa   Sepal  Length 4.9
-#> S011.1 S011     setosa   Sepal  Length 5.4
-#> S012.1 S012     setosa   Sepal  Length 4.8
-#> S013.1 S013     setosa   Sepal  Length 4.8
-#> S014.1 S014     setosa   Sepal  Length 4.3
-#> S015.1 S015     setosa   Sepal  Length 5.8
-#> S016.1 S016     setosa   Sepal  Length 5.7
-#> S017.1 S017     setosa   Sepal  Length 5.4
-#> S018.1 S018     setosa   Sepal  Length 5.1
-#> S019.1 S019     setosa   Sepal  Length 5.7
-#> S020.1 S020     setosa   Sepal  Length 5.1
-#> S021.1 S021     setosa   Sepal  Length 5.4
-#> S022.1 S022     setosa   Sepal  Length 5.1
-#> S023.1 S023     setosa   Sepal  Length 4.6
-#> S024.1 S024     setosa   Sepal  Length 5.1
-#> S025.1 S025     setosa   Sepal  Length 4.8
-#> S026.1 S026     setosa   Sepal  Length 5.0
-#> S027.1 S027     setosa   Sepal  Length 5.0
-#> S028.1 S028     setosa   Sepal  Length 5.2
-#> S029.1 S029     setosa   Sepal  Length 5.2
-#> S030.1 S030     setosa   Sepal  Length 4.7
-#> S031.1 S031     setosa   Sepal  Length 4.8
-#> S032.1 S032     setosa   Sepal  Length 5.4
-#> S033.1 S033     setosa   Sepal  Length 5.2
-#> S034.1 S034     setosa   Sepal  Length 5.5
-#> S035.1 S035     setosa   Sepal  Length 4.9
-#> S036.1 S036     setosa   Sepal  Length 5.0
-#> S037.1 S037     setosa   Sepal  Length 5.5
-#> S038.1 S038     setosa   Sepal  Length 4.9
-#> S039.1 S039     setosa   Sepal  Length 4.4
-#> S040.1 S040     setosa   Sepal  Length 5.1
-#> S041.1 S041     setosa   Sepal  Length 5.0
-#> S042.1 S042     setosa   Sepal  Length 4.5
-#> S043.1 S043     setosa   Sepal  Length 4.4
-#> S044.1 S044     setosa   Sepal  Length 5.0
-#> S045.1 S045     setosa   Sepal  Length 5.1
-#> S046.1 S046     setosa   Sepal  Length 4.8
-#> S047.1 S047     setosa   Sepal  Length 5.1
-#> S048.1 S048     setosa   Sepal  Length 4.6
-#> S049.1 S049     setosa   Sepal  Length 5.3
-#> S050.1 S050     setosa   Sepal  Length 5.0
-#> S051.1 S051 versicolor   Sepal  Length 7.0
-#> S052.1 S052 versicolor   Sepal  Length 6.4
-#> S053.1 S053 versicolor   Sepal  Length 6.9
-#> S054.1 S054 versicolor   Sepal  Length 5.5
-#> S055.1 S055 versicolor   Sepal  Length 6.5
-#> S056.1 S056 versicolor   Sepal  Length 5.7
-#> S057.1 S057 versicolor   Sepal  Length 6.3
-#> S058.1 S058 versicolor   Sepal  Length 4.9
-#> S059.1 S059 versicolor   Sepal  Length 6.6
-#> S060.1 S060 versicolor   Sepal  Length 5.2
-#> S061.1 S061 versicolor   Sepal  Length 5.0
-#> S062.1 S062 versicolor   Sepal  Length 5.9
-#> S063.1 S063 versicolor   Sepal  Length 6.0
-#> S064.1 S064 versicolor   Sepal  Length 6.1
-#> S065.1 S065 versicolor   Sepal  Length 5.6
-#> S066.1 S066 versicolor   Sepal  Length 6.7
-#> S067.1 S067 versicolor   Sepal  Length 5.6
-#> S068.1 S068 versicolor   Sepal  Length 5.8
-#> S069.1 S069 versicolor   Sepal  Length 6.2
-#> S070.1 S070 versicolor   Sepal  Length 5.6
-#> S071.1 S071 versicolor   Sepal  Length 5.9
-#> S072.1 S072 versicolor   Sepal  Length 6.1
-#> S073.1 S073 versicolor   Sepal  Length 6.3
-#> S074.1 S074 versicolor   Sepal  Length 6.1
-#> S075.1 S075 versicolor   Sepal  Length 6.4
-#> S076.1 S076 versicolor   Sepal  Length 6.6
-#> S077.1 S077 versicolor   Sepal  Length 6.8
-#> S078.1 S078 versicolor   Sepal  Length 6.7
-#> S079.1 S079 versicolor   Sepal  Length 6.0
-#> S080.1 S080 versicolor   Sepal  Length 5.7
-#> S081.1 S081 versicolor   Sepal  Length 5.5
-#> S082.1 S082 versicolor   Sepal  Length 5.5
-#> S083.1 S083 versicolor   Sepal  Length 5.8
-#> S084.1 S084 versicolor   Sepal  Length 6.0
-#> S085.1 S085 versicolor   Sepal  Length 5.4
-#> S086.1 S086 versicolor   Sepal  Length 6.0
-#> S087.1 S087 versicolor   Sepal  Length 6.7
-#> S088.1 S088 versicolor   Sepal  Length 6.3
-#> S089.1 S089 versicolor   Sepal  Length 5.6
-#> S090.1 S090 versicolor   Sepal  Length 5.5
-#> S091.1 S091 versicolor   Sepal  Length 5.5
-#> S092.1 S092 versicolor   Sepal  Length 6.1
-#> S093.1 S093 versicolor   Sepal  Length 5.8
-#> S094.1 S094 versicolor   Sepal  Length 5.0
-#> S095.1 S095 versicolor   Sepal  Length 5.6
-#> S096.1 S096 versicolor   Sepal  Length 5.7
-#> S097.1 S097 versicolor   Sepal  Length 5.7
-#> S098.1 S098 versicolor   Sepal  Length 6.2
-#> S099.1 S099 versicolor   Sepal  Length 5.1
-#> S100.1 S100 versicolor   Sepal  Length 5.7
-#> S101.1 S101  virginica   Sepal  Length 6.3
-#> S102.1 S102  virginica   Sepal  Length 5.8
-#> S103.1 S103  virginica   Sepal  Length 7.1
-#> S104.1 S104  virginica   Sepal  Length 6.3
-#> S105.1 S105  virginica   Sepal  Length 6.5
-#> S106.1 S106  virginica   Sepal  Length 7.6
-#> S107.1 S107  virginica   Sepal  Length 4.9
-#> S108.1 S108  virginica   Sepal  Length 7.3
-#> S109.1 S109  virginica   Sepal  Length 6.7
-#> S110.1 S110  virginica   Sepal  Length 7.2
-#> S111.1 S111  virginica   Sepal  Length 6.5
-#> S112.1 S112  virginica   Sepal  Length 6.4
-#> S113.1 S113  virginica   Sepal  Length 6.8
-#> S114.1 S114  virginica   Sepal  Length 5.7
-#> S115.1 S115  virginica   Sepal  Length 5.8
-#> S116.1 S116  virginica   Sepal  Length 6.4
-#> S117.1 S117  virginica   Sepal  Length 6.5
-#> S118.1 S118  virginica   Sepal  Length 7.7
-#> S119.1 S119  virginica   Sepal  Length 7.7
-#> S120.1 S120  virginica   Sepal  Length 6.0
-#> S121.1 S121  virginica   Sepal  Length 6.9
-#> S122.1 S122  virginica   Sepal  Length 5.6
-#> S123.1 S123  virginica   Sepal  Length 7.7
-#> S124.1 S124  virginica   Sepal  Length 6.3
-#> S125.1 S125  virginica   Sepal  Length 6.7
-#> S126.1 S126  virginica   Sepal  Length 7.2
-#> S127.1 S127  virginica   Sepal  Length 6.2
-#> S128.1 S128  virginica   Sepal  Length 6.1
-#> S129.1 S129  virginica   Sepal  Length 6.4
-#> S130.1 S130  virginica   Sepal  Length 7.2
-#> S131.1 S131  virginica   Sepal  Length 7.4
-#> S132.1 S132  virginica   Sepal  Length 7.9
-#> S133.1 S133  virginica   Sepal  Length 6.4
-#> S134.1 S134  virginica   Sepal  Length 6.3
-#> S135.1 S135  virginica   Sepal  Length 6.1
-#> S136.1 S136  virginica   Sepal  Length 7.7
-#> S137.1 S137  virginica   Sepal  Length 6.3
-#> S138.1 S138  virginica   Sepal  Length 6.4
-#> S139.1 S139  virginica   Sepal  Length 6.0
-#> S140.1 S140  virginica   Sepal  Length 6.9
-#> S141.1 S141  virginica   Sepal  Length 6.7
-#> S142.1 S142  virginica   Sepal  Length 6.9
-#> S143.1 S143  virginica   Sepal  Length 5.8
-#> S144.1 S144  virginica   Sepal  Length 6.8
-#> S145.1 S145  virginica   Sepal  Length 6.7
-#> S146.1 S146  virginica   Sepal  Length 6.7
-#> S147.1 S147  virginica   Sepal  Length 6.3
-#> S148.1 S148  virginica   Sepal  Length 6.5
-#> S149.1 S149  virginica   Sepal  Length 6.2
-#> S150.1 S150  virginica   Sepal  Length 5.9
-#> S001.2 S001     setosa   Sepal   Width 3.5
-#> S002.2 S002     setosa   Sepal   Width 3.0
-#> S003.2 S003     setosa   Sepal   Width 3.2
-#> S004.2 S004     setosa   Sepal   Width 3.1
-#> S005.2 S005     setosa   Sepal   Width 3.6
-#> S006.2 S006     setosa   Sepal   Width 3.9
-#> S007.2 S007     setosa   Sepal   Width 3.4
-#> S008.2 S008     setosa   Sepal   Width 3.4
-#> S009.2 S009     setosa   Sepal   Width 2.9
-#> S010.2 S010     setosa   Sepal   Width 3.1
-#> S011.2 S011     setosa   Sepal   Width 3.7
-#> S012.2 S012     setosa   Sepal   Width 3.4
-#> S013.2 S013     setosa   Sepal   Width 3.0
-#> S014.2 S014     setosa   Sepal   Width 3.0
-#> S015.2 S015     setosa   Sepal   Width 4.0
-#> S016.2 S016     setosa   Sepal   Width 4.4
-#> S017.2 S017     setosa   Sepal   Width 3.9
-#> S018.2 S018     setosa   Sepal   Width 3.5
-#> S019.2 S019     setosa   Sepal   Width 3.8
-#> S020.2 S020     setosa   Sepal   Width 3.8
-#> S021.2 S021     setosa   Sepal   Width 3.4
-#> S022.2 S022     setosa   Sepal   Width 3.7
-#> S023.2 S023     setosa   Sepal   Width 3.6
-#> S024.2 S024     setosa   Sepal   Width 3.3
-#> S025.2 S025     setosa   Sepal   Width 3.4
-#> S026.2 S026     setosa   Sepal   Width 3.0
-#> S027.2 S027     setosa   Sepal   Width 3.4
-#> S028.2 S028     setosa   Sepal   Width 3.5
-#> S029.2 S029     setosa   Sepal   Width 3.4
-#> S030.2 S030     setosa   Sepal   Width 3.2
-#> S031.2 S031     setosa   Sepal   Width 3.1
-#> S032.2 S032     setosa   Sepal   Width 3.4
-#> S033.2 S033     setosa   Sepal   Width 4.1
-#> S034.2 S034     setosa   Sepal   Width 4.2
-#> S035.2 S035     setosa   Sepal   Width 3.1
-#> S036.2 S036     setosa   Sepal   Width 3.2
-#> S037.2 S037     setosa   Sepal   Width 3.5
-#> S038.2 S038     setosa   Sepal   Width 3.6
-#> S039.2 S039     setosa   Sepal   Width 3.0
-#> S040.2 S040     setosa   Sepal   Width 3.4
-#> S041.2 S041     setosa   Sepal   Width 3.5
-#> S042.2 S042     setosa   Sepal   Width 2.3
-#> S043.2 S043     setosa   Sepal   Width 3.2
-#> S044.2 S044     setosa   Sepal   Width 3.5
-#> S045.2 S045     setosa   Sepal   Width 3.8
-#> S046.2 S046     setosa   Sepal   Width 3.0
-#> S047.2 S047     setosa   Sepal   Width 3.8
-#> S048.2 S048     setosa   Sepal   Width 3.2
-#> S049.2 S049     setosa   Sepal   Width 3.7
-#> S050.2 S050     setosa   Sepal   Width 3.3
-#> S051.2 S051 versicolor   Sepal   Width 3.2
-#> S052.2 S052 versicolor   Sepal   Width 3.2
-#> S053.2 S053 versicolor   Sepal   Width 3.1
-#> S054.2 S054 versicolor   Sepal   Width 2.3
-#> S055.2 S055 versicolor   Sepal   Width 2.8
-#> S056.2 S056 versicolor   Sepal   Width 2.8
-#> S057.2 S057 versicolor   Sepal   Width 3.3
-#> S058.2 S058 versicolor   Sepal   Width 2.4
-#> S059.2 S059 versicolor   Sepal   Width 2.9
-#> S060.2 S060 versicolor   Sepal   Width 2.7
-#> S061.2 S061 versicolor   Sepal   Width 2.0
-#> S062.2 S062 versicolor   Sepal   Width 3.0
-#> S063.2 S063 versicolor   Sepal   Width 2.2
-#> S064.2 S064 versicolor   Sepal   Width 2.9
-#> S065.2 S065 versicolor   Sepal   Width 2.9
-#> S066.2 S066 versicolor   Sepal   Width 3.1
-#> S067.2 S067 versicolor   Sepal   Width 3.0
-#> S068.2 S068 versicolor   Sepal   Width 2.7
-#> S069.2 S069 versicolor   Sepal   Width 2.2
-#> S070.2 S070 versicolor   Sepal   Width 2.5
-#> S071.2 S071 versicolor   Sepal   Width 3.2
-#> S072.2 S072 versicolor   Sepal   Width 2.8
-#> S073.2 S073 versicolor   Sepal   Width 2.5
-#> S074.2 S074 versicolor   Sepal   Width 2.8
-#> S075.2 S075 versicolor   Sepal   Width 2.9
-#> S076.2 S076 versicolor   Sepal   Width 3.0
-#> S077.2 S077 versicolor   Sepal   Width 2.8
-#> S078.2 S078 versicolor   Sepal   Width 3.0
-#> S079.2 S079 versicolor   Sepal   Width 2.9
-#> S080.2 S080 versicolor   Sepal   Width 2.6
-#> S081.2 S081 versicolor   Sepal   Width 2.4
-#> S082.2 S082 versicolor   Sepal   Width 2.4
-#> S083.2 S083 versicolor   Sepal   Width 2.7
-#> S084.2 S084 versicolor   Sepal   Width 2.7
-#> S085.2 S085 versicolor   Sepal   Width 3.0
-#> S086.2 S086 versicolor   Sepal   Width 3.4
-#> S087.2 S087 versicolor   Sepal   Width 3.1
-#> S088.2 S088 versicolor   Sepal   Width 2.3
-#> S089.2 S089 versicolor   Sepal   Width 3.0
-#> S090.2 S090 versicolor   Sepal   Width 2.5
-#> S091.2 S091 versicolor   Sepal   Width 2.6
-#> S092.2 S092 versicolor   Sepal   Width 3.0
-#> S093.2 S093 versicolor   Sepal   Width 2.6
-#> S094.2 S094 versicolor   Sepal   Width 2.3
-#> S095.2 S095 versicolor   Sepal   Width 2.7
-#> S096.2 S096 versicolor   Sepal   Width 3.0
-#> S097.2 S097 versicolor   Sepal   Width 2.9
-#> S098.2 S098 versicolor   Sepal   Width 2.9
-#> S099.2 S099 versicolor   Sepal   Width 2.5
-#> S100.2 S100 versicolor   Sepal   Width 2.8
-#> S101.2 S101  virginica   Sepal   Width 3.3
-#> S102.2 S102  virginica   Sepal   Width 2.7
-#> S103.2 S103  virginica   Sepal   Width 3.0
-#> S104.2 S104  virginica   Sepal   Width 2.9
-#> S105.2 S105  virginica   Sepal   Width 3.0
-#> S106.2 S106  virginica   Sepal   Width 3.0
-#> S107.2 S107  virginica   Sepal   Width 2.5
-#> S108.2 S108  virginica   Sepal   Width 2.9
-#> S109.2 S109  virginica   Sepal   Width 2.5
-#> S110.2 S110  virginica   Sepal   Width 3.6
-#> S111.2 S111  virginica   Sepal   Width 3.2
-#> S112.2 S112  virginica   Sepal   Width 2.7
-#> S113.2 S113  virginica   Sepal   Width 3.0
-#> S114.2 S114  virginica   Sepal   Width 2.5
-#> S115.2 S115  virginica   Sepal   Width 2.8
-#> S116.2 S116  virginica   Sepal   Width 3.2
-#> S117.2 S117  virginica   Sepal   Width 3.0
-#> S118.2 S118  virginica   Sepal   Width 3.8
-#> S119.2 S119  virginica   Sepal   Width 2.6
-#> S120.2 S120  virginica   Sepal   Width 2.2
-#> S121.2 S121  virginica   Sepal   Width 3.2
-#> S122.2 S122  virginica   Sepal   Width 2.8
-#> S123.2 S123  virginica   Sepal   Width 2.8
-#> S124.2 S124  virginica   Sepal   Width 2.7
-#> S125.2 S125  virginica   Sepal   Width 3.3
-#> S126.2 S126  virginica   Sepal   Width 3.2
-#> S127.2 S127  virginica   Sepal   Width 2.8
-#> S128.2 S128  virginica   Sepal   Width 3.0
-#> S129.2 S129  virginica   Sepal   Width 2.8
-#> S130.2 S130  virginica   Sepal   Width 3.0
-#> S131.2 S131  virginica   Sepal   Width 2.8
-#> S132.2 S132  virginica   Sepal   Width 3.8
-#> S133.2 S133  virginica   Sepal   Width 2.8
-#> S134.2 S134  virginica   Sepal   Width 2.8
-#> S135.2 S135  virginica   Sepal   Width 2.6
-#> S136.2 S136  virginica   Sepal   Width 3.0
-#> S137.2 S137  virginica   Sepal   Width 3.4
-#> S138.2 S138  virginica   Sepal   Width 3.1
-#> S139.2 S139  virginica   Sepal   Width 3.0
-#> S140.2 S140  virginica   Sepal   Width 3.1
-#> S141.2 S141  virginica   Sepal   Width 3.1
-#> S142.2 S142  virginica   Sepal   Width 3.1
-#> S143.2 S143  virginica   Sepal   Width 2.7
-#> S144.2 S144  virginica   Sepal   Width 3.2
-#> S145.2 S145  virginica   Sepal   Width 3.3
-#> S146.2 S146  virginica   Sepal   Width 3.0
-#> S147.2 S147  virginica   Sepal   Width 2.5
-#> S148.2 S148  virginica   Sepal   Width 3.0
-#> S149.2 S149  virginica   Sepal   Width 3.4
-#> S150.2 S150  virginica   Sepal   Width 3.0
-#> S001.3 S001     setosa   Petal  Length 1.4
-#> S002.3 S002     setosa   Petal  Length 1.4
-#> S003.3 S003     setosa   Petal  Length 1.3
-#> S004.3 S004     setosa   Petal  Length 1.5
-#> S005.3 S005     setosa   Petal  Length 1.4
-#> S006.3 S006     setosa   Petal  Length 1.7
-#> S007.3 S007     setosa   Petal  Length 1.4
-#> S008.3 S008     setosa   Petal  Length 1.5
-#> S009.3 S009     setosa   Petal  Length 1.4
-#> S010.3 S010     setosa   Petal  Length 1.5
-#> S011.3 S011     setosa   Petal  Length 1.5
-#> S012.3 S012     setosa   Petal  Length 1.6
-#> S013.3 S013     setosa   Petal  Length 1.4
-#> S014.3 S014     setosa   Petal  Length 1.1
-#> S015.3 S015     setosa   Petal  Length 1.2
-#> S016.3 S016     setosa   Petal  Length 1.5
-#> S017.3 S017     setosa   Petal  Length 1.3
-#> S018.3 S018     setosa   Petal  Length 1.4
-#> S019.3 S019     setosa   Petal  Length 1.7
-#> S020.3 S020     setosa   Petal  Length 1.5
-#> S021.3 S021     setosa   Petal  Length 1.7
-#> S022.3 S022     setosa   Petal  Length 1.5
-#> S023.3 S023     setosa   Petal  Length 1.0
-#> S024.3 S024     setosa   Petal  Length 1.7
-#> S025.3 S025     setosa   Petal  Length 1.9
-#> S026.3 S026     setosa   Petal  Length 1.6
-#> S027.3 S027     setosa   Petal  Length 1.6
-#> S028.3 S028     setosa   Petal  Length 1.5
-#> S029.3 S029     setosa   Petal  Length 1.4
-#> S030.3 S030     setosa   Petal  Length 1.6
-#> S031.3 S031     setosa   Petal  Length 1.6
-#> S032.3 S032     setosa   Petal  Length 1.5
-#> S033.3 S033     setosa   Petal  Length 1.5
-#> S034.3 S034     setosa   Petal  Length 1.4
-#> S035.3 S035     setosa   Petal  Length 1.5
-#> S036.3 S036     setosa   Petal  Length 1.2
-#> S037.3 S037     setosa   Petal  Length 1.3
-#> S038.3 S038     setosa   Petal  Length 1.4
-#> S039.3 S039     setosa   Petal  Length 1.3
-#> S040.3 S040     setosa   Petal  Length 1.5
-#> S041.3 S041     setosa   Petal  Length 1.3
-#> S042.3 S042     setosa   Petal  Length 1.3
-#> S043.3 S043     setosa   Petal  Length 1.3
-#> S044.3 S044     setosa   Petal  Length 1.6
-#> S045.3 S045     setosa   Petal  Length 1.9
-#> S046.3 S046     setosa   Petal  Length 1.4
-#> S047.3 S047     setosa   Petal  Length 1.6
-#> S048.3 S048     setosa   Petal  Length 1.4
-#> S049.3 S049     setosa   Petal  Length 1.5
-#> S050.3 S050     setosa   Petal  Length 1.4
-#> S051.3 S051 versicolor   Petal  Length 4.7
-#> S052.3 S052 versicolor   Petal  Length 4.5
-#> S053.3 S053 versicolor   Petal  Length 4.9
-#> S054.3 S054 versicolor   Petal  Length 4.0
-#> S055.3 S055 versicolor   Petal  Length 4.6
-#> S056.3 S056 versicolor   Petal  Length 4.5
-#> S057.3 S057 versicolor   Petal  Length 4.7
-#> S058.3 S058 versicolor   Petal  Length 3.3
-#> S059.3 S059 versicolor   Petal  Length 4.6
-#> S060.3 S060 versicolor   Petal  Length 3.9
-#> S061.3 S061 versicolor   Petal  Length 3.5
-#> S062.3 S062 versicolor   Petal  Length 4.2
-#> S063.3 S063 versicolor   Petal  Length 4.0
-#> S064.3 S064 versicolor   Petal  Length 4.7
-#> S065.3 S065 versicolor   Petal  Length 3.6
-#> S066.3 S066 versicolor   Petal  Length 4.4
-#> S067.3 S067 versicolor   Petal  Length 4.5
-#> S068.3 S068 versicolor   Petal  Length 4.1
-#> S069.3 S069 versicolor   Petal  Length 4.5
-#> S070.3 S070 versicolor   Petal  Length 3.9
-#> S071.3 S071 versicolor   Petal  Length 4.8
-#> S072.3 S072 versicolor   Petal  Length 4.0
-#> S073.3 S073 versicolor   Petal  Length 4.9
-#> S074.3 S074 versicolor   Petal  Length 4.7
-#> S075.3 S075 versicolor   Petal  Length 4.3
-#> S076.3 S076 versicolor   Petal  Length 4.4
-#> S077.3 S077 versicolor   Petal  Length 4.8
-#> S078.3 S078 versicolor   Petal  Length 5.0
-#> S079.3 S079 versicolor   Petal  Length 4.5
-#> S080.3 S080 versicolor   Petal  Length 3.5
-#> S081.3 S081 versicolor   Petal  Length 3.8
-#> S082.3 S082 versicolor   Petal  Length 3.7
-#> S083.3 S083 versicolor   Petal  Length 3.9
-#> S084.3 S084 versicolor   Petal  Length 5.1
-#> S085.3 S085 versicolor   Petal  Length 4.5
-#> S086.3 S086 versicolor   Petal  Length 4.5
-#> S087.3 S087 versicolor   Petal  Length 4.7
-#> S088.3 S088 versicolor   Petal  Length 4.4
-#> S089.3 S089 versicolor   Petal  Length 4.1
-#> S090.3 S090 versicolor   Petal  Length 4.0
-#> S091.3 S091 versicolor   Petal  Length 4.4
-#> S092.3 S092 versicolor   Petal  Length 4.6
-#> S093.3 S093 versicolor   Petal  Length 4.0
-#> S094.3 S094 versicolor   Petal  Length 3.3
-#> S095.3 S095 versicolor   Petal  Length 4.2
-#> S096.3 S096 versicolor   Petal  Length 4.2
-#> S097.3 S097 versicolor   Petal  Length 4.2
-#> S098.3 S098 versicolor   Petal  Length 4.3
-#> S099.3 S099 versicolor   Petal  Length 3.0
-#> S100.3 S100 versicolor   Petal  Length 4.1
-#> S101.3 S101  virginica   Petal  Length 6.0
-#> S102.3 S102  virginica   Petal  Length 5.1
-#> S103.3 S103  virginica   Petal  Length 5.9
-#> S104.3 S104  virginica   Petal  Length 5.6
-#> S105.3 S105  virginica   Petal  Length 5.8
-#> S106.3 S106  virginica   Petal  Length 6.6
-#> S107.3 S107  virginica   Petal  Length 4.5
-#> S108.3 S108  virginica   Petal  Length 6.3
-#> S109.3 S109  virginica   Petal  Length 5.8
-#> S110.3 S110  virginica   Petal  Length 6.1
-#> S111.3 S111  virginica   Petal  Length 5.1
-#> S112.3 S112  virginica   Petal  Length 5.3
-#> S113.3 S113  virginica   Petal  Length 5.5
-#> S114.3 S114  virginica   Petal  Length 5.0
-#> S115.3 S115  virginica   Petal  Length 5.1
-#> S116.3 S116  virginica   Petal  Length 5.3
-#> S117.3 S117  virginica   Petal  Length 5.5
-#> S118.3 S118  virginica   Petal  Length 6.7
-#> S119.3 S119  virginica   Petal  Length 6.9
-#> S120.3 S120  virginica   Petal  Length 5.0
-#> S121.3 S121  virginica   Petal  Length 5.7
-#> S122.3 S122  virginica   Petal  Length 4.9
-#> S123.3 S123  virginica   Petal  Length 6.7
-#> S124.3 S124  virginica   Petal  Length 4.9
-#> S125.3 S125  virginica   Petal  Length 5.7
-#> S126.3 S126  virginica   Petal  Length 6.0
-#> S127.3 S127  virginica   Petal  Length 4.8
-#> S128.3 S128  virginica   Petal  Length 4.9
-#> S129.3 S129  virginica   Petal  Length 5.6
-#> S130.3 S130  virginica   Petal  Length 5.8
-#> S131.3 S131  virginica   Petal  Length 6.1
-#> S132.3 S132  virginica   Petal  Length 6.4
-#> S133.3 S133  virginica   Petal  Length 5.6
-#> S134.3 S134  virginica   Petal  Length 5.1
-#> S135.3 S135  virginica   Petal  Length 5.6
-#> S136.3 S136  virginica   Petal  Length 6.1
-#> S137.3 S137  virginica   Petal  Length 5.6
-#> S138.3 S138  virginica   Petal  Length 5.5
-#> S139.3 S139  virginica   Petal  Length 4.8
-#> S140.3 S140  virginica   Petal  Length 5.4
-#> S141.3 S141  virginica   Petal  Length 5.6
-#> S142.3 S142  virginica   Petal  Length 5.1
-#> S143.3 S143  virginica   Petal  Length 5.1
-#> S144.3 S144  virginica   Petal  Length 5.9
-#> S145.3 S145  virginica   Petal  Length 5.7
-#> S146.3 S146  virginica   Petal  Length 5.2
-#> S147.3 S147  virginica   Petal  Length 5.0
-#> S148.3 S148  virginica   Petal  Length 5.2
-#> S149.3 S149  virginica   Petal  Length 5.4
-#> S150.3 S150  virginica   Petal  Length 5.1
-#> S001.4 S001     setosa   Petal   Width 0.2
-#> S002.4 S002     setosa   Petal   Width 0.2
-#> S003.4 S003     setosa   Petal   Width 0.2
-#> S004.4 S004     setosa   Petal   Width 0.2
-#> S005.4 S005     setosa   Petal   Width 0.2
-#> S006.4 S006     setosa   Petal   Width 0.4
-#> S007.4 S007     setosa   Petal   Width 0.3
-#> S008.4 S008     setosa   Petal   Width 0.2
-#> S009.4 S009     setosa   Petal   Width 0.2
-#> S010.4 S010     setosa   Petal   Width 0.1
-#> S011.4 S011     setosa   Petal   Width 0.2
-#> S012.4 S012     setosa   Petal   Width 0.2
-#> S013.4 S013     setosa   Petal   Width 0.1
-#> S014.4 S014     setosa   Petal   Width 0.1
-#> S015.4 S015     setosa   Petal   Width 0.2
-#> S016.4 S016     setosa   Petal   Width 0.4
-#> S017.4 S017     setosa   Petal   Width 0.4
-#> S018.4 S018     setosa   Petal   Width 0.3
-#> S019.4 S019     setosa   Petal   Width 0.3
-#> S020.4 S020     setosa   Petal   Width 0.3
-#> S021.4 S021     setosa   Petal   Width 0.2
-#> S022.4 S022     setosa   Petal   Width 0.4
-#> S023.4 S023     setosa   Petal   Width 0.2
-#> S024.4 S024     setosa   Petal   Width 0.5
-#> S025.4 S025     setosa   Petal   Width 0.2
-#> S026.4 S026     setosa   Petal   Width 0.2
-#> S027.4 S027     setosa   Petal   Width 0.4
-#> S028.4 S028     setosa   Petal   Width 0.2
-#> S029.4 S029     setosa   Petal   Width 0.2
-#> S030.4 S030     setosa   Petal   Width 0.2
-#> S031.4 S031     setosa   Petal   Width 0.2
-#> S032.4 S032     setosa   Petal   Width 0.4
-#> S033.4 S033     setosa   Petal   Width 0.1
-#> S034.4 S034     setosa   Petal   Width 0.2
-#> S035.4 S035     setosa   Petal   Width 0.2
-#> S036.4 S036     setosa   Petal   Width 0.2
-#> S037.4 S037     setosa   Petal   Width 0.2
-#> S038.4 S038     setosa   Petal   Width 0.1
-#> S039.4 S039     setosa   Petal   Width 0.2
-#> S040.4 S040     setosa   Petal   Width 0.2
-#> S041.4 S041     setosa   Petal   Width 0.3
-#> S042.4 S042     setosa   Petal   Width 0.3
-#> S043.4 S043     setosa   Petal   Width 0.2
-#> S044.4 S044     setosa   Petal   Width 0.6
-#> S045.4 S045     setosa   Petal   Width 0.4
-#> S046.4 S046     setosa   Petal   Width 0.3
-#> S047.4 S047     setosa   Petal   Width 0.2
-#> S048.4 S048     setosa   Petal   Width 0.2
-#> S049.4 S049     setosa   Petal   Width 0.2
-#> S050.4 S050     setosa   Petal   Width 0.2
-#> S051.4 S051 versicolor   Petal   Width 1.4
-#> S052.4 S052 versicolor   Petal   Width 1.5
-#> S053.4 S053 versicolor   Petal   Width 1.5
-#> S054.4 S054 versicolor   Petal   Width 1.3
-#> S055.4 S055 versicolor   Petal   Width 1.5
-#> S056.4 S056 versicolor   Petal   Width 1.3
-#> S057.4 S057 versicolor   Petal   Width 1.6
-#> S058.4 S058 versicolor   Petal   Width 1.0
-#> S059.4 S059 versicolor   Petal   Width 1.3
-#> S060.4 S060 versicolor   Petal   Width 1.4
-#> S061.4 S061 versicolor   Petal   Width 1.0
-#> S062.4 S062 versicolor   Petal   Width 1.5
-#> S063.4 S063 versicolor   Petal   Width 1.0
-#> S064.4 S064 versicolor   Petal   Width 1.4
-#> S065.4 S065 versicolor   Petal   Width 1.3
-#> S066.4 S066 versicolor   Petal   Width 1.4
-#> S067.4 S067 versicolor   Petal   Width 1.5
-#> S068.4 S068 versicolor   Petal   Width 1.0
-#> S069.4 S069 versicolor   Petal   Width 1.5
-#> S070.4 S070 versicolor   Petal   Width 1.1
-#> S071.4 S071 versicolor   Petal   Width 1.8
-#> S072.4 S072 versicolor   Petal   Width 1.3
-#> S073.4 S073 versicolor   Petal   Width 1.5
-#> S074.4 S074 versicolor   Petal   Width 1.2
-#> S075.4 S075 versicolor   Petal   Width 1.3
-#> S076.4 S076 versicolor   Petal   Width 1.4
-#> S077.4 S077 versicolor   Petal   Width 1.4
-#> S078.4 S078 versicolor   Petal   Width 1.7
-#> S079.4 S079 versicolor   Petal   Width 1.5
-#> S080.4 S080 versicolor   Petal   Width 1.0
-#> S081.4 S081 versicolor   Petal   Width 1.1
-#> S082.4 S082 versicolor   Petal   Width 1.0
-#> S083.4 S083 versicolor   Petal   Width 1.2
-#> S084.4 S084 versicolor   Petal   Width 1.6
-#> S085.4 S085 versicolor   Petal   Width 1.5
-#> S086.4 S086 versicolor   Petal   Width 1.6
-#> S087.4 S087 versicolor   Petal   Width 1.5
-#> S088.4 S088 versicolor   Petal   Width 1.3
-#> S089.4 S089 versicolor   Petal   Width 1.3
-#> S090.4 S090 versicolor   Petal   Width 1.3
-#> S091.4 S091 versicolor   Petal   Width 1.2
-#> S092.4 S092 versicolor   Petal   Width 1.4
-#> S093.4 S093 versicolor   Petal   Width 1.2
-#> S094.4 S094 versicolor   Petal   Width 1.0
-#> S095.4 S095 versicolor   Petal   Width 1.3
-#> S096.4 S096 versicolor   Petal   Width 1.2
-#> S097.4 S097 versicolor   Petal   Width 1.3
-#> S098.4 S098 versicolor   Petal   Width 1.3
-#> S099.4 S099 versicolor   Petal   Width 1.1
-#> S100.4 S100 versicolor   Petal   Width 1.3
-#> S101.4 S101  virginica   Petal   Width 2.5
-#> S102.4 S102  virginica   Petal   Width 1.9
-#> S103.4 S103  virginica   Petal   Width 2.1
-#> S104.4 S104  virginica   Petal   Width 1.8
-#> S105.4 S105  virginica   Petal   Width 2.2
-#> S106.4 S106  virginica   Petal   Width 2.1
-#> S107.4 S107  virginica   Petal   Width 1.7
-#> S108.4 S108  virginica   Petal   Width 1.8
-#> S109.4 S109  virginica   Petal   Width 1.8
-#> S110.4 S110  virginica   Petal   Width 2.5
-#> S111.4 S111  virginica   Petal   Width 2.0
-#> S112.4 S112  virginica   Petal   Width 1.9
-#> S113.4 S113  virginica   Petal   Width 2.1
-#> S114.4 S114  virginica   Petal   Width 2.0
-#> S115.4 S115  virginica   Petal   Width 2.4
-#> S116.4 S116  virginica   Petal   Width 2.3
-#> S117.4 S117  virginica   Petal   Width 1.8
-#> S118.4 S118  virginica   Petal   Width 2.2
-#> S119.4 S119  virginica   Petal   Width 2.3
-#> S120.4 S120  virginica   Petal   Width 1.5
-#> S121.4 S121  virginica   Petal   Width 2.3
-#> S122.4 S122  virginica   Petal   Width 2.0
-#> S123.4 S123  virginica   Petal   Width 2.0
-#> S124.4 S124  virginica   Petal   Width 1.8
-#> S125.4 S125  virginica   Petal   Width 2.1
-#> S126.4 S126  virginica   Petal   Width 1.8
-#> S127.4 S127  virginica   Petal   Width 1.8
-#> S128.4 S128  virginica   Petal   Width 1.8
-#> S129.4 S129  virginica   Petal   Width 2.1
-#> S130.4 S130  virginica   Petal   Width 1.6
-#> S131.4 S131  virginica   Petal   Width 1.9
-#> S132.4 S132  virginica   Petal   Width 2.0
-#> S133.4 S133  virginica   Petal   Width 2.2
-#> S134.4 S134  virginica   Petal   Width 1.5
-#> S135.4 S135  virginica   Petal   Width 1.4
-#> S136.4 S136  virginica   Petal   Width 2.3
-#> S137.4 S137  virginica   Petal   Width 2.4
-#> S138.4 S138  virginica   Petal   Width 1.8
-#> S139.4 S139  virginica   Petal   Width 1.8
-#> S140.4 S140  virginica   Petal   Width 2.1
-#> S141.4 S141  virginica   Petal   Width 2.4
-#> S142.4 S142  virginica   Petal   Width 2.3
-#> S143.4 S143  virginica   Petal   Width 1.9
-#> S144.4 S144  virginica   Petal   Width 2.3
-#> S145.4 S145  virginica   Petal   Width 2.5
-#> S146.4 S146  virginica   Petal   Width 2.3
-#> S147.4 S147  virginica   Petal   Width 1.9
-#> S148.4 S148  virginica   Petal   Width 2.0
-#> S149.4 S149  virginica   Petal   Width 2.3
-#> S150.4 S150  virginica   Petal   Width 1.8
      
+
      #>       id    Species Feature Measure   y
+#> 1   S001     setosa   Sepal  Length 5.1
+#> 2   S002     setosa   Sepal  Length 4.9
+#> 3   S003     setosa   Sepal  Length 4.7
+#> 4   S004     setosa   Sepal  Length 4.6
+#> 5   S005     setosa   Sepal  Length 5.0
+#> 6   S006     setosa   Sepal  Length 5.4
+#> 7   S007     setosa   Sepal  Length 4.6
+#> 8   S008     setosa   Sepal  Length 5.0
+#> 9   S009     setosa   Sepal  Length 4.4
+#> 10  S010     setosa   Sepal  Length 4.9
+#> 11  S011     setosa   Sepal  Length 5.4
+#> 12  S012     setosa   Sepal  Length 4.8
+#> 13  S013     setosa   Sepal  Length 4.8
+#> 14  S014     setosa   Sepal  Length 4.3
+#> 15  S015     setosa   Sepal  Length 5.8
+#> 16  S016     setosa   Sepal  Length 5.7
+#> 17  S017     setosa   Sepal  Length 5.4
+#> 18  S018     setosa   Sepal  Length 5.1
+#> 19  S019     setosa   Sepal  Length 5.7
+#> 20  S020     setosa   Sepal  Length 5.1
+#> 21  S021     setosa   Sepal  Length 5.4
+#> 22  S022     setosa   Sepal  Length 5.1
+#> 23  S023     setosa   Sepal  Length 4.6
+#> 24  S024     setosa   Sepal  Length 5.1
+#> 25  S025     setosa   Sepal  Length 4.8
+#> 26  S026     setosa   Sepal  Length 5.0
+#> 27  S027     setosa   Sepal  Length 5.0
+#> 28  S028     setosa   Sepal  Length 5.2
+#> 29  S029     setosa   Sepal  Length 5.2
+#> 30  S030     setosa   Sepal  Length 4.7
+#> 31  S031     setosa   Sepal  Length 4.8
+#> 32  S032     setosa   Sepal  Length 5.4
+#> 33  S033     setosa   Sepal  Length 5.2
+#> 34  S034     setosa   Sepal  Length 5.5
+#> 35  S035     setosa   Sepal  Length 4.9
+#> 36  S036     setosa   Sepal  Length 5.0
+#> 37  S037     setosa   Sepal  Length 5.5
+#> 38  S038     setosa   Sepal  Length 4.9
+#> 39  S039     setosa   Sepal  Length 4.4
+#> 40  S040     setosa   Sepal  Length 5.1
+#> 41  S041     setosa   Sepal  Length 5.0
+#> 42  S042     setosa   Sepal  Length 4.5
+#> 43  S043     setosa   Sepal  Length 4.4
+#> 44  S044     setosa   Sepal  Length 5.0
+#> 45  S045     setosa   Sepal  Length 5.1
+#> 46  S046     setosa   Sepal  Length 4.8
+#> 47  S047     setosa   Sepal  Length 5.1
+#> 48  S048     setosa   Sepal  Length 4.6
+#> 49  S049     setosa   Sepal  Length 5.3
+#> 50  S050     setosa   Sepal  Length 5.0
+#> 51  S051 versicolor   Sepal  Length 7.0
+#> 52  S052 versicolor   Sepal  Length 6.4
+#> 53  S053 versicolor   Sepal  Length 6.9
+#> 54  S054 versicolor   Sepal  Length 5.5
+#> 55  S055 versicolor   Sepal  Length 6.5
+#> 56  S056 versicolor   Sepal  Length 5.7
+#> 57  S057 versicolor   Sepal  Length 6.3
+#> 58  S058 versicolor   Sepal  Length 4.9
+#> 59  S059 versicolor   Sepal  Length 6.6
+#> 60  S060 versicolor   Sepal  Length 5.2
+#> 61  S061 versicolor   Sepal  Length 5.0
+#> 62  S062 versicolor   Sepal  Length 5.9
+#> 63  S063 versicolor   Sepal  Length 6.0
+#> 64  S064 versicolor   Sepal  Length 6.1
+#> 65  S065 versicolor   Sepal  Length 5.6
+#> 66  S066 versicolor   Sepal  Length 6.7
+#> 67  S067 versicolor   Sepal  Length 5.6
+#> 68  S068 versicolor   Sepal  Length 5.8
+#> 69  S069 versicolor   Sepal  Length 6.2
+#> 70  S070 versicolor   Sepal  Length 5.6
+#> 71  S071 versicolor   Sepal  Length 5.9
+#> 72  S072 versicolor   Sepal  Length 6.1
+#> 73  S073 versicolor   Sepal  Length 6.3
+#> 74  S074 versicolor   Sepal  Length 6.1
+#> 75  S075 versicolor   Sepal  Length 6.4
+#> 76  S076 versicolor   Sepal  Length 6.6
+#> 77  S077 versicolor   Sepal  Length 6.8
+#> 78  S078 versicolor   Sepal  Length 6.7
+#> 79  S079 versicolor   Sepal  Length 6.0
+#> 80  S080 versicolor   Sepal  Length 5.7
+#> 81  S081 versicolor   Sepal  Length 5.5
+#> 82  S082 versicolor   Sepal  Length 5.5
+#> 83  S083 versicolor   Sepal  Length 5.8
+#> 84  S084 versicolor   Sepal  Length 6.0
+#> 85  S085 versicolor   Sepal  Length 5.4
+#> 86  S086 versicolor   Sepal  Length 6.0
+#> 87  S087 versicolor   Sepal  Length 6.7
+#> 88  S088 versicolor   Sepal  Length 6.3
+#> 89  S089 versicolor   Sepal  Length 5.6
+#> 90  S090 versicolor   Sepal  Length 5.5
+#> 91  S091 versicolor   Sepal  Length 5.5
+#> 92  S092 versicolor   Sepal  Length 6.1
+#> 93  S093 versicolor   Sepal  Length 5.8
+#> 94  S094 versicolor   Sepal  Length 5.0
+#> 95  S095 versicolor   Sepal  Length 5.6
+#> 96  S096 versicolor   Sepal  Length 5.7
+#> 97  S097 versicolor   Sepal  Length 5.7
+#> 98  S098 versicolor   Sepal  Length 6.2
+#> 99  S099 versicolor   Sepal  Length 5.1
+#> 100 S100 versicolor   Sepal  Length 5.7
+#> 101 S101  virginica   Sepal  Length 6.3
+#> 102 S102  virginica   Sepal  Length 5.8
+#> 103 S103  virginica   Sepal  Length 7.1
+#> 104 S104  virginica   Sepal  Length 6.3
+#> 105 S105  virginica   Sepal  Length 6.5
+#> 106 S106  virginica   Sepal  Length 7.6
+#> 107 S107  virginica   Sepal  Length 4.9
+#> 108 S108  virginica   Sepal  Length 7.3
+#> 109 S109  virginica   Sepal  Length 6.7
+#> 110 S110  virginica   Sepal  Length 7.2
+#> 111 S111  virginica   Sepal  Length 6.5
+#> 112 S112  virginica   Sepal  Length 6.4
+#> 113 S113  virginica   Sepal  Length 6.8
+#> 114 S114  virginica   Sepal  Length 5.7
+#> 115 S115  virginica   Sepal  Length 5.8
+#> 116 S116  virginica   Sepal  Length 6.4
+#> 117 S117  virginica   Sepal  Length 6.5
+#> 118 S118  virginica   Sepal  Length 7.7
+#> 119 S119  virginica   Sepal  Length 7.7
+#> 120 S120  virginica   Sepal  Length 6.0
+#> 121 S121  virginica   Sepal  Length 6.9
+#> 122 S122  virginica   Sepal  Length 5.6
+#> 123 S123  virginica   Sepal  Length 7.7
+#> 124 S124  virginica   Sepal  Length 6.3
+#> 125 S125  virginica   Sepal  Length 6.7
+#> 126 S126  virginica   Sepal  Length 7.2
+#> 127 S127  virginica   Sepal  Length 6.2
+#> 128 S128  virginica   Sepal  Length 6.1
+#> 129 S129  virginica   Sepal  Length 6.4
+#> 130 S130  virginica   Sepal  Length 7.2
+#> 131 S131  virginica   Sepal  Length 7.4
+#> 132 S132  virginica   Sepal  Length 7.9
+#> 133 S133  virginica   Sepal  Length 6.4
+#> 134 S134  virginica   Sepal  Length 6.3
+#> 135 S135  virginica   Sepal  Length 6.1
+#> 136 S136  virginica   Sepal  Length 7.7
+#> 137 S137  virginica   Sepal  Length 6.3
+#> 138 S138  virginica   Sepal  Length 6.4
+#> 139 S139  virginica   Sepal  Length 6.0
+#> 140 S140  virginica   Sepal  Length 6.9
+#> 141 S141  virginica   Sepal  Length 6.7
+#> 142 S142  virginica   Sepal  Length 6.9
+#> 143 S143  virginica   Sepal  Length 5.8
+#> 144 S144  virginica   Sepal  Length 6.8
+#> 145 S145  virginica   Sepal  Length 6.7
+#> 146 S146  virginica   Sepal  Length 6.7
+#> 147 S147  virginica   Sepal  Length 6.3
+#> 148 S148  virginica   Sepal  Length 6.5
+#> 149 S149  virginica   Sepal  Length 6.2
+#> 150 S150  virginica   Sepal  Length 5.9
+#> 151 S001     setosa   Sepal   Width 3.5
+#> 152 S002     setosa   Sepal   Width 3.0
+#> 153 S003     setosa   Sepal   Width 3.2
+#> 154 S004     setosa   Sepal   Width 3.1
+#> 155 S005     setosa   Sepal   Width 3.6
+#> 156 S006     setosa   Sepal   Width 3.9
+#> 157 S007     setosa   Sepal   Width 3.4
+#> 158 S008     setosa   Sepal   Width 3.4
+#> 159 S009     setosa   Sepal   Width 2.9
+#> 160 S010     setosa   Sepal   Width 3.1
+#> 161 S011     setosa   Sepal   Width 3.7
+#> 162 S012     setosa   Sepal   Width 3.4
+#> 163 S013     setosa   Sepal   Width 3.0
+#> 164 S014     setosa   Sepal   Width 3.0
+#> 165 S015     setosa   Sepal   Width 4.0
+#> 166 S016     setosa   Sepal   Width 4.4
+#> 167 S017     setosa   Sepal   Width 3.9
+#> 168 S018     setosa   Sepal   Width 3.5
+#> 169 S019     setosa   Sepal   Width 3.8
+#> 170 S020     setosa   Sepal   Width 3.8
+#> 171 S021     setosa   Sepal   Width 3.4
+#> 172 S022     setosa   Sepal   Width 3.7
+#> 173 S023     setosa   Sepal   Width 3.6
+#> 174 S024     setosa   Sepal   Width 3.3
+#> 175 S025     setosa   Sepal   Width 3.4
+#> 176 S026     setosa   Sepal   Width 3.0
+#> 177 S027     setosa   Sepal   Width 3.4
+#> 178 S028     setosa   Sepal   Width 3.5
+#> 179 S029     setosa   Sepal   Width 3.4
+#> 180 S030     setosa   Sepal   Width 3.2
+#> 181 S031     setosa   Sepal   Width 3.1
+#> 182 S032     setosa   Sepal   Width 3.4
+#> 183 S033     setosa   Sepal   Width 4.1
+#> 184 S034     setosa   Sepal   Width 4.2
+#> 185 S035     setosa   Sepal   Width 3.1
+#> 186 S036     setosa   Sepal   Width 3.2
+#> 187 S037     setosa   Sepal   Width 3.5
+#> 188 S038     setosa   Sepal   Width 3.6
+#> 189 S039     setosa   Sepal   Width 3.0
+#> 190 S040     setosa   Sepal   Width 3.4
+#> 191 S041     setosa   Sepal   Width 3.5
+#> 192 S042     setosa   Sepal   Width 2.3
+#> 193 S043     setosa   Sepal   Width 3.2
+#> 194 S044     setosa   Sepal   Width 3.5
+#> 195 S045     setosa   Sepal   Width 3.8
+#> 196 S046     setosa   Sepal   Width 3.0
+#> 197 S047     setosa   Sepal   Width 3.8
+#> 198 S048     setosa   Sepal   Width 3.2
+#> 199 S049     setosa   Sepal   Width 3.7
+#> 200 S050     setosa   Sepal   Width 3.3
+#> 201 S051 versicolor   Sepal   Width 3.2
+#> 202 S052 versicolor   Sepal   Width 3.2
+#> 203 S053 versicolor   Sepal   Width 3.1
+#> 204 S054 versicolor   Sepal   Width 2.3
+#> 205 S055 versicolor   Sepal   Width 2.8
+#> 206 S056 versicolor   Sepal   Width 2.8
+#> 207 S057 versicolor   Sepal   Width 3.3
+#> 208 S058 versicolor   Sepal   Width 2.4
+#> 209 S059 versicolor   Sepal   Width 2.9
+#> 210 S060 versicolor   Sepal   Width 2.7
+#> 211 S061 versicolor   Sepal   Width 2.0
+#> 212 S062 versicolor   Sepal   Width 3.0
+#> 213 S063 versicolor   Sepal   Width 2.2
+#> 214 S064 versicolor   Sepal   Width 2.9
+#> 215 S065 versicolor   Sepal   Width 2.9
+#> 216 S066 versicolor   Sepal   Width 3.1
+#> 217 S067 versicolor   Sepal   Width 3.0
+#> 218 S068 versicolor   Sepal   Width 2.7
+#> 219 S069 versicolor   Sepal   Width 2.2
+#> 220 S070 versicolor   Sepal   Width 2.5
+#> 221 S071 versicolor   Sepal   Width 3.2
+#> 222 S072 versicolor   Sepal   Width 2.8
+#> 223 S073 versicolor   Sepal   Width 2.5
+#> 224 S074 versicolor   Sepal   Width 2.8
+#> 225 S075 versicolor   Sepal   Width 2.9
+#> 226 S076 versicolor   Sepal   Width 3.0
+#> 227 S077 versicolor   Sepal   Width 2.8
+#> 228 S078 versicolor   Sepal   Width 3.0
+#> 229 S079 versicolor   Sepal   Width 2.9
+#> 230 S080 versicolor   Sepal   Width 2.6
+#> 231 S081 versicolor   Sepal   Width 2.4
+#> 232 S082 versicolor   Sepal   Width 2.4
+#> 233 S083 versicolor   Sepal   Width 2.7
+#> 234 S084 versicolor   Sepal   Width 2.7
+#> 235 S085 versicolor   Sepal   Width 3.0
+#> 236 S086 versicolor   Sepal   Width 3.4
+#> 237 S087 versicolor   Sepal   Width 3.1
+#> 238 S088 versicolor   Sepal   Width 2.3
+#> 239 S089 versicolor   Sepal   Width 3.0
+#> 240 S090 versicolor   Sepal   Width 2.5
+#> 241 S091 versicolor   Sepal   Width 2.6
+#> 242 S092 versicolor   Sepal   Width 3.0
+#> 243 S093 versicolor   Sepal   Width 2.6
+#> 244 S094 versicolor   Sepal   Width 2.3
+#> 245 S095 versicolor   Sepal   Width 2.7
+#> 246 S096 versicolor   Sepal   Width 3.0
+#> 247 S097 versicolor   Sepal   Width 2.9
+#> 248 S098 versicolor   Sepal   Width 2.9
+#> 249 S099 versicolor   Sepal   Width 2.5
+#> 250 S100 versicolor   Sepal   Width 2.8
+#> 251 S101  virginica   Sepal   Width 3.3
+#> 252 S102  virginica   Sepal   Width 2.7
+#> 253 S103  virginica   Sepal   Width 3.0
+#> 254 S104  virginica   Sepal   Width 2.9
+#> 255 S105  virginica   Sepal   Width 3.0
+#> 256 S106  virginica   Sepal   Width 3.0
+#> 257 S107  virginica   Sepal   Width 2.5
+#> 258 S108  virginica   Sepal   Width 2.9
+#> 259 S109  virginica   Sepal   Width 2.5
+#> 260 S110  virginica   Sepal   Width 3.6
+#> 261 S111  virginica   Sepal   Width 3.2
+#> 262 S112  virginica   Sepal   Width 2.7
+#> 263 S113  virginica   Sepal   Width 3.0
+#> 264 S114  virginica   Sepal   Width 2.5
+#> 265 S115  virginica   Sepal   Width 2.8
+#> 266 S116  virginica   Sepal   Width 3.2
+#> 267 S117  virginica   Sepal   Width 3.0
+#> 268 S118  virginica   Sepal   Width 3.8
+#> 269 S119  virginica   Sepal   Width 2.6
+#> 270 S120  virginica   Sepal   Width 2.2
+#> 271 S121  virginica   Sepal   Width 3.2
+#> 272 S122  virginica   Sepal   Width 2.8
+#> 273 S123  virginica   Sepal   Width 2.8
+#> 274 S124  virginica   Sepal   Width 2.7
+#> 275 S125  virginica   Sepal   Width 3.3
+#> 276 S126  virginica   Sepal   Width 3.2
+#> 277 S127  virginica   Sepal   Width 2.8
+#> 278 S128  virginica   Sepal   Width 3.0
+#> 279 S129  virginica   Sepal   Width 2.8
+#> 280 S130  virginica   Sepal   Width 3.0
+#> 281 S131  virginica   Sepal   Width 2.8
+#> 282 S132  virginica   Sepal   Width 3.8
+#> 283 S133  virginica   Sepal   Width 2.8
+#> 284 S134  virginica   Sepal   Width 2.8
+#> 285 S135  virginica   Sepal   Width 2.6
+#> 286 S136  virginica   Sepal   Width 3.0
+#> 287 S137  virginica   Sepal   Width 3.4
+#> 288 S138  virginica   Sepal   Width 3.1
+#> 289 S139  virginica   Sepal   Width 3.0
+#> 290 S140  virginica   Sepal   Width 3.1
+#> 291 S141  virginica   Sepal   Width 3.1
+#> 292 S142  virginica   Sepal   Width 3.1
+#> 293 S143  virginica   Sepal   Width 2.7
+#> 294 S144  virginica   Sepal   Width 3.2
+#> 295 S145  virginica   Sepal   Width 3.3
+#> 296 S146  virginica   Sepal   Width 3.0
+#> 297 S147  virginica   Sepal   Width 2.5
+#> 298 S148  virginica   Sepal   Width 3.0
+#> 299 S149  virginica   Sepal   Width 3.4
+#> 300 S150  virginica   Sepal   Width 3.0
+#> 301 S001     setosa   Petal  Length 1.4
+#> 302 S002     setosa   Petal  Length 1.4
+#> 303 S003     setosa   Petal  Length 1.3
+#> 304 S004     setosa   Petal  Length 1.5
+#> 305 S005     setosa   Petal  Length 1.4
+#> 306 S006     setosa   Petal  Length 1.7
+#> 307 S007     setosa   Petal  Length 1.4
+#> 308 S008     setosa   Petal  Length 1.5
+#> 309 S009     setosa   Petal  Length 1.4
+#> 310 S010     setosa   Petal  Length 1.5
+#> 311 S011     setosa   Petal  Length 1.5
+#> 312 S012     setosa   Petal  Length 1.6
+#> 313 S013     setosa   Petal  Length 1.4
+#> 314 S014     setosa   Petal  Length 1.1
+#> 315 S015     setosa   Petal  Length 1.2
+#> 316 S016     setosa   Petal  Length 1.5
+#> 317 S017     setosa   Petal  Length 1.3
+#> 318 S018     setosa   Petal  Length 1.4
+#> 319 S019     setosa   Petal  Length 1.7
+#> 320 S020     setosa   Petal  Length 1.5
+#> 321 S021     setosa   Petal  Length 1.7
+#> 322 S022     setosa   Petal  Length 1.5
+#> 323 S023     setosa   Petal  Length 1.0
+#> 324 S024     setosa   Petal  Length 1.7
+#> 325 S025     setosa   Petal  Length 1.9
+#> 326 S026     setosa   Petal  Length 1.6
+#> 327 S027     setosa   Petal  Length 1.6
+#> 328 S028     setosa   Petal  Length 1.5
+#> 329 S029     setosa   Petal  Length 1.4
+#> 330 S030     setosa   Petal  Length 1.6
+#> 331 S031     setosa   Petal  Length 1.6
+#> 332 S032     setosa   Petal  Length 1.5
+#> 333 S033     setosa   Petal  Length 1.5
+#> 334 S034     setosa   Petal  Length 1.4
+#> 335 S035     setosa   Petal  Length 1.5
+#> 336 S036     setosa   Petal  Length 1.2
+#> 337 S037     setosa   Petal  Length 1.3
+#> 338 S038     setosa   Petal  Length 1.4
+#> 339 S039     setosa   Petal  Length 1.3
+#> 340 S040     setosa   Petal  Length 1.5
+#> 341 S041     setosa   Petal  Length 1.3
+#> 342 S042     setosa   Petal  Length 1.3
+#> 343 S043     setosa   Petal  Length 1.3
+#> 344 S044     setosa   Petal  Length 1.6
+#> 345 S045     setosa   Petal  Length 1.9
+#> 346 S046     setosa   Petal  Length 1.4
+#> 347 S047     setosa   Petal  Length 1.6
+#> 348 S048     setosa   Petal  Length 1.4
+#> 349 S049     setosa   Petal  Length 1.5
+#> 350 S050     setosa   Petal  Length 1.4
+#> 351 S051 versicolor   Petal  Length 4.7
+#> 352 S052 versicolor   Petal  Length 4.5
+#> 353 S053 versicolor   Petal  Length 4.9
+#> 354 S054 versicolor   Petal  Length 4.0
+#> 355 S055 versicolor   Petal  Length 4.6
+#> 356 S056 versicolor   Petal  Length 4.5
+#> 357 S057 versicolor   Petal  Length 4.7
+#> 358 S058 versicolor   Petal  Length 3.3
+#> 359 S059 versicolor   Petal  Length 4.6
+#> 360 S060 versicolor   Petal  Length 3.9
+#> 361 S061 versicolor   Petal  Length 3.5
+#> 362 S062 versicolor   Petal  Length 4.2
+#> 363 S063 versicolor   Petal  Length 4.0
+#> 364 S064 versicolor   Petal  Length 4.7
+#> 365 S065 versicolor   Petal  Length 3.6
+#> 366 S066 versicolor   Petal  Length 4.4
+#> 367 S067 versicolor   Petal  Length 4.5
+#> 368 S068 versicolor   Petal  Length 4.1
+#> 369 S069 versicolor   Petal  Length 4.5
+#> 370 S070 versicolor   Petal  Length 3.9
+#> 371 S071 versicolor   Petal  Length 4.8
+#> 372 S072 versicolor   Petal  Length 4.0
+#> 373 S073 versicolor   Petal  Length 4.9
+#> 374 S074 versicolor   Petal  Length 4.7
+#> 375 S075 versicolor   Petal  Length 4.3
+#> 376 S076 versicolor   Petal  Length 4.4
+#> 377 S077 versicolor   Petal  Length 4.8
+#> 378 S078 versicolor   Petal  Length 5.0
+#> 379 S079 versicolor   Petal  Length 4.5
+#> 380 S080 versicolor   Petal  Length 3.5
+#> 381 S081 versicolor   Petal  Length 3.8
+#> 382 S082 versicolor   Petal  Length 3.7
+#> 383 S083 versicolor   Petal  Length 3.9
+#> 384 S084 versicolor   Petal  Length 5.1
+#> 385 S085 versicolor   Petal  Length 4.5
+#> 386 S086 versicolor   Petal  Length 4.5
+#> 387 S087 versicolor   Petal  Length 4.7
+#> 388 S088 versicolor   Petal  Length 4.4
+#> 389 S089 versicolor   Petal  Length 4.1
+#> 390 S090 versicolor   Petal  Length 4.0
+#> 391 S091 versicolor   Petal  Length 4.4
+#> 392 S092 versicolor   Petal  Length 4.6
+#> 393 S093 versicolor   Petal  Length 4.0
+#> 394 S094 versicolor   Petal  Length 3.3
+#> 395 S095 versicolor   Petal  Length 4.2
+#> 396 S096 versicolor   Petal  Length 4.2
+#> 397 S097 versicolor   Petal  Length 4.2
+#> 398 S098 versicolor   Petal  Length 4.3
+#> 399 S099 versicolor   Petal  Length 3.0
+#> 400 S100 versicolor   Petal  Length 4.1
+#> 401 S101  virginica   Petal  Length 6.0
+#> 402 S102  virginica   Petal  Length 5.1
+#> 403 S103  virginica   Petal  Length 5.9
+#> 404 S104  virginica   Petal  Length 5.6
+#> 405 S105  virginica   Petal  Length 5.8
+#> 406 S106  virginica   Petal  Length 6.6
+#> 407 S107  virginica   Petal  Length 4.5
+#> 408 S108  virginica   Petal  Length 6.3
+#> 409 S109  virginica   Petal  Length 5.8
+#> 410 S110  virginica   Petal  Length 6.1
+#> 411 S111  virginica   Petal  Length 5.1
+#> 412 S112  virginica   Petal  Length 5.3
+#> 413 S113  virginica   Petal  Length 5.5
+#> 414 S114  virginica   Petal  Length 5.0
+#> 415 S115  virginica   Petal  Length 5.1
+#> 416 S116  virginica   Petal  Length 5.3
+#> 417 S117  virginica   Petal  Length 5.5
+#> 418 S118  virginica   Petal  Length 6.7
+#> 419 S119  virginica   Petal  Length 6.9
+#> 420 S120  virginica   Petal  Length 5.0
+#> 421 S121  virginica   Petal  Length 5.7
+#> 422 S122  virginica   Petal  Length 4.9
+#> 423 S123  virginica   Petal  Length 6.7
+#> 424 S124  virginica   Petal  Length 4.9
+#> 425 S125  virginica   Petal  Length 5.7
+#> 426 S126  virginica   Petal  Length 6.0
+#> 427 S127  virginica   Petal  Length 4.8
+#> 428 S128  virginica   Petal  Length 4.9
+#> 429 S129  virginica   Petal  Length 5.6
+#> 430 S130  virginica   Petal  Length 5.8
+#> 431 S131  virginica   Petal  Length 6.1
+#> 432 S132  virginica   Petal  Length 6.4
+#> 433 S133  virginica   Petal  Length 5.6
+#> 434 S134  virginica   Petal  Length 5.1
+#> 435 S135  virginica   Petal  Length 5.6
+#> 436 S136  virginica   Petal  Length 6.1
+#> 437 S137  virginica   Petal  Length 5.6
+#> 438 S138  virginica   Petal  Length 5.5
+#> 439 S139  virginica   Petal  Length 4.8
+#> 440 S140  virginica   Petal  Length 5.4
+#> 441 S141  virginica   Petal  Length 5.6
+#> 442 S142  virginica   Petal  Length 5.1
+#> 443 S143  virginica   Petal  Length 5.1
+#> 444 S144  virginica   Petal  Length 5.9
+#> 445 S145  virginica   Petal  Length 5.7
+#> 446 S146  virginica   Petal  Length 5.2
+#> 447 S147  virginica   Petal  Length 5.0
+#> 448 S148  virginica   Petal  Length 5.2
+#> 449 S149  virginica   Petal  Length 5.4
+#> 450 S150  virginica   Petal  Length 5.1
+#> 451 S001     setosa   Petal   Width 0.2
+#> 452 S002     setosa   Petal   Width 0.2
+#> 453 S003     setosa   Petal   Width 0.2
+#> 454 S004     setosa   Petal   Width 0.2
+#> 455 S005     setosa   Petal   Width 0.2
+#> 456 S006     setosa   Petal   Width 0.4
+#> 457 S007     setosa   Petal   Width 0.3
+#> 458 S008     setosa   Petal   Width 0.2
+#> 459 S009     setosa   Petal   Width 0.2
+#> 460 S010     setosa   Petal   Width 0.1
+#> 461 S011     setosa   Petal   Width 0.2
+#> 462 S012     setosa   Petal   Width 0.2
+#> 463 S013     setosa   Petal   Width 0.1
+#> 464 S014     setosa   Petal   Width 0.1
+#> 465 S015     setosa   Petal   Width 0.2
+#> 466 S016     setosa   Petal   Width 0.4
+#> 467 S017     setosa   Petal   Width 0.4
+#> 468 S018     setosa   Petal   Width 0.3
+#> 469 S019     setosa   Petal   Width 0.3
+#> 470 S020     setosa   Petal   Width 0.3
+#> 471 S021     setosa   Petal   Width 0.2
+#> 472 S022     setosa   Petal   Width 0.4
+#> 473 S023     setosa   Petal   Width 0.2
+#> 474 S024     setosa   Petal   Width 0.5
+#> 475 S025     setosa   Petal   Width 0.2
+#> 476 S026     setosa   Petal   Width 0.2
+#> 477 S027     setosa   Petal   Width 0.4
+#> 478 S028     setosa   Petal   Width 0.2
+#> 479 S029     setosa   Petal   Width 0.2
+#> 480 S030     setosa   Petal   Width 0.2
+#> 481 S031     setosa   Petal   Width 0.2
+#> 482 S032     setosa   Petal   Width 0.4
+#> 483 S033     setosa   Petal   Width 0.1
+#> 484 S034     setosa   Petal   Width 0.2
+#> 485 S035     setosa   Petal   Width 0.2
+#> 486 S036     setosa   Petal   Width 0.2
+#> 487 S037     setosa   Petal   Width 0.2
+#> 488 S038     setosa   Petal   Width 0.1
+#> 489 S039     setosa   Petal   Width 0.2
+#> 490 S040     setosa   Petal   Width 0.2
+#> 491 S041     setosa   Petal   Width 0.3
+#> 492 S042     setosa   Petal   Width 0.3
+#> 493 S043     setosa   Petal   Width 0.2
+#> 494 S044     setosa   Petal   Width 0.6
+#> 495 S045     setosa   Petal   Width 0.4
+#> 496 S046     setosa   Petal   Width 0.3
+#> 497 S047     setosa   Petal   Width 0.2
+#> 498 S048     setosa   Petal   Width 0.2
+#> 499 S049     setosa   Petal   Width 0.2
+#> 500 S050     setosa   Petal   Width 0.2
+#> 501 S051 versicolor   Petal   Width 1.4
+#> 502 S052 versicolor   Petal   Width 1.5
+#> 503 S053 versicolor   Petal   Width 1.5
+#> 504 S054 versicolor   Petal   Width 1.3
+#> 505 S055 versicolor   Petal   Width 1.5
+#> 506 S056 versicolor   Petal   Width 1.3
+#> 507 S057 versicolor   Petal   Width 1.6
+#> 508 S058 versicolor   Petal   Width 1.0
+#> 509 S059 versicolor   Petal   Width 1.3
+#> 510 S060 versicolor   Petal   Width 1.4
+#> 511 S061 versicolor   Petal   Width 1.0
+#> 512 S062 versicolor   Petal   Width 1.5
+#> 513 S063 versicolor   Petal   Width 1.0
+#> 514 S064 versicolor   Petal   Width 1.4
+#> 515 S065 versicolor   Petal   Width 1.3
+#> 516 S066 versicolor   Petal   Width 1.4
+#> 517 S067 versicolor   Petal   Width 1.5
+#> 518 S068 versicolor   Petal   Width 1.0
+#> 519 S069 versicolor   Petal   Width 1.5
+#> 520 S070 versicolor   Petal   Width 1.1
+#> 521 S071 versicolor   Petal   Width 1.8
+#> 522 S072 versicolor   Petal   Width 1.3
+#> 523 S073 versicolor   Petal   Width 1.5
+#> 524 S074 versicolor   Petal   Width 1.2
+#> 525 S075 versicolor   Petal   Width 1.3
+#> 526 S076 versicolor   Petal   Width 1.4
+#> 527 S077 versicolor   Petal   Width 1.4
+#> 528 S078 versicolor   Petal   Width 1.7
+#> 529 S079 versicolor   Petal   Width 1.5
+#> 530 S080 versicolor   Petal   Width 1.0
+#> 531 S081 versicolor   Petal   Width 1.1
+#> 532 S082 versicolor   Petal   Width 1.0
+#> 533 S083 versicolor   Petal   Width 1.2
+#> 534 S084 versicolor   Petal   Width 1.6
+#> 535 S085 versicolor   Petal   Width 1.5
+#> 536 S086 versicolor   Petal   Width 1.6
+#> 537 S087 versicolor   Petal   Width 1.5
+#> 538 S088 versicolor   Petal   Width 1.3
+#> 539 S089 versicolor   Petal   Width 1.3
+#> 540 S090 versicolor   Petal   Width 1.3
+#> 541 S091 versicolor   Petal   Width 1.2
+#> 542 S092 versicolor   Petal   Width 1.4
+#> 543 S093 versicolor   Petal   Width 1.2
+#> 544 S094 versicolor   Petal   Width 1.0
+#> 545 S095 versicolor   Petal   Width 1.3
+#> 546 S096 versicolor   Petal   Width 1.2
+#> 547 S097 versicolor   Petal   Width 1.3
+#> 548 S098 versicolor   Petal   Width 1.3
+#> 549 S099 versicolor   Petal   Width 1.1
+#> 550 S100 versicolor   Petal   Width 1.3
+#> 551 S101  virginica   Petal   Width 2.5
+#> 552 S102  virginica   Petal   Width 1.9
+#> 553 S103  virginica   Petal   Width 2.1
+#> 554 S104  virginica   Petal   Width 1.8
+#> 555 S105  virginica   Petal   Width 2.2
+#> 556 S106  virginica   Petal   Width 2.1
+#> 557 S107  virginica   Petal   Width 1.7
+#> 558 S108  virginica   Petal   Width 1.8
+#> 559 S109  virginica   Petal   Width 1.8
+#> 560 S110  virginica   Petal   Width 2.5
+#> 561 S111  virginica   Petal   Width 2.0
+#> 562 S112  virginica   Petal   Width 1.9
+#> 563 S113  virginica   Petal   Width 2.1
+#> 564 S114  virginica   Petal   Width 2.0
+#> 565 S115  virginica   Petal   Width 2.4
+#> 566 S116  virginica   Petal   Width 2.3
+#> 567 S117  virginica   Petal   Width 1.8
+#> 568 S118  virginica   Petal   Width 2.2
+#> 569 S119  virginica   Petal   Width 2.3
+#> 570 S120  virginica   Petal   Width 1.5
+#> 571 S121  virginica   Petal   Width 2.3
+#> 572 S122  virginica   Petal   Width 2.0
+#> 573 S123  virginica   Petal   Width 2.0
+#> 574 S124  virginica   Petal   Width 1.8
+#> 575 S125  virginica   Petal   Width 2.1
+#> 576 S126  virginica   Petal   Width 1.8
+#> 577 S127  virginica   Petal   Width 1.8
+#> 578 S128  virginica   Petal   Width 1.8
+#> 579 S129  virginica   Petal   Width 2.1
+#> 580 S130  virginica   Petal   Width 1.6
+#> 581 S131  virginica   Petal   Width 1.9
+#> 582 S132  virginica   Petal   Width 2.0
+#> 583 S133  virginica   Petal   Width 2.2
+#> 584 S134  virginica   Petal   Width 1.5
+#> 585 S135  virginica   Petal   Width 1.4
+#> 586 S136  virginica   Petal   Width 2.3
+#> 587 S137  virginica   Petal   Width 2.4
+#> 588 S138  virginica   Petal   Width 1.8
+#> 589 S139  virginica   Petal   Width 1.8
+#> 590 S140  virginica   Petal   Width 2.1
+#> 591 S141  virginica   Petal   Width 2.4
+#> 592 S142  virginica   Petal   Width 2.3
+#> 593 S143  virginica   Petal   Width 1.9
+#> 594 S144  virginica   Petal   Width 2.3
+#> 595 S145  virginica   Petal   Width 2.5
+#> 596 S146  virginica   Petal   Width 2.3
+#> 597 S147  virginica   Petal   Width 1.9
+#> 598 S148  virginica   Petal   Width 2.0
+#> 599 S149  virginica   Petal   Width 2.3
+#> 600 S150  virginica   Petal   Width 1.8
      • - + - + @@ -294,17 +292,17 @@

      Simulate data for a mixed design

      - + - + - +
      rep

      the number of data frames to return (default 1); if greater than 1, the returned data frame is nested by rep
      nested

      Whether to nest data frames by rep if rep > 1

      seed

      DEPRECATED use set.seed() instead before running this function
      r

      the correlations among the variables (can be a single number, full correlation matrix as a matric or vector, or a vector of the upper right triangle of the correlation matrix

      the correlations among the variables (can be a single number, full correlation matrix as a matrix or vector, or a vector of the upper right triangle of the correlation matrix
      empiricalid

      the name of the id column (defaults to id)
      vardesc

      a list of variable descriptions having the names of the within- and between-subject factors

      plot

      whether to show a plot of the design

      design

      a design list including within, between, n, mu, sd, r, dv, id

      a design list including within, between, n, mu, sd, r, dv, id, and vardesc
      rep

      the number of data frames to return (default 1); if greater than 1, the returned data frame is nested by rep

      the number of data frames to return (default 1); if greater than 1, the returned data frame is nested by rep (if nested = TRUE)

      nested

      Whether to nest data frames by rep if rep > 1
      seed
      private M6566
      private F6554
      private
      public M171186
      public F174187
      public NB48
      diff --git a/pkgdown/index.md b/pkgdown/index.md index 0737ba8b..dce47abc 100644 --- a/pkgdown/index.md +++ b/pkgdown/index.md @@ -19,21 +19,19 @@ It is useful to be able to simulate data with a specified structure. The `faux` ## Installation -You can install the released version of faux (1.0.0) from [CRAN](https://CRAN.R-project.org) with: +You can install the released version of faux from [CRAN](https://CRAN.R-project.org) with: ``` r install.packages("faux") ``` -And the development version (1.0.0.9006) from [GitHub](https://github.com/debruine/faux) with: +And the development version from [GitHub](https://github.com/debruine/faux) with: ``` r # install.packages("devtools") devtools::install_github("debruine/faux") ``` -See the [development version manual](https://debruine.github.io/faux/dev/). - ## Quick overview ### Simulate data for a factorial design @@ -103,12 +101,12 @@ data <- add_random(class = 20) %>% |school_type |gender | n| |:-----------|:------|---:| -|private |M | 65| -|private |F | 65| +|private |M | 66| +|private |F | 54| |private |NB | 1| -|public |M | 171| -|public |F | 174| -|public |NB | 4| +|public |M | 186| +|public |F | 187| +|public |NB | 8| diff --git a/pkgdown/man/figures/plot-design-1.png b/pkgdown/man/figures/plot-design-1.png index c467d03b..2ed69780 100644 Binary files a/pkgdown/man/figures/plot-design-1.png and b/pkgdown/man/figures/plot-design-1.png differ diff --git a/pkgdown/man/figures/plot-iris-sim-1.png b/pkgdown/man/figures/plot-iris-sim-1.png index 1c06395b..cc3930ff 100644 Binary files a/pkgdown/man/figures/plot-iris-sim-1.png and b/pkgdown/man/figures/plot-iris-sim-1.png differ diff --git a/vignettes/random_reports.Rmd b/vignettes/random_reports.Rmd index 561b4edf..039f73c9 100644 --- a/vignettes/random_reports.Rmd +++ b/vignettes/random_reports.Rmd @@ -17,13 +17,13 @@ knitr::opts_chunk$set( library(kableExtra) ``` -Have you ever wanted to create unique versions of datasets for each student in a class? This vignette walks through the proess of setting up a simple study with a subject table and a questionnaire table. +Have you ever wanted to create unique versions of datasets for each student in a class? This vignette walks through the process of setting up a simple study with a subject table and a questionnaire table. The subjects have varying demographic attributes and are assigned to a counterbalanced condition and version of the study. Some of the demographic data is missing. The questionnaire has a variable number of questions, answered on a 7-point Likert scale. Some of the questions are reverse-coded, indicated by the column names. There are some missing observations. -The data is set up with fixed effects for condition, version, and subject language that you can vary between students. The DV is created using a mixed effects model formula to simulate realistic random interpects and slopes, so these data would be suitable for analysis with mixed effects models, or you can instruct students to aggregate the questionnaire data to analyse with t-tests, correlations, and ANOVAs. +The data is set up with fixed effects for condition, version, and subject language that you can vary between students. The DV is created using a mixed effects model formula to simulate realistic random intercepts and slopes, so these data would be suitable for analysis with mixed effects models, or you can instruct students to aggregate the questionnaire data to analyse with single-level models. A custom assignment is created for each student, where they are told the questionnaire contains scores on one of three topics, and are asked to determine if there is an effect of one factor for a subgroup of another factor. The categories chosen here are pretty arbitrary, but you can change this to make sense for your subject area. @@ -32,10 +32,10 @@ Finally, the subject table, questionnaire table, and instructions are saved to a ## Setup -Some of the functions below currently require the development version of faux (`add_random()`, `add_between()`, `add_ranef()` and `add_contrast()`). I am working on submitting the new version to CRAN, so any feedback would be welcome. +Some of the functions below require version 1.1.0 of faux (`add_random()`, `add_between()`, `add_ranef()` and `add_contrast()`). This version has been submitted to CRAN, and is available on github. Any feedback would be welcome. ```{r setup, message=FALSE} -# devtools::install_github("debruine/faux", ref = "dev") +# devtools::install_github("debruine/faux") library(faux) library(dplyr) library(tidyr) @@ -46,17 +46,22 @@ library(glue) theme_set(theme_minimal()) ``` -Start your script by setting a seed that is unique to each student. Below is a useful pattern to generate a seed from a student ID, even if it contains letters. +Start your script by setting a seed that is unique to each student. Below is a useful function you can use to generate a seed from a student ID, even if it contains letters. ```{r} -student_id <- "8675309J" +seed_from_string <- function(seed) { + if (is.numeric(seed)) return(set.seed(seed)) + + # otherwise set from characters + seed %>% + openssl::md5() %>% # turn into something with lots of numbers + gsub("[a-z]", "", .) %>% # get rid of letters + substr(1, 8) %>% # select the first 8 numbers + set.seed() +} -student_id %>% - as.character() %>% # has to be a character string to start - openssl::md5() %>% # turn into something with lots of numbers - gsub("[a-z]", "", .) %>% # get rid of letters - substr(1, 8) %>% # select the first 8 numbers - set.seed() +student_id <- "8675309J" +seed_from_string(student_id) ``` @@ -419,9 +424,10 @@ I sample a topic from "stress", "extroversion", and "disgust". You can choose re I also sample an alpha level and power for the power calculation aspect of the assessment. This reinforces a lesson I want to teach about alpha = 0.5 and target power = 0.8 being mindless defaults that we should question. [Justify everything](https://osf.io/j4s3c/). ```{r} -factors <- sample(c("language", "condition", "version"), 2) -iv <- factors[[1]] -subset <- factors[[2]] +factors <- c("language", "condition", "version") +two_factors <- sample(factors, 2) +iv <- two_factors[[1]] +subset <- two_factors[[2]] level <- subjects[[subset]] %>% unique() %>% sample(1) topic <- sample(c("stress", "extroversion", "disgust"), 1) alpha <- sample(c(.005, .01, .05), 1) @@ -431,18 +437,40 @@ power <- sample(c(80, 85, 90), 1) This will produce the following text. ```{r} -research_question <- readLines("template_instructions.txt") %>% +instructions <- readLines("template_instructions.txt") %>% paste(collapse = "\n") %>% glue::glue() ```
      -`r research_question` +`r instructions`
      +### Instructions function + +As before, wrap this code in a function. + +```{r} +make_instructions <- function(template, + factors = c("language", "condition", "version")) { + two_factors <- sample(factors, 2) + iv <- two_factors[[1]] + subset <- two_factors[[2]] + level <- subjects[[subset]] %>% unique() %>% sample(1) + topic <- sample(c("stress", "extroversion", "disgust"), 1) + alpha <- sample(c(.005, .01, .05), 1) + power <- sample(c(80, 85, 90), 1) + + readLines(template) %>% + paste(collapse = "\n") %>% + glue::glue() +} +``` + + ## Save files Now you need to create a directory for the student's files and save the subject table, question table, and instructions. I'm saving the files in a directory that contains the student ID so I can upload them to Moodle easily. @@ -452,7 +480,7 @@ dir <- paste0("exam_", student_id) dir.create(dir, showWarnings = FALSE) readr::write_csv(subjects, file.path(dir, "subjects.csv")) readr::write_csv(questions, file.path(dir, "questions.csv")) -write(research_question, file.path(dir, "instructions.txt")) +write(instructions, file.path(dir, "instructions.txt")) ``` @@ -463,12 +491,7 @@ Once you've debugged your code above, put each step inside a function with the a ```{r function} random_project <- function(student_id) { # set seed using student ID - student_id %>% - as.character() %>% - openssl::md5() %>% - gsub("[a-z]", "", .) %>% - substr(1, 8) %>% - set.seed() + seed_from_string(student_id) # simulate subjects subjects <- make_subjects() @@ -476,26 +499,15 @@ random_project <- function(student_id) { # simulate questionnaire questions <- make_quest(subjects) - # sample research question variables - factors <- sample(c("language", "condition", "version"), 2) - iv <- factors[[1]] - subset <- factors[[2]] - level <- subjects[[subset]] %>% unique() %>% sample(1) - topic <- sample(c("stress", "extroversion", "disgust"), 1) - alpha <- sample(c(.005, .01, .05), 1) - power <- sample(c(80, 85, 90), 1) - - # create random research question text - research_question <- readLines("template_instructions.txt") %>% - paste(collapse = "\n") %>% - glue::glue() + # customise instructions + instructions <- make_instructions("template_instructions.txt") - # write files + # write files to a new directory dir <- paste0("exam_", student_id) dir.create(dir, showWarnings = FALSE) readr::write_csv(subjects, file.path(dir, "subjects.csv")) readr::write_csv(questions, file.path(dir, "questions.csv")) - write(research_question, file.path(dir, "instructions.txt")) + write(instructions, file.path(dir, "instructions.txt")) return(dir) }