From 9c2a200ff458986534f9cd185d2cfe13e7f76fda Mon Sep 17 00:00:00 2001 From: debruine Date: Mon, 29 Apr 2019 17:04:08 +0100 Subject: [PATCH 1/6] Speed test for anova --- data-raw/speed_tests.R | 38 ++++++++++++++++++++++++++++---------- 1 file changed, 28 insertions(+), 10 deletions(-) diff --git a/data-raw/speed_tests.R b/data-raw/speed_tests.R index 4dd0fd85..8b9a188c 100644 --- a/data-raw/speed_tests.R +++ b/data-raw/speed_tests.R @@ -1,8 +1,12 @@ +library(faux) +library(tidyverse) + + # speed test ---- -library(tidyverse) + within <- list( - "A" = c("A1", "A2") + "W" = c("W1", "W2") ) between <- list( @@ -14,20 +18,30 @@ mu <- list( "B2" = c(10, 10) ) -func <- function(i) { +anova_func <- function(i) { utils::setTxtProgressBar(pb, i) df <- sim_design(within, between, n = 20, mu = mu, frame_long = TRUE) - suppressMessages( - anova <- afex::aov_4(val~B*(A|sub_id), data = df, check_contrasts = TRUE) - ) - anova$anova_table %>% - tibble::as_tibble(rownames = "factor") + aov <- aov(val~B*W+Error(sub_id/W), data = df, contrasts = NULL) %>% + summary() %>% + unclass() + + b <- aov[[1]][[1]] %>% tibble::as_tibble(rownames = "term") + w <- aov[[2]][[1]] %>% tibble::as_tibble(rownames = "term") + b$`den Df` <- b$Df[length(b$Df)] + w$`den Df` <- w$Df[length(w$Df)] + + dplyr::bind_rows(b, w) %>% + dplyr::filter(term != "Residuals") %>% + dplyr::mutate(term = trimws(term)) %>% + dplyr::select(`term`, `num Df` = `Df`, `den Df`, + `Sum Sq`, `Mean Sq`, `F` = `F value`, + `p` = `Pr(>F)`) } -reps <- 2 +reps <- 10000 pb <- txtProgressBar(max = reps) timestamp() -sims <- purrr::map_df(1:reps, func) +sims <- purrr::map_df(1:reps, anova_func) timestamp() close(pb) @@ -48,3 +62,7 @@ sims %>% # df <- purrr::map(1:1e4, ~sim_design(within, between, n = 20)) ##------ Sun Apr 28 20:44:48 2019 ------## +##------ Mon Apr 29 16:59:53 2019 ------## +# > sims <- purrr::map_df(1:1e4, anova_func) +##------ Mon Apr 29 17:02:51 2019 ------## + From 327f9c11eaefd8d814905e6ea94e88fe1eb4f39d Mon Sep 17 00:00:00 2001 From: debruine Date: Wed, 1 May 2019 07:30:36 +0100 Subject: [PATCH 2/6] Added long2wide for check_sim_stats --- NAMESPACE | 1 + R/check_sim_stats.R | 25 +++++++++- R/make_id.R | 17 +++++++ R/sim_design.R | 18 +++++-- data-raw/speed_tests.R | 69 ++++++++++++++++----------- man/make_id.Rd | 24 ++++++++++ tests/testthat/test-check_design.R | 29 +++++++++++ tests/testthat/test-check_sim_stats.R | 14 ++++++ vignettes/intro.Rmd | 10 ++++ vignettes/sim_design.Rmd | 11 +++++ 10 files changed, 184 insertions(+), 34 deletions(-) create mode 100644 R/make_id.R create mode 100644 man/make_id.Rd diff --git a/NAMESPACE b/NAMESPACE index a4f021d0..d8093a74 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,6 +6,7 @@ export(check_sim_stats) export(cormat) export(cormat_from_triangle) export(is_pos_def) +export(make_id) export(pos_def_limits) export(rnorm_multi) export(rnorm_pre) diff --git a/R/check_sim_stats.R b/R/check_sim_stats.R index c3839dfa..15679487 100644 --- a/R/check_sim_stats.R +++ b/R/check_sim_stats.R @@ -11,8 +11,17 @@ #' @examples #' check_sim_stats(iris, "Species") #' @export +#' -check_sim_stats <- function(dat, grp_by = NULL, digits = 2, usekable = FALSE) { +check_sim_stats <- function(dat, within = c(), between = c(), dv = c(), id = c(), + digits = 2, usekable = FALSE, grp_by = between) { + + if (length(within) && length(dv) && length(id)) { + # convert long to wide + dat <- long2wide(dat, within, between, dv, id) %>% + dplyr::select(-tidyselect::one_of(id)) + } + grpdat <- select_num_grp(dat, grp_by) grpvars <- dplyr::group_vars(grpdat) numvars <- names(grpdat)[!names(grpdat) %in% grpvars] @@ -27,7 +36,7 @@ check_sim_stats <- function(dat, grp_by = NULL, digits = 2, usekable = FALSE) { tidyr::spread(stat, val) stats <- grpdat %>% - tidyr::nest(dplyr::one_of(numvars), .key = "multisim_data") %>% + tidyr::nest(tidyselect::one_of(numvars), .key = "multisim_data") %>% dplyr::mutate(multisim_cor = purrr::map(multisim_data, function(d) { cor(d) %>% round(digits) %>% tibble::as_tibble(rownames = "var") })) %>% @@ -42,3 +51,15 @@ check_sim_stats <- function(dat, grp_by = NULL, digits = 2, usekable = FALSE) { return(stats) } } + + +#' Long to wide format +#' +long2wide <- function(dat, within = c(), between = c(), dv = c(), id = c()) { + dat %>% + dplyr::select(tidyselect::one_of(c(between, within, dv, id))) %>% + tidyr::unite(".tmpwithin.", tidyselect::one_of(within)) %>% + dplyr::group_by_at(dplyr::vars(tidyselect::one_of(between))) %>% + tidyr::spread(".tmpwithin.", !!quo(dv)) %>% + dplyr::ungroup() +} diff --git a/R/make_id.R b/R/make_id.R new file mode 100644 index 00000000..2720590a --- /dev/null +++ b/R/make_id.R @@ -0,0 +1,17 @@ +#' Make ID +#' +#' Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100). +#' @param n the number of IDs to generate +#' @param prefix the letter prefix to the number +#' +#' @return a vector of IDs +#' @export +#' +#' @examples +#' +#' make_id(20, "SUBJECT_") +#' +make_id <- function(n = 100, prefix = "S") { + max_digits <- floor(log10(n))+1 + paste0(prefix, formatC(1:n, width = max_digits, flag = "0")) +} \ No newline at end of file diff --git a/R/sim_design.R b/R/sim_design.R index d172da6e..1b82686c 100644 --- a/R/sim_design.R +++ b/R/sim_design.R @@ -20,6 +20,7 @@ sim_design <- function(within = list(), between = list(), empirical = FALSE, frame_long = FALSE) { # check the design is specified correctly design <- check_design(within, between, n, cors, mu, sd) + # simulate the data sim_design_(design, empirical = empirical, frame_long = frame_long) } @@ -148,7 +149,19 @@ convert_param <- function (param, cells_b, cells_w, type = "this parameter") { #' check_design <- function(within = list(), between = list(), n = 100, cors = 0, mu = 0, sd = 1) { - # error checking + # name anonymous factors + if (is.numeric(within) && within %in% 2:10 %>% mean() == 1) { # vector of level numbers + within_names <- LETTERS[1:length(within)] + within <- purrr::map2(within_names, within, ~paste0(.x, 1:.y)) + names(within) <- within_names + } + if (is.numeric(between) && between %in% 2:10 %>% mean() == 1) { # vector of level numbers + between_names <- LETTERS[(length(within)+1):(length(within)+length(between))] + between <- purrr::map2(between_names, between, ~paste0(.x, 1:.y)) + names(between) <- between_names + } + + # check factor specification if (!is.list(within) || !is.list(between)) { stop("within and between must be lists") } else if (length(within) == 0 && length(between) == 0) { @@ -213,8 +226,7 @@ check_design <- function(within = list(), between = list(), # figure out number of subjects and their IDs sub_n <- sum(cell_n[1,]) - max_digits <- floor(log10(sub_n))+1 - sub_id <- paste0("S",formatC(1:sub_n, width = max_digits, flag = "0")) + sub_id <- make_id(sub_n) # set up cell correlations from cors (number, vector, matrix or list styles) cell_cors <- list() diff --git a/data-raw/speed_tests.R b/data-raw/speed_tests.R index 8b9a188c..cfc6dcd5 100644 --- a/data-raw/speed_tests.R +++ b/data-raw/speed_tests.R @@ -6,44 +6,55 @@ library(tidyverse) within <- list( - "W" = c("W1", "W2") + "W" = c("W1", "W2"), + "X" = c("X1", "X2") ) -between <- list( - "B" = c("B1", "B2") -) +between <- list() + +mu <- c(W1_X1 = 10, W1_X2 = 12, W2_X1 = 10, W2_X2 = 10) -mu <- list( - "B1" = c(10, 10), - "B2" = c(10, 10) -) -anova_func <- function(i) { - utils::setTxtProgressBar(pb, i) - df <- sim_design(within, between, n = 20, mu = mu, frame_long = TRUE) - aov <- aov(val~B*W+Error(sub_id/W), data = df, contrasts = NULL) %>% - summary() %>% - unclass() +anova_func <- function(i, v = "afex") { + #utils::setTxtProgressBar(pb, i) + df <- sim_design(within, between, n = 20, mu = mu, sd = 4, frame_long = TRUE) - b <- aov[[1]][[1]] %>% tibble::as_tibble(rownames = "term") - w <- aov[[2]][[1]] %>% tibble::as_tibble(rownames = "term") - b$`den Df` <- b$Df[length(b$Df)] - w$`den Df` <- w$Df[length(w$Df)] + if (v == "afex") { + afex::aov_4(val~(X*W|sub_id), data = df, return = "aov") %>% + broom::tidy() + } else if (v == "aov") { + aov(val~(X*W)+Error(sub_id/(X*W)), data = df, contrasts = NULL) %>% + broom::tidy() + } +} + +anova_func2 <- function(i) { + df <- sim_design(within, between, n = 20, mu = mu, sd = 4, frame_long = TRUE) - dplyr::bind_rows(b, w) %>% - dplyr::filter(term != "Residuals") %>% - dplyr::mutate(term = trimws(term)) %>% - dplyr::select(`term`, `num Df` = `Df`, `den Df`, - `Sum Sq`, `Mean Sq`, `F` = `F value`, - `p` = `Pr(>F)`) + aov(val~(X*W)+Error(sub_id/(X*W)), data = df, contrasts = NULL) %>% + broom::tidy() } reps <- 10000 -pb <- txtProgressBar(max = reps) -timestamp() -sims <- purrr::map_df(1:reps, anova_func) -timestamp() -close(pb) +#pb <- utils::txtProgressBar(max = reps) +system.time( + sims_afex <- purrr::map_df(1:reps, anova_func, v = "afex") +) +system.time( + sims_aov <- purrr::map_df(1:reps, anova_func, v = "aov") +) +#close(pb) + +sims_afex %>% + filter(term != "Residuals") %>% + group_by(term) %>% + summarise(power = mean(p.value < .05)) + +sims_aov %>% + filter(term != "Residuals") %>% + group_by(term) %>% + summarise(power = mean(p.value < .05)) + alpha <- 0.05 diff --git a/man/make_id.Rd b/man/make_id.Rd new file mode 100644 index 00000000..8678d9d6 --- /dev/null +++ b/man/make_id.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/make_id.R +\name{make_id} +\alias{make_id} +\title{Make ID} +\usage{ +make_id(n = 100, prefix = "S") +} +\arguments{ +\item{n}{the number of IDs to generate} + +\item{prefix}{the letter prefix to the number} +} +\value{ +a vector of IDs +} +\description{ +Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100). +} +\examples{ + +make_id(20, "SUBJECT_") + +} diff --git a/tests/testthat/test-check_design.R b/tests/testthat/test-check_design.R index 61876cc6..ed9edb70 100644 --- a/tests/testthat/test-check_design.R +++ b/tests/testthat/test-check_design.R @@ -59,3 +59,32 @@ test_that("design spec", { expect_equal(names(design), design_elements) }) + +test_that("anon factors", { + design <- check_design(c(2, 4), c(2, 2)) + + w <- list( + A = c("A1", "A2"), + B = c("B1", "B2", "B3", "B4") + ) + + b <- list( + C = c("C1","C2"), + D = c("D1", "D2") + ) + + expect_equal(design$within, w) + expect_equal(design$between, b) +}) + +test_that("make_id", { + expect_equal(make_id(10), c("S01", "S02", "S03", "S04", "S05", + "S06", "S07", "S08", "S09", "S10")) + + expect_equal(make_id(10, "SUB"), c("SUB01", "SUB02", "SUB03", "SUB04", "SUB05", + "SUB06", "SUB07", "SUB08", "SUB09", "SUB10")) + + expect_equal(make_id(100)[[1]], "S001") + expect_equal(make_id(1000)[[1]], "S0001") + expect_equal(make_id(1000, "pokemon_")[[1]], "pokemon_0001") +}) diff --git a/tests/testthat/test-check_sim_stats.R b/tests/testthat/test-check_sim_stats.R index c60bafb2..4a517391 100644 --- a/tests/testthat/test-check_sim_stats.R +++ b/tests/testthat/test-check_sim_stats.R @@ -31,3 +31,17 @@ test_that("is_pos_def", { .9, -.2, 1), 3) expect_equal(is_pos_def(bad_matrix), FALSE) }) + + +test_that("long", { + iris_long <- iris %>% + dplyr::mutate(id = make_id(nrow(.), "I")) %>% + tidyr::gather(var, val, Sepal.Length:Petal.Width) %>% + tidyr::separate(var, c("Feature", "Measure")) + + iris_wide <- long2wide(iris_long, within = c("Feature", "Measure"), + between = "Species", dv = "val", id = "id") + + check_sim_stats(iris_long, within = c("Feature", "Measure"), + between = "Species", dv = "val", id = "id") +}) diff --git a/vignettes/intro.Rmd b/vignettes/intro.Rmd index d480e12a..20625552 100644 --- a/vignettes/intro.Rmd +++ b/vignettes/intro.Rmd @@ -111,6 +111,14 @@ check_sim_stats(iris, usekable = TRUE) ``` +### make_id + +It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don't accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,..., S10 rather than S1, S10, S2, ...) This function returns a list of IDs that have the same string length and a specified prefix. + +```{r} +make_id(n = 10, prefix = "ITEM_") +``` + ### pos_def_limits @@ -136,6 +144,8 @@ lims <- pos_def_limits(.8, .2, 0, `r knitr::kable(lims)` +### is_pos_def() + If you have a full matrix and want to know if it is positive definite, you can use the following code: ```{r is_pos_def} diff --git a/vignettes/sim_design.Rmd b/vignettes/sim_design.Rmd index d452c745..05838497 100644 --- a/vignettes/sim_design.Rmd +++ b/vignettes/sim_design.Rmd @@ -84,6 +84,17 @@ within <- list( The long name for factor levels isn't used yet, but will be used in future functions that create a codebook or plots. +If you don't feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w\*2w\*2b withthe following code. + +```{r} +df <- sim_design(within = c(2,2), between = c(2), n = 5) +``` + +`r knitr::kable(df)` + + + + ### N, mean, SD You can specify the Ns, means and standard deviations for each cell as a single number, vector, list, or data frame. From 425de8ceedbb7b51cb7aaaa523ef4adb87f7bff8 Mon Sep 17 00:00:00 2001 From: debruine Date: Wed, 1 May 2019 07:38:44 +0100 Subject: [PATCH 3/6] tests for long2wide --- NAMESPACE | 1 + R/check_sim_stats.R | 12 ++++++++++++ man/check_sim_stats.Rd | 7 ++++--- man/long2wide.Rd | 25 +++++++++++++++++++++++++ tests/testthat/test-check_sim_stats.R | 12 ++++++++++-- 5 files changed, 52 insertions(+), 5 deletions(-) create mode 100644 man/long2wide.Rd diff --git a/NAMESPACE b/NAMESPACE index d8093a74..988cea79 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,6 +6,7 @@ export(check_sim_stats) export(cormat) export(cormat_from_triangle) export(is_pos_def) +export(long2wide) export(make_id) export(pos_def_limits) export(rnorm_multi) diff --git a/R/check_sim_stats.R b/R/check_sim_stats.R index 15679487..da510d47 100644 --- a/R/check_sim_stats.R +++ b/R/check_sim_stats.R @@ -55,6 +55,18 @@ check_sim_stats <- function(dat, within = c(), between = c(), dv = c(), id = c() #' Long to wide format #' +#' Converts data from long format to wide +#' +#' @param dat the long data frame to convert +#' @param within the names of the within column(s) +#' @param between the names of between column(s) (optional) +#' @param dv the name of the DV (value) column +#' @param id the names of the column(s) for grouping observations +#' +#' @return the data frame in wide format +#' +#' @export +#' long2wide <- function(dat, within = c(), between = c(), dv = c(), id = c()) { dat %>% dplyr::select(tidyselect::one_of(c(between, within, dv, id))) %>% diff --git a/man/check_sim_stats.Rd b/man/check_sim_stats.Rd index 56b38f6c..dd7bc219 100644 --- a/man/check_sim_stats.Rd +++ b/man/check_sim_stats.Rd @@ -4,16 +4,17 @@ \alias{check_sim_stats} \title{Check table stats} \usage{ -check_sim_stats(dat, grp_by = NULL, digits = 2, usekable = FALSE) +check_sim_stats(dat, within = c(), between = c(), dv = c(), + id = c(), digits = 2, usekable = FALSE, grp_by = between) } \arguments{ \item{dat}{the existing dataframe} -\item{grp_by}{an optional list of column names to group by} - \item{digits}{how many digits to round to (default = 2)} \item{usekable}{logical. If TRUE, output with knitr::kable} + +\item{grp_by}{an optional list of column names to group by} } \value{ tibble or kable diff --git a/man/long2wide.Rd b/man/long2wide.Rd new file mode 100644 index 00000000..53888228 --- /dev/null +++ b/man/long2wide.Rd @@ -0,0 +1,25 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/check_sim_stats.R +\name{long2wide} +\alias{long2wide} +\title{Long to wide format} +\usage{ +long2wide(dat, within = c(), between = c(), dv = c(), id = c()) +} +\arguments{ +\item{dat}{the long data frame to convert} + +\item{within}{the names of the within column(s)} + +\item{between}{the names of between column(s) (optional)} + +\item{dv}{the name of the DV (value) column} + +\item{id}{the names of the column(s) for grouping observations} +} +\value{ +the data frame in wide format +} +\description{ +Converts data from long format to wide +} diff --git a/tests/testthat/test-check_sim_stats.R b/tests/testthat/test-check_sim_stats.R index 4a517391..af67f248 100644 --- a/tests/testthat/test-check_sim_stats.R +++ b/tests/testthat/test-check_sim_stats.R @@ -41,7 +41,15 @@ test_that("long", { iris_wide <- long2wide(iris_long, within = c("Feature", "Measure"), between = "Species", dv = "val", id = "id") + inames <- c("Species", "id", "Petal_Length", "Petal_Width", "Sepal_Length", "Sepal_Width") + testthat::expect_equal(names(iris_wide), inames) + testthat::expect_equal(nrow(iris_wide), 150) - check_sim_stats(iris_long, within = c("Feature", "Measure"), - between = "Species", dv = "val", id = "id") + long <- check_sim_stats(iris_long, within = c("Feature", "Measure"), + between = "Species", dv = "val", id = "id") + + wide <- check_sim_stats(iris, between = "Species") + + + testthat::expect_equal(nrow(long), nrow(wide)) }) From 2e6b53215a76edb725bd2a55068d6181d5594185 Mon Sep 17 00:00:00 2001 From: debruine Date: Wed, 1 May 2019 16:55:34 +0100 Subject: [PATCH 4/6] Making argument names more consistent (grp_by -> between, etc) --- NAMESPACE | 3 +- R/check_sim_stats.R | 39 +- R/select_num_grp.R | 20 +- R/{simdf.R => sim_df.R} | 13 +- README.Rmd | 246 ++----- README.md | 325 +++------ .../plot-iris-sim-1.png | Bin 0 -> 64496 bytes docs/LICENSE-text.html | 5 +- docs/articles/index.html | 8 +- docs/articles/intro.html | 28 +- docs/articles/rnorm_multi.html | 7 +- docs/articles/sim_design.html | 103 ++- .../figure-html/unnamed-chunk-17-1.png | Bin 0 -> 207894 bytes docs/articles/sim_df.html | 171 +++++ .../figure-html/plot-cars-orig-1.png | Bin 0 -> 95563 bytes .../figure-html/plot-cars-sim-1.png | Bin 0 -> 165548 bytes .../figure-html/plot-iris-orig-1.png | Bin 0 -> 148149 bytes .../figure-html/plot-iris-sim-1.png | Bin 0 -> 148429 bytes .../figure-html/plot-iris-sim-round-1.png | Bin 0 -> 147057 bytes docs/authors.html | 5 +- docs/index.html | 682 ++++-------------- .../figure-html/plot-iris-sim-1.png | Bin 0 -> 147098 bytes docs/news/index.html | 5 +- docs/pkgdown.yml | 3 +- docs/reference/check_design.html | 5 +- docs/reference/check_sim_stats.html | 24 +- docs/reference/convert_param.html | 5 +- docs/reference/cormat.html | 5 +- docs/reference/cormat_from_triangle.html | 5 +- docs/reference/faceratings.html | 5 +- docs/reference/fix_name_labels.html | 5 +- docs/reference/index.html | 20 +- docs/reference/is_pos_def.html | 5 +- docs/reference/long2wide.html | 199 +++++ docs/reference/make_id.html | 185 +++++ docs/reference/pipe.html | 5 +- docs/reference/pos_def_limits.html | 5 +- docs/reference/rnorm_multi.html | 405 ++++++----- docs/reference/rnorm_pre.html | 5 +- docs/reference/select_num_grp.html | 9 +- docs/reference/sim_design.html | 5 +- docs/reference/sim_design_.html | 5 +- docs/reference/sim_df.html | 194 +++++ docs/reference/simdf_mixed.html | 207 +++--- man/check_sim_stats.Rd | 11 +- man/long2wide.Rd | 33 + man/select_num_grp.Rd | 4 +- man/sim_df.Rd | 30 + man/simdf.Rd | 27 - tests/testthat/test-check_sim_stats.R | 26 +- tests/testthat/test-select_num_grp.R | 2 +- tests/testthat/test-sim_design.R | 18 +- .../testthat/{test-simdf.R => test-sim_df.R} | 20 +- vignettes/intro.Rmd | 171 ----- vignettes/sim_design.Rmd | 4 +- vignettes/{simdf.Rmd => sim_df.Rmd} | 12 +- 56 files changed, 1689 insertions(+), 1635 deletions(-) rename R/{simdf.R => sim_df.R} (60%) create mode 100644 README_files/figure-markdown_github/plot-iris-sim-1.png create mode 100644 docs/articles/sim_design_files/figure-html/unnamed-chunk-17-1.png create mode 100644 docs/articles/sim_df.html create mode 100644 docs/articles/sim_df_files/figure-html/plot-cars-orig-1.png create mode 100644 docs/articles/sim_df_files/figure-html/plot-cars-sim-1.png create mode 100644 docs/articles/sim_df_files/figure-html/plot-iris-orig-1.png create mode 100644 docs/articles/sim_df_files/figure-html/plot-iris-sim-1.png create mode 100644 docs/articles/sim_df_files/figure-html/plot-iris-sim-round-1.png create mode 100644 docs/index_files/figure-html/plot-iris-sim-1.png create mode 100644 docs/reference/long2wide.html create mode 100644 docs/reference/make_id.html create mode 100644 docs/reference/sim_df.html create mode 100644 man/long2wide.Rd create mode 100644 man/sim_df.Rd delete mode 100644 man/simdf.Rd rename tests/testthat/{test-simdf.R => test-sim_df.R} (79%) delete mode 100644 vignettes/intro.Rmd rename vignettes/{simdf.Rmd => sim_df.Rmd} (82%) diff --git a/NAMESPACE b/NAMESPACE index d8093a74..83cdf4d8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -6,12 +6,13 @@ export(check_sim_stats) export(cormat) export(cormat_from_triangle) export(is_pos_def) +export(long2wide) export(make_id) export(pos_def_limits) export(rnorm_multi) export(rnorm_pre) export(select_num_grp) export(sim_design) -export(simdf) +export(sim_df) export(simdf_mixed) importFrom(magrittr,"%>%") diff --git a/R/check_sim_stats.R b/R/check_sim_stats.R index 15679487..70e04110 100644 --- a/R/check_sim_stats.R +++ b/R/check_sim_stats.R @@ -3,7 +3,10 @@ #' \code{check_sim_stats} Generates a table of the correlations and means of numeric columns in a data frame #' #' @param dat the existing dataframe -#' @param grp_by an optional list of column names to group by +#' @param between a vector of column names for between-subject factors +#' @param within a vector of column names for within-subject factors (if data is long) +#' @param dv the column name of the dv (if data is long) +#' @param id the column name(s) of the subject ID (if data is long) #' @param digits how many digits to round to (default = 2) #' @param usekable logical. If TRUE, output with knitr::kable #' @@ -13,16 +16,16 @@ #' @export #' -check_sim_stats <- function(dat, within = c(), between = c(), dv = c(), id = c(), - digits = 2, usekable = FALSE, grp_by = between) { +check_sim_stats <- function(dat, between = c(), within = c(), dv = c(), id = c(), + digits = 2, usekable = FALSE) { if (length(within) && length(dv) && length(id)) { # convert long to wide - dat <- long2wide(dat, within, between, dv, id) %>% + dat <- long2wide(dat, between, within, dv, id) %>% dplyr::select(-tidyselect::one_of(id)) } - grpdat <- select_num_grp(dat, grp_by) + grpdat <- select_num_grp(dat, between) grpvars <- dplyr::group_vars(grpdat) numvars <- names(grpdat)[!names(grpdat) %in% grpvars] @@ -42,8 +45,8 @@ check_sim_stats <- function(dat, within = c(), between = c(), dv = c(), id = c() })) %>% dplyr::select(-multisim_data) %>% tidyr::unnest(multisim_cor) %>% - dplyr::left_join(descriptives, by = c(grp_by, "var")) %>% - dplyr::select(tidyselect::one_of(c(grp_by, "n", "var", numvars, "mean", "sd"))) + dplyr::left_join(descriptives, by = c(between, "var")) %>% + dplyr::select(tidyselect::one_of(c(between, "n", "var", numvars, "mean", "sd"))) if (usekable) { return(knitr::kable(stats)) @@ -54,12 +57,28 @@ check_sim_stats <- function(dat, within = c(), between = c(), dv = c(), id = c() #' Long to wide format + +#' Converts a table from long to wide format +#' +#' @param dat the existing dataframe in long format +#' @param between a vector of column names for between-subject factors +#' @param within a vector of column names for within-subject factors +#' @param dv the column name of the dv +#' @param id the column name(s) of the subject ID +#' +#' @return tibble in wide format +#' @examples +#' +#' df_long <- sim_design(2,2, frame_long = TRUE) +#' df_wide <- long2wide(df_long, "B", "A", "val", "sub_id") +#' +#' @export #' -long2wide <- function(dat, within = c(), between = c(), dv = c(), id = c()) { +long2wide <- function(dat, between = c(), within = c(), dv = c(), id = c()) { dat %>% - dplyr::select(tidyselect::one_of(c(between, within, dv, id))) %>% + dplyr::select(tidyselect::one_of(c(id, between, within, dv))) %>% tidyr::unite(".tmpwithin.", tidyselect::one_of(within)) %>% dplyr::group_by_at(dplyr::vars(tidyselect::one_of(between))) %>% - tidyr::spread(".tmpwithin.", !!quo(dv)) %>% + tidyr::spread(".tmpwithin.", !!dplyr::quo(dv)) %>% dplyr::ungroup() } diff --git a/R/select_num_grp.R b/R/select_num_grp.R index 8a1076e5..5e9d1342 100644 --- a/R/select_num_grp.R +++ b/R/select_num_grp.R @@ -3,7 +3,7 @@ #' \code{select_num_grp} Select grouping and (optionally specified) numeric columns and group #' #' @param dat the existing dataframe -#' @param grp_by an optional list of column names to group by +#' @param between an optional list of column names to group by #' @param cols an optional list of column names to return (default of NULL returns all numeric columns) #' #' @return tibble @@ -11,7 +11,7 @@ #' select_num_grp(iris, "Species") #' @export -select_num_grp <- function(dat, grp_by = NULL, cols = NULL) { +select_num_grp <- function(dat, between = c(), cols = NULL) { # error checking ----------------- if (is.matrix(dat)) { dat = as.data.frame(dat) @@ -20,26 +20,26 @@ select_num_grp <- function(dat, grp_by = NULL, cols = NULL) { } # select only grouping and numeric columns ----------------- - if (is.null(grp_by)) { + if (is.null(between)) { # no grouping, so select all numeric columns numdat <- dplyr::select_if(dat, is.numeric) grpdat <- numdat - } else if (is.numeric(grp_by) || is.character(grp_by)) { + } else if (is.numeric(between) || is.character(between)) { # get grouping column names if specified by index - if (is.numeric(grp_by)) grp_by <- names(dat)[grp_by] + if (is.numeric(between)) between <- names(dat)[between] # numeric columns, excluding grouping columns numdat <- dat %>% - dplyr::select(-tidyselect::one_of(grp_by)) %>% + dplyr::select(-tidyselect::one_of(between)) %>% dplyr::select_if(is.numeric) # get grouping columns, add remaining numeric columns, and group grpdat <- dat %>% - dplyr::select(tidyselect::one_of(grp_by)) %>% + dplyr::select(tidyselect::one_of(between)) %>% dplyr::bind_cols(numdat) %>% - dplyr::group_by_at(dplyr::vars(tidyselect::one_of(grp_by))) + dplyr::group_by_at(dplyr::vars(tidyselect::one_of(between))) } else { - stop("grp_by must be a numeric or character vector") + stop("between must be a numeric or character vector") } if (!is.null(cols)) { @@ -47,7 +47,7 @@ select_num_grp <- function(dat, grp_by = NULL, cols = NULL) { if (is.numeric(cols)) cols <- names(dat)[cols] grpdat <- grpdat %>% - dplyr::select(tidyselect::one_of(c(grp_by, cols))) + dplyr::select(tidyselect::one_of(c(between, cols))) } return(grpdat) diff --git a/R/simdf.R b/R/sim_df.R similarity index 60% rename from R/simdf.R rename to R/sim_df.R index 1b02cc2d..4770ce3b 100644 --- a/R/simdf.R +++ b/R/sim_df.R @@ -1,25 +1,26 @@ #' Simulate an existing dataframe #' -#' \code{simdf} Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). +#' \code{sim_df} Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). #' #' @param dat the existing dataframe #' @param n the number of samples to return per group -#' @param grp_by an optional list of column names to group by +#' @param between a list of the between-subject factors +#' @param within a list of the within-subject factors #' @param empirical logical. Passed on to rnorm_multi #' #' @return tibble #' @examples -#' iris100 <- simdf(iris, 100) -#' iris_species <- simdf(iris, 100, "Species") +#' iris100 <- sim_df(iris, 100) +#' iris_species <- sim_df(iris, 100, "Species") #' @export -simdf <- function (dat, n=100, grp_by=NULL, empirical = FALSE) { +sim_df <- function (dat, n = 100, between = c(), within = c(), empirical = FALSE) { # error checking if ( !is.numeric(n) || n %% 1 > 0 || n < 3 ) { stop("n must be an integer > 2") } - grpdat <- select_num_grp(dat, grp_by) + grpdat <- select_num_grp(dat, between) simdat <- grpdat %>% tidyr::nest() %>% diff --git a/README.Rmd b/README.Rmd index d90a152f..653b6431 100644 --- a/README.Rmd +++ b/README.Rmd @@ -5,192 +5,121 @@ always_allow_html: yes + +[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) + + + ```{r setup, include = FALSE} knitr::opts_chunk$set( + fig.width = 8, + fig.height = 5, collapse = TRUE, - comment = "#>", - fig.path = "man/figures/README-", - out.width = "100%" + comment = "#>" ) + +library(ggplot2) +library(dplyr) +library(tidyr) +library(faux) +theme_set(theme_bw()) +set.seed(200) ``` -# faux + It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier. -## Installation +## sim_design -You can install the released version of faux from -[GitHub](https://github.com/debruine/faux) with: +This function creates a dataset with a specific between- and within-subjects design. [see vignette](articles/sim_design.html) -``` r -devtools::install_github("debruine/faux") -``` +For example, the following creates a 2w*2b design with 100 observations in each cell. The between-subject factor is `pet` with twolevels of `cat` and `dog`. The within-subject factor is `time` with two levels of `day` and `night`. The mean for the `cat_day` cell is 10, the mean for the `cat_night` cell is 20, the mean for the `dog_day` cell is 15, and the mean for the `dog_night` cell is 25. All cells have a SD of 5 and all within-subject cells are correlated r = 0.5. The resulting data has exactly these values (set `empirical = FALSE` to sample from a population with these values). -## Examples -```{r libraries, message=FALSE} -library(tidyverse) -library(faux) +```{r} +between <- list("pet" = c("cat", "dog")) +within <- list("time" = c("day", "night")) +mu <- data.frame( + cat = c(10, 20), + dog = c(15, 25), + row.names = within$time +) +df <- sim_design(within, between, + n = 100, cors = 0.5, mu = mu, sd = 5, + empirical = TRUE) ``` +`r check_sim_stats(df, between = "pet", usekable = TRUE)` +Table: Sample `sim_design()` stats -### rnorm_multi +## rnorm_multi -This function makes multiple normally distributed vectors with specified parameters and relationships. +This function makes multiple normally distributed vectors with specified parameters and relationships.[see vignette](articles/rnorm_multi.html) For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. -```{r set-seed, include=FALSE} -set.seed(200) -``` - - ```{r multirnorm-example} -dat <- rnorm_multi(n = 100, - cors = c(0.5, 0.5, 0.25), - mu = c(0, 20, 20), - sd = c(1, 5, 5), - varnames = c("A", "B", "C"), - empirical = FALSE) +dat <- rnorm_multi( + n = 100, + cors = c(0.5, 0.5, 0.25), + mu = c(0, 20, 20), + sd = c(1, 5, 5), + varnames = c("A", "B", "C"), + empirical = FALSE +) ``` `r check_sim_stats(dat, usekable = T)` -Table: Sample stats - -#### Specify `cors` +Table: Sample `rnorm_multi()` stats -You can specify the correlations in one of four ways: -* A single r for all pairs -* A vars by vars matrix -* A vars\*vars length vector -* A vars\*(vars-1)/2 length vector +## sim_df -##### One Number +This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). [see vignette](articles/sim_df.html) -If you want all the pairs to have the same correlation, just specify a single number. +For example, the following code creates a new sample from the built-in dataset `iris` with 50 observations of each species. ```{r} -bvn <- rnorm_multi(100, 5, .3, varnames = letters[1:5]) +new_iris <- sim_df(iris, 50, "Species") ``` -`r check_sim_stats(bvn, usekable = T)` -Table: Sample stats from a single rho - -##### Matrix - -If you already have a correlation matrix, such as the output of `cor()`, you can specify the simulated data with that. - -```{r vvmatrix} -cmat <- cor(iris[,1:4]) -bvn <- rnorm_multi(100, 4, cmat, - varnames = colnames(cmat)) +```{r plot-iris-sim, echo = FALSE, fig.cap="Simulated iris dataset"} +new_iris %>% + ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + + geom_point() + + geom_smooth(method = "lm") ``` -`r check_sim_stats(bvn, usekable = T)` -Table: Sample stats from a correlation matrix +## Additional functions -##### Vector (vars\*vars) +### check_sim_stats -You can specify your correlation matrix by hand as a vars\*vars length vector, which will include the correlations of 1 down the diagonal. +If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`. ```{r} -cmat <- c(1, .3, .5, - .3, 1, 0, - .5, 0, 1) -bvn <- rnorm_multi(100, 3, cmat, - varnames = c("first", "second", "third")) +check_sim_stats(iris) ``` -`r check_sim_stats(bvn, usekable = T)` -Table: Sample stats from a vars\*vars vector - -##### Vector (vars\*(vars-1)/2) - -You can specify your correlation matrix by hand as a vars\*(vars-1)/2 length vector, skipping the diagonal and lower left duplicate values. +You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header. -```{r} -rho1_2 <- .3 -rho1_3 <- .5 -rho1_4 <- .5 -rho2_3 <- .2 -rho2_4 <- 0 -rho3_4 <- -.3 -cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4) -bvn <- rnorm_multi(100, 4, cmat, - varnames = letters[1:4]) +```{r, results='asis'} +check_sim_stats(iris, + between = "Species", + digits = 3, + usekable = TRUE) ``` -`r check_sim_stats(bvn, usekable = T)` -Table: Sample stats from a (vars\*(vars-1)/2) vector - -#### empirical +### make_id -If you want your samples to have the *exact* correlations, means, and SDs you entered, set `empirical` to TRUE. +It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don't accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,..., S10 rather than S1, S10, S2, ...) This function returns a list of IDs that have the same string length and a specified prefix. ```{r} -bvn <- rnorm_multi(100, 5, .3, - varnames = letters[1:5], - empirical = T) -``` - -`r check_sim_stats(bvn, usekable = T)` -Table: Sample stats with empirical = TRUE - -### simdf - -This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). - -For example, here is the relationship between speed and distance in the built-in dataset `cars`. - -```{r plot-cars-orig, fig.cap="Original cars dataset"} -cars %>% - ggplot(aes(speed, dist)) + - geom_point() + - geom_smooth(method = "lm") -``` - -You can create a new sample with the same parameters and 500 rows with the code `simdf(cars, 500)`. - -```{r plot-cars-sim, fig.cap="Simulated cars dataset"} -simdf(cars, 500) %>% - ggplot(aes(speed, dist)) + - geom_point() + - geom_smooth(method = "lm") +make_id(n = 10, prefix = "ITEM_") ``` -#### Grouping Variables - -You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset `iris`. - -```{r plot-iris-orig, fig.cap="Original iris dataset"} -iris %>% - ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + - geom_point() + - geom_smooth(method = "lm") -``` - -And here is a new sample with 50 observations of each species, made with the code `simdf(iris, 100, "Species")`. - -```{r plot-iris-sim, fig.cap="Simulated iris dataset"} -simdf(iris, 50, "Species") %>% - ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + - geom_point() + - geom_smooth(method = "lm") -``` - -For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you'd need to do any rounding or truncating yourself. - -```{r plot-iris-sim-round, fig.cap="Simulated iris dataset (rounded)"} -simdf(iris, 50, "Species") %>% - mutate_if(is.numeric, round, 1) %>% - ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + - geom_point() + - geom_smooth(method = "lm") -``` ### pos_def_limits @@ -216,6 +145,8 @@ lims <- pos_def_limits(.8, .2, 0, `r knitr::kable(lims)` +### is_pos_def() + If you have a full matrix and want to know if it is positive definite, you can use the following code: ```{r is_pos_def} @@ -235,50 +166,7 @@ matrix(c(1, .3, -.9, .2, is_pos_def() ``` -### check_sim_stats -If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`. -```{r} -check_sim_stats(iris) -``` - -You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header. - -```{r, results='asis'} -check_sim_stats(iris, - grp_by = "Species", - digits = 3, - usekable = TRUE) -``` - - -### sim_design - -Simulate data by specifying a design structure. -**This function is under development and should be carefully checked!** - -```{r} -between <- list("pet" = c("cat", "dog")) -within <- list("time" = c("day", "night")) -mu <- data.frame( - cat = c(10, 20), - dog = c(15, 25), - row.names = within$time -) -df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5) -``` - -```{r echo = FALSE, result='asis'} -check_sim_stats(df, grp_by = "pet", usekable = TRUE) -``` -```{r, echo = FALSE, fig.width = 8, fig.height = 4} -gather(df, time, val, day:night) %>% - ggplot(aes(pet, val, fill = pet)) + - geom_violin(show.legend = FALSE) + - geom_boxplot(width = .2, fill = "white", show.legend = FALSE) + - facet_grid(~time) + - scale_fill_manual(values = c("dodgerblue", "red")) -``` \ No newline at end of file diff --git a/README.md b/README.md index bba184d3..1a9106ea 100644 --- a/README.md +++ b/README.md @@ -1,227 +1,127 @@ -faux -==== + +[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier. -Installation ------------- +sim\_design +----------- -You can install the released version of faux from [GitHub](https://github.com/debruine/faux) with: +This function creates a dataset with a specific between- and within-subjects design. [see vignette](sim_design.html) -``` r -devtools::install_github("debruine/faux") -``` - -Examples --------- +For example, the following creates a 2w\*2b design with 100 observations in each cell. The between-subject factor is `pet` with twolevels of `cat` and `dog`. The within-subject factor is `time` with two levels of `day` and `night`. The mean for the `cat_day` cell is 10, the mean for the `cat_night` cell is 20, the mean for the `dog_day` cell is 15, and the mean for the `dog_night` cell is 25. All cells have a SD of 5 and all within-subject cells are correlated r = 0.5. The resulting data has exactly these values (set `empirical = FALSE` to sample from a population with these values). ``` r -library(tidyverse) -library(faux) +between <- list("pet" = c("cat", "dog")) +within <- list("time" = c("day", "night")) +mu <- data.frame( + cat = c(10, 20), + dog = c(15, 25), + row.names = within$time +) +df <- sim_design(within, between, + n = 100, cors = 0.5, mu = mu, sd = 5, + empirical = TRUE) ``` -### rnorm\_multi +| pet | n| var | day| night| mean| sd| +|:----|----:|:------|----:|------:|-----:|----:| +| cat | 100| day | 1.0| 0.5| 10| 5| +| cat | 100| night | 0.5| 1.0| 20| 5| +| dog | 100| day | 1.0| 0.5| 15| 5| +| dog | 100| night | 0.5| 1.0| 25| 5| + +rnorm\_multi +------------ -This function makes multiple normally distributed vectors with specified parameters and relationships. +This function makes multiple normally distributed vectors with specified parameters and relationships.[see vignette](rnorm_multi.html) For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5. ``` r -dat <- rnorm_multi(n = 100, - cors = c(0.5, 0.5, 0.25), - mu = c(0, 20, 20), - sd = c(1, 5, 5), - varnames = c("A", "B", "C"), - empirical = FALSE) +dat <- rnorm_multi( + n = 100, + cors = c(0.5, 0.5, 0.25), + mu = c(0, 20, 20), + sd = c(1, 5, 5), + varnames = c("A", "B", "C"), + empirical = FALSE +) ``` | n| var | A| B| C| mean| sd| |----:|:----|-----:|-----:|-----:|------:|-----:| -| 100| A | 1.00| 0.45| 0.49| 0.03| 0.99| -| 100| B | 0.45| 1.00| 0.33| 20.01| 4.89| -| 100| C | 0.49| 0.33| 1.00| 19.76| 4.02| - -#### Specify `cors` - -You can specify the correlations in one of four ways: - -- A single r for all pairs -- A vars by vars matrix -- A vars\*vars length vector -- A vars\*(vars-1)/2 length vector - -##### One Number - -If you want all the pairs to have the same correlation, just specify a single number. - -``` r -bvn <- rnorm_multi(100, 5, .3, varnames = letters[1:5]) -``` - -| n| var | a| b| c| d| e| mean| sd| -|----:|:----|-----:|-----:|-----:|-----:|-----:|------:|-----:| -| 100| a | 1.00| 0.35| 0.22| 0.45| 0.37| -0.04| 1.09| -| 100| b | 0.35| 1.00| 0.19| 0.36| 0.28| -0.05| 0.83| -| 100| c | 0.22| 0.19| 1.00| 0.26| 0.20| 0.01| 1.08| -| 100| d | 0.45| 0.36| 0.26| 1.00| 0.24| 0.00| 1.00| -| 100| e | 0.37| 0.28| 0.20| 0.24| 1.00| 0.04| 0.97| - -##### Matrix - -If you already have a correlation matrix, such as the output of `cor()`, you can specify the simulated data with that. - -``` r -cmat <- cor(iris[,1:4]) -bvn <- rnorm_multi(100, 4, cmat, - varnames = colnames(cmat)) -``` - -| n| var | Sepal.Length| Sepal.Width| Petal.Length| Petal.Width| mean| sd| -|----:|:-------------|-------------:|------------:|-------------:|------------:|------:|-----:| -| 100| Sepal.Length | 1.00| -0.10| 0.88| 0.83| -0.01| 1.05| -| 100| Sepal.Width | -0.10| 1.00| -0.38| -0.29| -0.19| 1.09| -| 100| Petal.Length | 0.88| -0.38| 1.00| 0.96| -0.01| 1.02| -| 100| Petal.Width | 0.83| -0.29| 0.96| 1.00| -0.05| 0.98| - -##### Vector (vars\*vars) - -You can specify your correlation matrix by hand as a vars\*vars length vector, which will include the correlations of 1 down the diagonal. - -``` r -cmat <- c(1, .3, .5, - .3, 1, 0, - .5, 0, 1) -bvn <- rnorm_multi(100, 3, cmat, - varnames = c("first", "second", "third")) -``` +| 100| A | 1.00| 0.62| 0.46| -0.05| 1.08| +| 100| B | 0.62| 1.00| 0.19| 19.95| 5.38| +| 100| C | 0.46| 0.19| 1.00| 19.81| 5.15| -| n| var | first| second| third| mean| sd| -|----:|:-------|------:|-------:|------:|------:|-----:| -| 100| first | 1.00| 0.33| 0.45| -0.12| 1.01| -| 100| second | 0.33| 1.00| -0.04| -0.01| 1.04| -| 100| third | 0.45| -0.04| 1.00| -0.11| 1.00| +sim\_df +------- -##### Vector (vars\*(vars-1)/2) +This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). [see vignette](sim_df.html) -You can specify your correlation matrix by hand as a vars\*(vars-1)/2 length vector, skipping the diagonal and lower left duplicate values. +For example, the following code creates a new sample from the built-in dataset `iris` with 50 observations of each species. ``` r -rho1_2 <- .3 -rho1_3 <- .5 -rho1_4 <- .5 -rho2_3 <- .2 -rho2_4 <- 0 -rho3_4 <- -.3 -cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4) -bvn <- rnorm_multi(100, 4, cmat, - varnames = letters[1:4]) +new_iris <- sim_df(iris, 50, "Species") ``` -| n| var | a| b| c| d| mean| sd| -|----:|:----|-----:|-----:|------:|------:|------:|-----:| -| 100| a | 1.00| 0.35| 0.55| 0.50| -0.13| 1.01| -| 100| b | 0.35| 1.00| 0.16| 0.09| -0.10| 1.05| -| 100| c | 0.55| 0.16| 1.00| -0.21| -0.19| 0.91| -| 100| d | 0.50| 0.09| -0.21| 1.00| 0.12| 0.97| +![Simulated iris dataset](README_files/figure-markdown_github/plot-iris-sim-1.png) -#### empirical +Additional functions +-------------------- -If you want your samples to have the *exact* correlations, means, and SDs you entered, set `empirical` to TRUE. - -``` r -bvn <- rnorm_multi(100, 5, .3, - varnames = letters[1:5], - empirical = T) -``` - -| n| var | a| b| c| d| e| mean| sd| -|----:|:----|----:|----:|----:|----:|----:|-----:|----:| -| 100| a | 1.0| 0.3| 0.3| 0.3| 0.3| 0| 1| -| 100| b | 0.3| 1.0| 0.3| 0.3| 0.3| 0| 1| -| 100| c | 0.3| 0.3| 1.0| 0.3| 0.3| 0| 1| -| 100| d | 0.3| 0.3| 0.3| 1.0| 0.3| 0| 1| -| 100| e | 0.3| 0.3| 0.3| 0.3| 1.0| 0| 1| - -### simdf - -This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). - -For example, here is the relationship between speed and distance in the built-in dataset `cars`. - -``` r -cars %>% - ggplot(aes(speed, dist)) + - geom_point() + - geom_smooth(method = "lm") -``` - -Original cars dataset -

-Original cars dataset -

+### check\_sim\_stats -You can create a new sample with the same parameters and 500 rows with the code `simdf(cars, 500)`. +If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`. ``` r -simdf(cars, 500) %>% - ggplot(aes(speed, dist)) + - geom_point() + - geom_smooth(method = "lm") +check_sim_stats(iris) +#> # A tibble: 4 x 8 +#> n var Sepal.Length Sepal.Width Petal.Length Petal.Width mean sd +#> +#> 1 150 Sepa… 1 -0.12 0.87 0.82 5.84 0.83 +#> 2 150 Sepa… -0.12 1 -0.43 -0.37 3.06 0.44 +#> 3 150 Peta… 0.87 -0.43 1 0.96 3.76 1.77 +#> 4 150 Peta… 0.82 -0.37 0.96 1 1.2 0.76 ``` -Simulated cars dataset -

-Simulated cars dataset -

- -#### Grouping Variables - -You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset `iris`. +You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header. ``` r -iris %>% - ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + - geom_point() + - geom_smooth(method = "lm") +check_sim_stats(iris, + between = "Species", + digits = 3, + usekable = TRUE) ``` -Original iris dataset -

-Original iris dataset -

- -And here is a new sample with 50 observations of each species, made with the code `simdf(iris, 100, "Species")`. - -``` r -simdf(iris, 50, "Species") %>% - ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + - geom_point() + - geom_smooth(method = "lm") -``` +| Species | n| var | Sepal.Length| Sepal.Width| Petal.Length| Petal.Width| mean| sd| +|:-----------|----:|:-------------|-------------:|------------:|-------------:|------------:|------:|------:| +| setosa | 50| Sepal.Length | 1.000| 0.743| 0.267| 0.278| 5.006| 0.352| +| setosa | 50| Sepal.Width | 0.743| 1.000| 0.178| 0.233| 3.428| 0.379| +| setosa | 50| Petal.Length | 0.267| 0.178| 1.000| 0.332| 1.462| 0.174| +| setosa | 50| Petal.Width | 0.278| 0.233| 0.332| 1.000| 0.246| 0.105| +| versicolor | 50| Sepal.Length | 1.000| 0.526| 0.754| 0.546| 5.936| 0.516| +| versicolor | 50| Sepal.Width | 0.526| 1.000| 0.561| 0.664| 2.770| 0.314| +| versicolor | 50| Petal.Length | 0.754| 0.561| 1.000| 0.787| 4.260| 0.470| +| versicolor | 50| Petal.Width | 0.546| 0.664| 0.787| 1.000| 1.326| 0.198| +| virginica | 50| Sepal.Length | 1.000| 0.457| 0.864| 0.281| 6.588| 0.636| +| virginica | 50| Sepal.Width | 0.457| 1.000| 0.401| 0.538| 2.974| 0.322| +| virginica | 50| Petal.Length | 0.864| 0.401| 1.000| 0.322| 5.552| 0.552| +| virginica | 50| Petal.Width | 0.281| 0.538| 0.322| 1.000| 2.026| 0.275| -Simulated iris dataset -

-Simulated iris dataset -

+### make\_id -For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you'd need to do any rounding or truncating yourself. +It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don't accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,..., S10 rather than S1, S10, S2, ...) This function returns a list of IDs that have the same string length and a specified prefix. ``` r -simdf(iris, 50, "Species") %>% - mutate_if(is.numeric, round, 1) %>% - ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) + - geom_point() + - geom_smooth(method = "lm") +make_id(n = 10, prefix = "ITEM_") +#> [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07" +#> [8] "ITEM_08" "ITEM_09" "ITEM_10" ``` -Simulated iris dataset (rounded) -

-Simulated iris dataset (rounded) -

- ### pos\_def\_limits Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C. @@ -250,6 +150,8 @@ lims <- pos_def_limits(.8, .2, 0, |:----|:----| | NA | NA | +### is\_pos\_def() + If you have a full matrix and want to know if it is positive definite, you can use the following code: ``` r @@ -270,68 +172,3 @@ matrix(c(1, .3, -.9, .2, is_pos_def() #> [1] FALSE ``` - -### check\_sim\_stats - -If you want to check your simulated stats or just describe an existing dataset, use `check_sim_stats()`. - -``` r -check_sim_stats(iris) -#> # A tibble: 4 x 8 -#> n var Sepal.Length Sepal.Width Petal.Length Petal.Width mean sd -#> -#> 1 150 Sepa… 1 -0.12 0.87 0.82 5.84 0.83 -#> 2 150 Sepa… -0.12 1 -0.43 -0.37 3.06 0.44 -#> 3 150 Peta… 0.87 -0.43 1 0.96 3.76 1.77 -#> 4 150 Peta… 0.82 -0.37 0.96 1 1.2 0.76 -``` - -You can also group your data and change the digits to round. Display the table using `knitr::kable()` by setting `usekable` to `TRUE` (remember to set `results='asis'` in the chunk header. - -``` r -check_sim_stats(iris, - grp_by = "Species", - digits = 3, - usekable = TRUE) -``` - -| Species | n| var | Sepal.Length| Sepal.Width| Petal.Length| Petal.Width| mean| sd| -|:-----------|----:|:-------------|-------------:|------------:|-------------:|------------:|------:|------:| -| setosa | 50| Sepal.Length | 1.000| 0.743| 0.267| 0.278| 5.006| 0.352| -| setosa | 50| Sepal.Width | 0.743| 1.000| 0.178| 0.233| 3.428| 0.379| -| setosa | 50| Petal.Length | 0.267| 0.178| 1.000| 0.332| 1.462| 0.174| -| setosa | 50| Petal.Width | 0.278| 0.233| 0.332| 1.000| 0.246| 0.105| -| versicolor | 50| Sepal.Length | 1.000| 0.526| 0.754| 0.546| 5.936| 0.516| -| versicolor | 50| Sepal.Width | 0.526| 1.000| 0.561| 0.664| 2.770| 0.314| -| versicolor | 50| Petal.Length | 0.754| 0.561| 1.000| 0.787| 4.260| 0.470| -| versicolor | 50| Petal.Width | 0.546| 0.664| 0.787| 1.000| 1.326| 0.198| -| virginica | 50| Sepal.Length | 1.000| 0.457| 0.864| 0.281| 6.588| 0.636| -| virginica | 50| Sepal.Width | 0.457| 1.000| 0.401| 0.538| 2.974| 0.322| -| virginica | 50| Petal.Length | 0.864| 0.401| 1.000| 0.322| 5.552| 0.552| -| virginica | 50| Petal.Width | 0.281| 0.538| 0.322| 1.000| 2.026| 0.275| - -### sim\_design - -Simulate data by specifying a design structure. - -**This function is under development and should be carefully checked!** - -``` r -between <- list("pet" = c("cat", "dog")) -within <- list("time" = c("day", "night")) -mu <- data.frame( - cat = c(10, 20), - dog = c(15, 25), - row.names = within$time -) -df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5) -``` - -| pet | n| var | day| night| mean| sd| -|:----|----:|:------|-----:|------:|------:|-----:| -| cat | 100| day | 1.00| 0.55| 10.39| 4.69| -| cat | 100| night | 0.55| 1.00| 19.78| 4.67| -| dog | 100| day | 1.00| 0.46| 15.02| 4.18| -| dog | 100| night | 0.46| 1.00| 24.79| 4.74| - 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z3Oj<+!MgTc)Y(||z=mqgf9**VPs=4}ZEZc;UXnVRfBHhO{XKkLXo1|~#U1n^V3NfgMRfr53BQEH(9p2$-bLhzmO+J_d~oxy+JJvoflJYg%GWLJJSG7q7Pe_{PcqFNx582S@~3m2Sen#_``3764R+ zd*7q49RHr+PacdNJckJ6_M2py|D#gSa^;xx

diff --git a/docs/articles/index.html b/docs/articles/index.html index 1b2dde89..ac00776b 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -82,9 +82,6 @@ @@ -123,10 +120,9 @@

All vignettes

diff --git a/docs/articles/intro.html b/docs/articles/intro.html index 4dbbf0d0..5a4af1c5 100644 --- a/docs/articles/intro.html +++ b/docs/articles/intro.html @@ -62,7 +62,7 @@ Simulate by Design
  • - Simulate from Existing Data + Simulate from Existing Data
    • @@ -85,7 +85,7 @@

    Introduction to faux

    Lisa DeBruine

    -

    2019-04-29

    +

    2019-05-01

    @@ -217,12 +217,12 @@

    -
    +

    -simdf

    -

    This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). see vignette

    +sim_df

    +

    This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). see vignette

    For example, the following code creates a new sample from the built-in dataset iris with 50 observations of each species.

    -
    new_iris <- simdf(iris, 50, "Species")
    +
    new_iris <- sim_df(iris, 50, "Species")
    Simulated iris dataset

    Simulated iris dataset @@ -246,7 +246,7 @@

    #> 4 150 Peta… 0.82 -0.37 0.96 1 1.2 0.76

    You can also group your data and change the digits to round. Display the table using knitr::kable() by setting usekable to TRUE (remember to set results='asis' in the chunk header.

    check_sim_stats(iris, 
-                grp_by = "Species", 
+                between = "Species", 
                 digits = 3, 
                 usekable = TRUE)
    @@ -397,6 +397,14 @@

    +
    +

    +make_id

    +

    It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don’t accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,…, S10 rather than S1, S10, S2, …) This function returns a list of IDs that have the same string length and a specified prefix.

    +
    make_id(n = 10, prefix = "ITEM_")
+#>  [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07"
+#>  [8] "ITEM_08" "ITEM_09" "ITEM_10"
    +

    pos_def_limits

    @@ -428,6 +436,10 @@

    NA +

    +
    +

    +is_pos_def()

    If you have a full matrix and want to know if it is positive definite, you can use the following code:

    c(.2, .3, .4, .2,
       .3, -.1, .2,
@@ -453,7 +465,7 @@
    diff --git a/docs/articles/rnorm_multi.html b/docs/articles/rnorm_multi.html index 7e30208c..4aa6a9b6 100644 --- a/docs/articles/rnorm_multi.html +++ b/docs/articles/rnorm_multi.html @@ -53,16 +53,13 @@ @@ -85,7 +82,7 @@

    Simulate Correlated Variables

    Lisa DeBruine

    -

    2019-04-29

    +

    2019-05-01

    diff --git a/docs/articles/sim_design.html b/docs/articles/sim_design.html index 2f1a48f0..091be14d 100644 --- a/docs/articles/sim_design.html +++ b/docs/articles/sim_design.html @@ -53,16 +53,13 @@ @@ -85,7 +82,7 @@

    Simulate by Design

    Lisa DeBruine

    -

    2019-04-29

    +

    2019-05-01

    @@ -179,6 +176,100 @@

    time = c(day = "Tested during the day", night = "Tested at night") )

    The long name for factor levels isn’t used yet, but will be used in future functions that create a codebook or plots.

    +

    If you don’t feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w*2w*2b withthe following code.

    +
    df <- sim_design(within = c(2,2), between = c(2), n = 5)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    sub_idCA1_B1A2_B1A1_B2A2_B2
    S01C10.1561683-0.3062499-2.91899381.4686767
    S02C10.29577200.0918673-1.31257911.2485948
    S03C10.11969822.1077554-0.7807512-1.3847227
    S04C1-0.3682321-1.1357070-0.21892450.0516151
    S05C1-2.7198911-1.04837260.80626740.0170679
    S06C20.79303221.7506883-1.4776848-1.0363013
    S07C2-0.3230935-0.47198801.03683592.1271924
    S08C2-0.0863936-0.3977303-0.56213650.2723021
    S09C21.61519191.0392466-0.6035584-0.3109535
    S10C20.9005048-0.8744312-0.5389020-0.4465484

    @@ -329,7 +420,7 @@

    )

    You can set frame_long = TRUE to return the data frame in long format, which is usually easier for plotting.

    df <- sim_design(within, between, n = 100, cors = cors, mu = mu, sd = 2, frame_long = TRUE)
    -

    +

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zuH(7z6I79FYiMZHaoj_0!UMMfTr$_!dhXcK2XBl5L;Ir6>Chk9{{$;B9=}2In!@haSyPG20Y0t{}tzk zgo8}wKBE6trymS-mrVuMUB;mfQ%tV_H~ITk`~fygL5mgdf0ayj)b;xZ!bNvsamPJ1B7~83; zLBd?;=iA>0PRCkDZ_Amvy5s}bd+q8;pvfHd`87#Zx!2dt&771RS^(Xo1c|u?uRuF3 zti1am8$OYP16FQ108cMs(lD6b)Pj_aQ4%L`9ZxB+WC@w8>W-8?5h(-AQZN82VR;rb zK>%L(Ao{gX)+#?>=3BM-i3iMXO!u&W*7zNW;Sy@Xl!Lm@;VSTGm4MP|GLU_F7`6Zt zXyXCUc;5_fMK)wxN^NL31&w*@4CN|=f@Jvfl#2Q`>N)qzF+)yN{0 z&KXj+0b0rgTIQFa$I6M+%|dmH199;LT&#I!se&Tz&=cHkpPY!>+Z;VWC5<~p zR9LVD99XPz6{s|NrDG|C+uMzxu`oI%ng{ + + + + + + +Simulate from Existing Data • faux + + + + + + + + + +

    +
    + + + +
    +
    +
    +

    Simulate from Existing Data

    +

    Lisa DeBruine

    + +

    2019-05-01

    + + + + +
    + + + +

    The sim_df() function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).

    +

    For example, here is the relationship between speed and distance in the built-in dataset cars.

    +
    cars %>%
+  ggplot(aes(speed, dist)) + 
+  geom_point() +
+  geom_smooth(method = "lm")
    +
    +Original cars dataset

    +Original cars dataset +

    +
    +

    You can create a new sample with the same parameters and 500 rows with the code sim_df(cars, 500).

    +
    sim_df(cars, 500) %>%
+  ggplot(aes(speed, dist)) + 
+    geom_point() +
+    geom_smooth(method = "lm")
    +
    +Simulated cars dataset

    +Simulated cars dataset +

    +
    +
    +

    +Grouping Variables

    +

    You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset iris.

    +
    iris %>%
+  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
+  geom_point() +
+  geom_smooth(method = "lm")
    +
    +Original iris dataset

    +Original iris dataset +

    +
    +

    And here is a new sample with 50 observations of each species, made with the code sim_df(iris, 100, "Species").

    +
    sim_df(iris, 50, "Species") %>%
+  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
+  geom_point() +
+  geom_smooth(method = "lm")
    +
    +Simulated iris dataset

    +Simulated iris dataset +

    +
    +

    For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you’d need to do any rounding or truncating yourself.

    +
    sim_df(iris, 50, "Species") %>%
+  mutate_if(is.numeric, round, 1) %>%
+  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
+  geom_point() +
+  geom_smooth(method = "lm")
    +
    +Simulated iris dataset (rounded)

    +Simulated iris dataset (rounded) +

    +
    +
    +
    + + + +
    + + +
    +

    Developed by Lisa DeBruine.

    +
    + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    +
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    +

    This function creates a dataset with a specific between- and within-subjects design. see vignette

    +

    For example, the following creates a 2w*2b design with 100 observations in each cell. The between-subject factor is pet with twolevels of cat and dog. The within-subject factor is time with two levels of day and night. The mean for the cat_day cell is 10, the mean for the cat_night cell is 20, the mean for the dog_day cell is 15, and the mean for the dog_night cell is 25. All cells have a SD of 5 and all within-subject cells are correlated r = 0.5. The resulting data has exactly these values (set empirical = FALSE to sample from a population with these values).

    +
    between <- list("pet" = c("cat", "dog"))
+within <- list("time" = c("day", "night"))
+mu <- data.frame(
+  cat = c(10, 20),
+  dog = c(15, 25),
+  row.names = within$time
+)
+df <- sim_design(within, between, 
+                 n = 100, cors = 0.5, mu = mu, sd = 5,
+                 empirical = TRUE)
    - + + - - - - - + + + - - - - - - - - + + + + + + - - - - - - - - + + + + + + - - - - - - - - + + + + + + - - - - - - - - - - - - - - - - - - - + + + + +
    Sample stats from a single rhoSample sim_design() stats
    pet n varabcdedaynight mean sd
    cat 100a1.000.350.220.450.37-0.041.09day1.00.5105
    cat 100b0.351.000.190.360.28-0.050.83night0.51.0205
    dog 100c0.220.191.000.260.200.011.08day1.00.5155
    dog 100d0.450.360.261.000.240.001.00
    100e0.370.280.200.241.000.040.97night0.51.0255
    -
    -
    -Matrix
    -

    If you already have a correlation matrix, such as the output of cor(), you can specify the simulated data with that.

    -
    cmat <- cor(iris[,1:4])
-bvn <- rnorm_multi(100, 4, cmat, 
-                  varnames = colnames(cmat))
    +
    +

    +rnorm_multi

    +

    This function makes multiple normally distributed vectors with specified parameters and relationships.see vignette

    +

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

    +
    
+dat <- rnorm_multi(
+  n = 100, 
+  cors = c(0.5, 0.5, 0.25), 
+  mu = c(0, 20, 20),
+  sd = c(1, 5, 5),
+  varnames = c("A", "B", "C"),
+  empirical = FALSE
+)
    - + - - - - + + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + - - - -
    Sample stats from a correlation matrixSample rnorm_multi() stats
    n varSepal.LengthSepal.WidthPetal.LengthPetal.WidthABC mean sd
    100Sepal.Length1.00-0.100.880.83-0.011.05
    100Sepal.Width-0.101.00-0.38-0.29-0.191.09
    100Petal.Length0.88-0.381.000.96-0.011.02
    100Petal.Width0.83-0.290.96A 1.000.620.46 -0.050.98
    -
    -
    -
    -Vector (vars*vars)
    -

    You can specify your correlation matrix by hand as a vars*vars length vector, which will include the correlations of 1 down the diagonal.

    -
    cmat <- c(1, .3, .5,
-          .3, 1, 0,
-          .5, 0, 1)
-bvn <- rnorm_multi(100, 3, cmat, 
-                  varnames = c("first", "second", "third"))
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Sample stats from a vars*vars vector
    nvarfirstsecondthirdmeansd
    100first1.000.330.45-0.121.01
    100second0.331.00-0.04-0.011.04
    100third0.45-0.041.00-0.111.00
    -
    -
    -
    -Vector (vars*(vars-1)/2)
    -

    You can specify your correlation matrix by hand as a vars*(vars-1)/2 length vector, skipping the diagonal and lower left duplicate values.

    -
    rho1_2 <- .3
-rho1_3 <- .5
-rho1_4 <- .5
-rho2_3 <- .2
-rho2_4 <- 0
-rho3_4 <- -.3
-cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4)
-bvn <- rnorm_multi(100, 4, cmat, 
-                  varnames = letters[1:4])
    - - - - - - - - - - - - - - - - - - - - - - + - - + + - - - - + + + - - - - - - - - - - - - - - + + + - - + +
    Sample stats from a (vars*(vars-1)/2) vector
    nvarabcdmeansd
    100a1.000.350.550.50-0.131.011.08
    100b0.35B0.62 1.000.160.09-0.101.050.1919.955.38
    100c0.550.161.00-0.21-0.190.91
    100d0.500.09-0.21C0.460.19 1.000.120.9719.815.15
    -
    -
    -

    -empirical

    -

    If you want your samples to have the exact correlations, means, and SDs you entered, set empirical to TRUE.

    -
    bvn <- rnorm_multi(100, 5, .3, 
-                  varnames = letters[1:5], 
-                  empirical = T)
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    Sample stats with empirical = TRUE
    nvarabcdemeansd
    100a1.00.30.30.30.301
    100b0.31.00.30.30.301
    100c0.30.31.00.30.301
    100d0.30.30.31.00.301
    100e0.30.30.30.31.001
    -
    - -
    -

    -simdf

    -

    This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).

    -

    For example, here is the relationship between speed and distance in the built-in dataset cars.

    -
    cars %>%
-  ggplot(aes(speed, dist)) + 
-  geom_point() +
-  geom_smooth(method = "lm")
    -
    -Original cars dataset

    -Original cars dataset -

    -
    -

    You can create a new sample with the same parameters and 500 rows with the code simdf(cars, 500).

    -
    simdf(cars, 500) %>%
-  ggplot(aes(speed, dist)) + 
-    geom_point() +
-    geom_smooth(method = "lm")
    -
    -Simulated cars dataset

    -Simulated cars dataset -

    -
    -
    -

    -Grouping Variables

    -

    You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset iris.

    -
    iris %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
    -
    -Original iris dataset

    -Original iris dataset -

    -
    -

    And here is a new sample with 50 observations of each species, made with the code simdf(iris, 100, "Species").

    -
    simdf(iris, 50, "Species") %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
    +
    +

    +sim_df

    +

    This function produces a dataframe with the same distributions and correlations as an existing dataframe. It only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now). see vignette

    +

    For example, the following code creates a new sample from the built-in dataset iris with 50 observations of each species.

    +
    new_iris <- sim_df(iris, 50, "Species")
    -Simulated iris dataset

    +Simulated iris dataset

    Simulated iris dataset

    -

    For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you’d need to do any rounding or truncating yourself.

    -
    simdf(iris, 50, "Species") %>%
-  mutate_if(is.numeric, round, 1) %>%
-  ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
-  geom_point() +
-  geom_smooth(method = "lm")
    -
    -Simulated iris dataset (rounded)

    -Simulated iris dataset (rounded) -

    -
    -
    -
    -
    -

    -pos_def_limits

    -

    Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C.

    -

    The function pos_def_limits() lets you know what the possible range of values is for the missing value in a correlation matrix with one missing value. The correlation values are entered just from the top right triangle of the matrix, with a single NA for the missing value.

    -
    lims <- pos_def_limits(.8, .2, NA)
    - - - - - - - - - -
    minmax
    -0.4270.747
    -

    For example, if rAB = 0.8 and rAC = 0.2, then -0.427 <= rBC <= 0.747.

    -

    If you enter a correlation matrix that contains impossible combinations, your limits will be NA.

    -
    lims <- pos_def_limits(.8, .2,  0,
-                          -.5, NA,
-                               .2)
    - - - - - - - - - -
    minmax
    NANA
    -

    If you have a full matrix and want to know if it is positive definite, you can use the following code:

    -
    c(.2, .3, .4, .2,
-      .3, -.1, .2,
-           .4, .5,
-               .3) %>%
-  cormat_from_triangle() %>%
-  is_pos_def()
-#> [1] TRUE
    -
    matrix(c(1, .3, -.9, .2,
-        .3,  1,  .4, .5,
-       -.9, .4,   1, .3,
-        .2, .5,  .3,  1), 4) %>%
-  is_pos_def()
-#> [1] FALSE
    +
    +

    +Additional functions

    check_sim_stats

    @@ -626,7 +237,7 @@

    #> 4 150 Peta… 0.82 -0.37 0.96 1 1.2 0.76

    You can also group your data and change the digits to round. Display the table using knitr::kable() by setting usekable to TRUE (remember to set results='asis' in the chunk header.

    check_sim_stats(iris, 
-                grp_by = "Species", 
+                between = "Species", 
                 digits = 3, 
                 usekable = TRUE)
    @@ -777,70 +388,63 @@

    -
    +

    -sim_design

    -

    Simulate data by specifying a design structure.

    -

    This function is under development and should be carefully checked!

    -
    between <- list("pet" = c("cat", "dog"))
-within <- list("time" = c("day", "night"))
-mu <- data.frame(
-  cat = c(10, 20),
-  dog = c(15, 25),
-  row.names = within$time
-)
-df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5)
    +make_id +

    It is useful for IDs for random effects (e.g., subjects or stimuli) to be character strings (so you don’t accidentally include them as fixed effects) with the same length s(o you can sort them in order like S01, S02,…, S10 rather than S1, S10, S2, …) This function returns a list of IDs that have the same string length and a specified prefix.

    +
    make_id(n = 10, prefix = "ITEM_")
+#>  [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07"
+#>  [8] "ITEM_08" "ITEM_09" "ITEM_10"
    +
    +
    +

    +pos_def_limits

    +

    Not all correlation matrices are possible. For example, if variables A and B are correlated with r = 1.0, then the correlation between A and C can only be exactly equal to the correlation between B and C.

    +

    The function pos_def_limits() lets you know what the possible range of values is for the missing value in a correlation matrix with one missing value. The correlation values are entered just from the top right triangle of the matrix, with a single NA for the missing value.

    +
    lims <- pos_def_limits(.8, .2, NA)
    - - - - - - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + +
    petnvardaynightmeansdminmax
    cat100day1.000.5510.394.69
    cat100night0.551.0019.784.67
    dog100day1.000.4615.024.18
    dog100night0.461.0024.794.74
    -0.4270.747
    +

    For example, if rAB = 0.8 and rAC = 0.2, then -0.427 <= rBC <= 0.747.

    +

    If you enter a correlation matrix that contains impossible combinations, your limits will be NA.

    +
    lims <- pos_def_limits(.8, .2,  0,
+                          -.5, NA,
+                               .2)
    + + + + + + + + +
    minmax
    NANA
    -

    +
    +

    +is_pos_def()

    +

    If you have a full matrix and want to know if it is positive definite, you can use the following code:

    +
    c(.2, .3, .4, .2,
+      .3, -.1, .2,
+           .4, .5,
+               .3) %>%
+  cormat_from_triangle() %>%
+  is_pos_def()
+#> [1] TRUE
    +
    matrix(c(1, .3, -.9, .2,
+        .3,  1,  .4, .5,
+       -.9, .4,   1, .3,
+        .2, .5,  .3,  1), 4) %>%
+  is_pos_def()
+#> [1] FALSE
    @@ -861,7 +465,13 @@

    Developers

    - +
    +

    Dev status

    +
      +
    • Lifecycle: experimental
      • +
    +
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a/docs/news/index.html b/docs/news/index.html index 1970a793..f4aecd31 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -82,9 +82,6 @@

    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml index 59781696..ba63c6ac 100644 --- a/docs/pkgdown.yml +++ b/docs/pkgdown.yml @@ -2,8 +2,7 @@ pandoc: 1.19.2.1 pkgdown: 1.3.0 pkgdown_sha: ~ articles: - intro: intro.html rnorm_multi: rnorm_multi.html sim_design: sim_design.html - simdf: simdf.html + sim_df: sim_df.html diff --git a/docs/reference/check_design.html b/docs/reference/check_design.html index 9743ebb2..97b90867 100644 --- a/docs/reference/check_design.html +++ b/docs/reference/check_design.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/check_sim_stats.html b/docs/reference/check_sim_stats.html index d95cc2e0..4f597133 100644 --- a/docs/reference/check_sim_stats.html +++ b/docs/reference/check_sim_stats.html @@ -85,9 +85,6 @@ @@ -129,7 +126,8 @@

    Check table stats

    - 
    check_sim_stats(dat, grp_by = NULL, digits = 2, usekable = FALSE)
    + 
    check_sim_stats(dat, between = c(), within = c(), dv = c(),
+  id = c(), digits = 2, usekable = FALSE)

    Arguments

    @@ -139,8 +137,20 @@

    Arg

    - - + + + + + + + + + + + + + + diff --git a/docs/reference/convert_param.html b/docs/reference/convert_param.html index 92e13ce4..b4fc1268 100644 --- a/docs/reference/convert_param.html +++ b/docs/reference/convert_param.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/cormat.html b/docs/reference/cormat.html index c6b0a676..cf441b57 100644 --- a/docs/reference/cormat.html +++ b/docs/reference/cormat.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/cormat_from_triangle.html b/docs/reference/cormat_from_triangle.html index f2797f34..5f14ae71 100644 --- a/docs/reference/cormat_from_triangle.html +++ b/docs/reference/cormat_from_triangle.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/faceratings.html b/docs/reference/faceratings.html index 481c039d..600c9ea5 100644 --- a/docs/reference/faceratings.html +++ b/docs/reference/faceratings.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/fix_name_labels.html b/docs/reference/fix_name_labels.html index fec99c75..2c229b5b 100644 --- a/docs/reference/fix_name_labels.html +++ b/docs/reference/fix_name_labels.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/index.html b/docs/reference/index.html index 2e76c849..8f003b04 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -82,9 +82,6 @@ @@ -177,6 +174,19 @@

    long2wide()

    + +

    + + + + + + @@ -208,7 +218,7 @@

    simdf()

    +

    sim_df()

    diff --git a/docs/reference/is_pos_def.html b/docs/reference/is_pos_def.html index 375742c3..e7c50532 100644 --- a/docs/reference/is_pos_def.html +++ b/docs/reference/is_pos_def.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/long2wide.html b/docs/reference/long2wide.html new file mode 100644 index 00000000..f6a1ab50 --- /dev/null +++ b/docs/reference/long2wide.html @@ -0,0 +1,199 @@ + + + + + + + + +Long to wide format +Converts a table from long to wide format — long2wide • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + +
    + +
    +
    +
    +

    Long to wide format +Converts a table from long to wide format

    + + +
    + +
    + +

    Long to wide format +Converts a table from long to wide format

    + +
    + + 
    long2wide(dat, between = c(), within = c(), dv = c(), id = c())
    + +

    Arguments

    +

    the existing dataframe
    grp_by

    an optional list of column names to group by

    		between

    a vector of column names for between-subject factors

    within

    a vector of column names for within-subject factors (if data is long)

    dv

    the column name of the dv (if data is long)

    id

    the column name(s) of the subject ID (if data is long)
    digits

    Long to wide format +Converts a table from long to wide format

    +

    make_id()

    +

    Make ID

    pos_def_limits()

    Simulate an existing dataframe
    + + + + + + + + + + + + + + + + + + + + + +
    dat

    the existing dataframe in long format

    between

    a vector of column names for between-subject factors

    within

    a vector of column names for within-subject factors

    dv

    the column name of the dv

    id

    the column name(s) of the subject ID

    + +

    Value

    + +

    tibble in wide format

    + + +

    Examples

    + 
    
+df_long <- sim_design(2,2, frame_long = TRUE)
+df_wide <- long2wide(df_long, "B", "A", "val", "sub_id")
    + + + + +
    +
    +

    Developed by Lisa DeBruine.

    +
    + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    + + + + + + + diff --git a/docs/reference/make_id.html b/docs/reference/make_id.html new file mode 100644 index 00000000..d101cb3c --- /dev/null +++ b/docs/reference/make_id.html @@ -0,0 +1,185 @@ + + + + + + + + +Make ID — make_id • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + +
    + +
    +
    +
    +

    Make ID

    + + +
    + +
    + +

    Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S002, ..., S100).

    + +
    + + 
    make_id(n = 100, prefix = "S")
    + +

    Arguments

    + + + + + + + + + + +
    n

    the number of IDs to generate

    prefix

    the letter prefix to the number

    + +

    Value

    + +

    a vector of IDs

    + + +

    Examples

    + 
    
+make_id(20, "SUBJECT_")
    #>  [1] "SUBJECT_01" "SUBJECT_02" "SUBJECT_03" "SUBJECT_04" "SUBJECT_05"
+#>  [6] "SUBJECT_06" "SUBJECT_07" "SUBJECT_08" "SUBJECT_09" "SUBJECT_10"
+#> [11] "SUBJECT_11" "SUBJECT_12" "SUBJECT_13" "SUBJECT_14" "SUBJECT_15"
+#> [16] "SUBJECT_16" "SUBJECT_17" "SUBJECT_18" "SUBJECT_19" "SUBJECT_20"
    
+
    +
    + +
    + +
    +
    +

    Developed by Lisa DeBruine.

    +
    + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    +
    + + + + + + diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 9592e06d..27b4a213 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/pos_def_limits.html b/docs/reference/pos_def_limits.html index 2c4957db..f89a849d 100644 --- a/docs/reference/pos_def_limits.html +++ b/docs/reference/pos_def_limits.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/rnorm_multi.html b/docs/reference/rnorm_multi.html index 4b74b783..2c2b8f00 100644 --- a/docs/reference/rnorm_multi.html +++ b/docs/reference/rnorm_multi.html @@ -85,9 +85,6 @@ @@ -176,206 +173,206 @@

    Value

    Examples

    rnorm_multi(100, 3, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
    #>                A            B           C
-#> 1    0.750792572  1.424873995  1.01619162
-#> 2   -1.042243673 -0.275816637  0.52639710
-#> 3    0.878491981  2.434684102  2.13943193
-#> 4   -0.500509231  0.536184495 -0.07283162
-#> 5   -1.099839859  1.395668514 -1.63782640
-#> 6   -1.268203415 -0.516346650 -0.90254750
-#> 7    1.062341096  2.085361364  1.05389880
-#> 8   -0.072670487  0.713209683 -0.06669068
-#> 9    0.799007438  0.832138301 -0.84904571
-#> 10   0.274439429 -1.305844900  1.50703213
-#> 11   0.483758950  0.961135166 -0.09054153
-#> 12  -1.335560745  0.410231530 -0.60767290
-#> 13  -1.365394967 -1.989742102 -1.39808947
-#> 14   1.058685530  1.942674898  0.79418602
-#> 15  -0.059286958 -0.896242839 -0.20963410
-#> 16   2.732876265  0.225408869  1.48006414
-#> 17   0.520040403 -0.045099167  0.69894364
-#> 18  -1.261009283  0.795477058  0.38156161
-#> 19   0.152387337 -0.001507871 -1.21894494
-#> 20   1.153452339 -0.243123670  1.18059995
-#> 21   1.221036402 -1.329257135 -0.72696920
-#> 22   0.097150590 -0.488381489 -0.38403747
-#> 23   1.847044170  0.215193119  1.03746265
-#> 24  -0.213594069 -0.833126883 -0.60370775
-#> 25  -0.944882567 -1.108280341 -2.13611782
-#> 26  -0.203627401 -0.399418526  0.70486850
-#> 27   0.971899030  0.251539268  0.93124695
-#> 28  -0.067710098  0.330687769 -0.20521675
-#> 29   0.008234813  0.643985743  1.10851032
-#> 30   1.491364279  1.606960145  0.51407350
-#> 31  -1.491314723 -0.173569149 -0.59828848
-#> 32   0.788400822 -0.100235777 -0.87821239
-#> 33  -0.275325098 -0.506920585  0.16149729
-#> 34  -1.646030985  0.066253801 -2.08692166
-#> 35  -0.763940091  0.400133152  0.01055207
-#> 36   0.559861711  0.094147624 -0.24183194
-#> 37   0.851096610  2.217705492  1.27999821
-#> 38  -0.174916339  0.113335133  0.60261481
-#> 39   0.579792609 -0.678784282  0.78461866
-#> 40  -0.270889155 -0.210612089 -1.77436354
-#> 41   0.045616422  0.372504963 -0.50383699
-#> 42  -0.469471970  0.436848174  1.27704375
-#> 43   1.116862858 -1.388382465  0.45742838
-#> 44   0.435540978 -0.674785328  0.76644032
-#> 45   0.733199928 -0.330524626 -1.17879303
-#> 46  -1.202678340 -3.307972897 -1.77046080
-#> 47  -0.829440876  0.569422843  0.04572102
-#> 48   0.001784459 -0.684587716 -0.57477015
-#> 49  -1.255359394  0.562293801  0.23466705
-#> 50   2.329409361  0.563363357  1.59377323
-#> 51  -0.074278634 -0.571594535 -1.19274011
-#> 52   0.364196656  0.305387342 -0.05428873
-#> 53  -0.399415335  1.216792632 -0.33993885
-#> 54  -0.237192644  0.568221498 -0.35410471
-#> 55   0.395099319 -1.135493621  0.63054444
-#> 56   0.309387409 -0.471324983 -0.94209682
-#> 57   1.467751541  1.584594817  2.09891298
-#> 58  -1.921116085 -1.649349291 -2.52231252
-#> 59   0.936534595  0.074720566 -0.05360963
-#> 60   0.095504143 -1.141376090  0.49330431
-#> 61  -2.100395036  0.162246518 -0.70544430
-#> 62   1.437719944  0.530497597 -0.22212529
-#> 63  -0.956070367 -0.668411888 -0.93985501
-#> 64   0.399297618  0.122294316  0.08451302
-#> 65  -0.286806047  2.194503989  0.68953463
-#> 66   0.569197516  1.947087832 -0.16302798
-#> 67  -1.949290727 -0.897080260  0.12529717
-#> 68   0.394865364  0.065765899 -0.62937094
-#> 69  -0.503900977 -0.175240568 -0.45048740
-#> 70   1.439840970  0.910978267  1.54359768
-#> 71   0.887303297  1.141197444  1.30315927
-#> 72  -0.380841256  1.126287256 -1.27061154
-#> 73   0.046357782 -1.651026981 -1.30496243
-#> 74   0.501969183 -0.740051843 -0.21905098
-#> 75  -0.327677152 -1.412160664 -1.18738515
-#> 76  -0.041519929 -1.656652359  0.41972821
-#> 77  -1.237010922 -1.007396285  0.60385664
-#> 78  -0.999395609  0.507528583  0.54872639
-#> 79  -0.508546869 -0.250979903  0.26216982
-#> 80   1.706916012 -1.013138961 -0.24417856
-#> 81  -2.250638781 -1.264743365 -1.06547039
-#> 82  -1.465750791 -0.014902364 -1.49434831
-#> 83  -1.819797525  0.131454869 -0.23351058
-#> 84   0.644464430 -1.365010318 -0.43015447
-#> 85   0.673776094 -0.023604894  0.61492934
-#> 86   0.424058255 -2.337033827 -0.53427314
-#> 87   1.838236640  2.403650450  1.78253998
-#> 88   1.880351381 -1.351154782  0.05113393
-#> 89   0.143202959 -1.430192202  0.26174046
-#> 90   1.155769129 -0.186903327  0.04880420
-#> 91   0.559813595 -0.862499677 -0.53711912
-#> 92  -0.936341076 -0.503527111 -0.99627787
-#> 93  -1.049096344  0.032194348 -1.34962850
-#> 94  -0.497978396  0.162235981  0.31899526
-#> 95   0.565120969 -1.919780662  0.25948394
-#> 96  -0.664895124 -0.766542929 -2.21344800
-#> 97   0.829280403  0.592403315 -0.43854944
-#> 98  -0.017677656 -1.656619109 -0.45664659
-#> 99  -0.131923116 -1.896881557 -0.91048949
-#> 100  0.146219686 -0.896684910  1.27558673
    rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
    #>               A             B           C
-#> 1    1.39081618  1.2987554842 -0.54274184
-#> 2    0.12474394 -1.4341962212 -0.83385076
-#> 3   -1.31234622 -0.1773139308  1.11548187
-#> 4    0.13764915  1.4386412100  1.18934079
-#> 5   -1.55271429 -0.7649622047 -0.14994138
-#> 6   -0.69104513 -1.1444681510 -0.39532881
-#> 7    0.28331723  0.6930376611  0.78522733
-#> 8    0.62698282  2.6282635812  2.09300595
-#> 9   -1.35745434  0.4066573669  2.08806190
-#> 10  -0.06698887 -2.4653818490 -1.20034841
-#> 11   0.02483736 -0.0410126675 -0.86813242
-#> 12   0.79142685 -1.8084817841 -0.37123506
-#> 13  -0.88409828 -0.1531384713  0.49194054
-#> 14   0.60358179 -1.3450087188 -1.86485444
-#> 15   0.74467121  1.2994217211  0.32121665
-#> 16   0.26231671  0.7238420836 -0.21253993
-#> 17  -0.08841355  0.8852067209  1.38717738
-#> 18  -1.18692758 -1.5262910813 -0.22392502
-#> 19   1.02473345  0.3596218704 -1.36911133
-#> 20   0.29173164  0.0537979231  0.48062427
-#> 21  -0.10151671  1.0013139089  0.41741153
-#> 22  -0.38409912  0.1086193588  0.27613213
-#> 23  -0.17473214  1.3226328277  1.56677087
-#> 24  -0.68841302 -0.8568191519  0.22462907
-#> 25  -3.40268201 -1.1503624457  2.19509962
-#> 26   1.27476703 -0.1608727574 -1.60243175
-#> 27  -0.76774044 -0.3136043209  0.40748355
-#> 28  -2.93463796  0.0002708164  0.54330948
-#> 29  -0.36309562  0.4812979048  0.08769210
-#> 30   0.11235349  1.2436686101  0.40667721
-#> 31  -0.42112919  1.1241955619  0.82149954
-#> 32   0.65132497 -1.9692499386 -1.72175518
-#> 33  -0.39001734  1.2671731043  1.01569959
-#> 34   1.05444567  0.7305534127 -0.08594560
-#> 35  -0.65195625 -0.5195396901  0.41514849
-#> 36  -0.95833910  0.2003833523  1.41935582
-#> 37  -0.09312194 -1.7133185727 -0.91550462
-#> 38   0.15254009 -0.8956400361 -1.25958623
-#> 39   0.07535271  0.1033439817  0.16790440
-#> 40  -1.61236803 -1.1266807862  1.13697372
-#> 41  -0.53493316 -1.6457716779  0.28787366
-#> 42  -0.69921708  1.7978716255  1.97787573
-#> 43  -0.58102874 -1.3412768754  0.28024767
-#> 44   0.39080373 -0.9614140080 -1.51340506
-#> 45   0.98098214 -0.2651307385 -1.15693664
-#> 46  -1.41159592  0.9114650277  1.44179604
-#> 47  -1.05225142 -0.9418931935 -0.20252229
-#> 48  -0.40068116 -1.1950503292 -0.24799347
-#> 49   1.38669506  0.9589220188  0.06210057
-#> 50  -0.35464447  0.8920651923  0.55778884
-#> 51   1.53904431  1.2176546707 -0.77686030
-#> 52   0.16578814  0.4331779127 -0.40893746
-#> 53  -0.60914155 -0.8140949898  1.01178545
-#> 54  -2.16814062 -0.2863029111  1.76151931
-#> 55  -0.85887053  0.7286470557  1.46281376
-#> 56  -2.69493701 -0.5352409200  1.22842833
-#> 57   0.25329846 -0.2477709459 -0.33847285
-#> 58  -1.42870286  0.8775450724  1.65829026
-#> 59   1.59072274  0.1852101502 -0.06485567
-#> 60   0.19047954 -0.5331432083 -1.01246796
-#> 61   1.12633124  0.4510249139 -0.16385936
-#> 62  -0.86502391  0.4564717361  0.97700377
-#> 63   0.41198377  2.1574189238  0.29625434
-#> 64  -1.33266567  0.1334556024  2.13446398
-#> 65  -1.26258869 -0.1687697156 -0.84856895
-#> 66  -1.02980626  0.6029517902  0.65050954
-#> 67   0.38682167 -1.3061125155 -1.84341380
-#> 68   0.23417755 -0.4236220335 -0.99612575
-#> 69   0.38936213  1.4383517669  1.20765999
-#> 70   0.52613652  0.4669543053 -0.25615525
-#> 71  -0.78625593 -0.1890983901 -0.06583813
-#> 72  -0.52607091 -0.7252667231 -1.05297080
-#> 73  -0.28890582 -1.0441594449 -1.15239430
-#> 74  -1.25168046 -2.1088896749 -1.11497680
-#> 75  -0.67847213 -1.4529646435 -0.09410188
-#> 76  -0.99112777  0.2271702323  1.19094153
-#> 77  -0.27328045 -0.6278109929  0.89942994
-#> 78  -0.05788188 -0.2011886425 -0.70444974
-#> 79   0.44426016  0.3086371578 -0.14094071
-#> 80  -1.86391621 -0.2017716705  1.04344653
-#> 81  -0.52027246  0.1144370542  0.17047033
-#> 82   0.68790768  0.3227648343 -0.35982484
-#> 83   0.06379928  1.0963218557 -0.79995582
-#> 84   0.71240232  0.6765428974  0.08052656
-#> 85   1.00680982  1.3004044786  0.46945908
-#> 86  -0.19307208 -0.8284714031  0.42528039
-#> 87   0.58798451  0.2443710099  0.22379468
-#> 88   0.29882724  0.8581150980  1.22292625
-#> 89   1.84074363  1.0970181676 -0.69553217
-#> 90  -1.91299092 -1.2784569227  0.17807915
-#> 91   0.64255687 -0.5425659342  0.03429620
-#> 92  -0.83135155  0.4205869612  0.80329005
-#> 93  -1.44298017  0.7499570038  1.75007779
-#> 94  -0.80721771 -1.3782307639 -0.83537525
-#> 95  -0.56849692 -0.2160438395 -0.20213552
-#> 96  -1.43237796  1.0144647169  1.92039006
-#> 97  -0.17925759 -1.5018571840 -1.44550197
-#> 98  -0.35894894  0.9200034445  1.16485148
-#> 99   0.03204885 -0.5520698956  0.01677568
-#> 100  0.83799420  1.2748026410 -0.90440295
    +#> 1 -1.711924939 -1.011599824 -1.32018668 +#> 2 0.887134792 0.043794457 0.99462051 +#> 3 0.748427556 0.410991509 1.01598343 +#> 4 -0.988547303 -1.612324193 0.10827445 +#> 5 0.250415810 1.967522573 1.12127447 +#> 6 1.012473136 1.329047164 0.46137529 +#> 7 0.290391321 -0.492076253 -1.07257786 +#> 8 -0.999779485 -2.143044059 -1.61909580 +#> 9 1.296369128 0.223297040 0.05172819 +#> 10 1.844675802 0.525323939 1.43792983 +#> 11 -0.594332509 1.134701125 -0.51513681 +#> 12 2.892055704 -0.024653246 -0.87388294 +#> 13 0.674912545 1.145791522 -0.16442269 +#> 14 0.114929596 0.751160451 0.22516804 +#> 15 -0.798672240 -0.534844472 -1.59558698 +#> 16 -1.031496700 0.049687627 -0.54144674 +#> 17 -0.004310488 -1.375989084 0.16455645 +#> 18 1.415566785 1.598106413 1.06560667 +#> 19 -1.642731803 -0.241490494 -0.33724621 +#> 20 0.910502722 -0.191788473 -0.98662517 +#> 21 -1.175150978 -0.479527388 0.14625695 +#> 22 0.305901881 0.761645013 -0.54205693 +#> 23 -0.367942472 -1.256019905 -0.12063644 +#> 24 0.892308000 0.193255995 1.16876289 +#> 25 2.538452134 2.240282666 2.04947504 +#> 26 -0.695068465 0.222697327 -1.11980217 +#> 27 0.512021718 0.508770162 0.57718656 +#> 28 -0.932274424 2.643562030 2.24879422 +#> 29 -0.585604074 0.622505249 -0.24917585 +#> 30 -1.355941412 -0.590585765 -0.21519908 +#> 31 -0.733846362 0.082328508 -0.42830930 +#> 32 1.264872249 0.715577491 0.12590367 +#> 33 -0.518786456 0.201122493 -0.92638734 +#> 34 -0.637506919 -1.122500780 -0.86513473 +#> 35 0.632266473 -0.010632207 1.05787745 +#> 36 1.104769095 0.406299810 -0.21379747 +#> 37 1.472185167 0.971669943 0.38215346 +#> 38 0.208642678 1.232615573 -0.26677942 +#> 39 0.194660642 -0.021702220 -0.36527459 +#> 40 1.781764439 0.408108369 1.86009067 +#> 41 2.042761937 -0.215878860 1.47377857 +#> 42 -0.490815530 -0.425984391 -0.68877135 +#> 43 1.655781000 -0.033659668 1.27356903 +#> 44 -0.025382344 0.905039301 -0.02548762 +#> 45 -0.665268785 -0.563017316 0.06761990 +#> 46 -0.276302646 0.608159422 0.33566043 +#> 47 0.484250987 1.126346311 1.25406657 +#> 48 1.282907646 0.720836758 0.47160057 +#> 49 -0.277030402 -1.021860386 -1.96611414 +#> 50 -0.819145743 -0.037232249 -0.04063296 +#> 51 -1.564663610 -0.636464908 -1.85042764 +#> 52 -0.898058218 0.284764624 -0.34268128 +#> 53 1.805225599 -0.408926161 0.83297444 +#> 54 1.365198223 1.113118376 1.19018362 +#> 55 0.216231182 -0.115908105 0.10565315 +#> 56 0.641593299 1.738105080 2.21714184 +#> 57 0.213674643 0.168441446 -0.31953324 +#> 58 0.253013272 0.862077906 -0.22789514 +#> 59 0.287070174 -1.951729555 -0.83911657 +#> 60 -0.481794003 0.239976721 0.61024139 +#> 61 0.025132801 -0.628056375 -1.53689844 +#> 62 -0.167673231 -0.101515541 0.75379782 +#> 63 -2.425362627 -0.498203692 -1.06272155 +#> 64 1.350643220 -0.486047718 0.95719724 +#> 65 -1.242626738 2.175710297 0.92752367 +#> 66 -0.338836433 1.113640640 -0.15518463 +#> 67 -0.026841819 0.966138768 0.37921477 +#> 68 -0.453608406 0.413598279 0.23549706 +#> 69 -0.493723033 -1.334332953 -0.84630857 +#> 70 -1.221646818 -1.238780109 0.72514498 +#> 71 0.321048652 1.743009432 -0.49620029 +#> 72 -0.527149031 1.184322665 1.00890307 +#> 73 0.070016525 1.224225492 0.62085618 +#> 74 1.134493406 2.219461421 1.58638671 +#> 75 1.504749960 0.614156332 1.09643083 +#> 76 0.446530671 0.052052719 0.61180283 +#> 77 1.818863849 -0.302817368 0.04494236 +#> 78 -0.791160513 0.179908256 0.79171348 +#> 79 -0.300914823 -0.253138072 -0.53330713 +#> 80 0.926355041 1.873512137 0.35236855 +#> 81 0.144573957 1.043953709 -0.49422615 +#> 82 -0.027810676 0.325938318 -1.58919459 +#> 83 -2.100930284 1.066836105 -0.83668769 +#> 84 -0.444333571 -0.715766312 -0.85121894 +#> 85 -0.968875193 -1.687613536 -0.79915130 +#> 86 0.959440062 -1.088246737 1.55349882 +#> 87 -0.345251762 -1.686853560 0.65311349 +#> 88 0.572082195 -0.813048594 -1.24514160 +#> 89 -1.365521552 -1.373149134 -1.59569125 +#> 90 1.176097394 2.119269863 1.42040520 +#> 91 1.221033387 -0.577310879 -0.56619463 +#> 92 0.108998277 0.648942202 -0.11418245 +#> 93 0.101151686 0.111384203 0.73298987 +#> 94 0.586259342 1.453725946 1.13935521 +#> 95 0.153452555 1.348353614 -0.27134221 +#> 96 0.235523130 0.480340620 -0.05456858 +#> 97 0.785445485 1.876836130 -0.04612489 +#> 98 -0.198978331 -0.525346365 -0.12181418 +#> 99 0.629380511 -0.357590811 0.51006913 +#> 100 -2.276017019 -0.001639159 -1.01436632
    rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
    #> A B C +#> 1 0.98053619 1.397345080 1.24702308 +#> 2 0.28494917 -0.929721452 0.11231993 +#> 3 -0.48926432 -0.317158657 0.91387543 +#> 4 0.62746163 0.690257228 -0.04676857 +#> 5 0.52497027 -0.006770402 -0.54982277 +#> 6 -0.08113113 -1.203695112 -0.38591526 +#> 7 0.10950230 -0.448304595 -0.72749899 +#> 8 -0.98054763 -1.413085775 0.44500300 +#> 9 -0.22717869 0.588496717 1.47955055 +#> 10 -0.08167748 0.129810359 0.62297867 +#> 11 -1.06794579 1.622348860 2.17266535 +#> 12 1.23713517 1.254934760 0.21802500 +#> 13 2.34268548 -0.127564780 -1.84686336 +#> 14 -0.79906111 -0.670100344 -0.78681222 +#> 15 1.14762821 0.425896059 -0.19353105 +#> 16 -0.68792927 0.746387469 1.17342419 +#> 17 -0.42239464 -2.279769415 -1.45252677 +#> 18 -0.71983873 -0.782993158 -0.48339888 +#> 19 -0.27644209 -0.078365574 -0.15548499 +#> 20 0.65702277 -2.167247198 -2.28768427 +#> 21 1.36346504 2.807540375 0.61058213 +#> 22 2.26453757 -0.107450280 -1.03851429 +#> 23 1.07057295 1.036447960 0.77105891 +#> 24 -0.75535093 0.189251094 1.72491550 +#> 25 -0.68100214 -0.665149580 -0.04357655 +#> 26 -0.47017350 -0.314580812 -0.27934568 +#> 27 0.73476477 1.147051694 0.19216533 +#> 28 -0.09149070 0.876867711 -0.08339957 +#> 29 -2.05839553 -1.187120062 -0.84369069 +#> 30 -0.62779679 0.680592120 0.13823150 +#> 31 0.82896856 0.492073064 -0.10702775 +#> 32 1.12230254 0.029101542 -0.69550195 +#> 33 1.41109327 -0.547954608 -1.55760838 +#> 34 -1.28998785 -0.644308963 0.45818445 +#> 35 -0.74524292 0.423205419 2.23457494 +#> 36 0.18163205 0.475178614 0.81660170 +#> 37 -1.29214617 0.507173875 0.60586872 +#> 38 -0.52453660 -0.422831247 0.14219949 +#> 39 2.71082498 1.025301778 -0.77900169 +#> 40 0.67234061 -0.927892656 -0.94160161 +#> 41 -0.22932465 -0.452949466 -0.75393801 +#> 42 0.79662820 0.012013809 -0.66456985 +#> 43 -1.48598921 -1.485862845 -0.34075299 +#> 44 -2.73794234 -0.333565773 1.35801078 +#> 45 1.04730267 -0.239862856 -0.94911295 +#> 46 0.79570220 0.409234356 -0.02512850 +#> 47 -0.29797799 -0.960151155 0.32858669 +#> 48 1.32398457 0.086940658 -0.84502893 +#> 49 0.70645034 0.763352966 0.66553042 +#> 50 -1.19011584 -0.898273386 0.01265418 +#> 51 0.17313328 0.375553597 -0.01675357 +#> 52 1.04061188 -0.219130525 -0.77337525 +#> 53 -0.43883826 -0.042922039 1.27360295 +#> 54 0.67987637 -1.058120498 -0.27502560 +#> 55 -0.51468288 0.062507127 0.76999444 +#> 56 -0.80241295 -0.945210621 0.34889021 +#> 57 -0.47060134 -1.268959331 0.70686003 +#> 58 2.49959376 0.682354301 -1.46377249 +#> 59 -0.76711289 -0.641906992 0.26363487 +#> 60 0.84037060 -0.130252667 -0.61136080 +#> 61 -0.47759972 0.405378724 0.06637905 +#> 62 -1.35795459 0.303024258 1.33414293 +#> 63 -0.86120089 -1.445283701 0.98588190 +#> 64 0.65405904 -1.262137582 -1.45782179 +#> 65 -1.52487511 -1.185720620 -0.29528001 +#> 66 -0.60310623 0.543202595 0.16109147 +#> 67 -1.34640940 0.111464903 0.03001872 +#> 68 -1.20726090 -0.211677096 0.84891723 +#> 69 -0.07838011 0.191885581 0.98564804 +#> 70 1.13262307 0.785543693 -0.06059885 +#> 71 1.36490199 -0.252005899 -2.00968693 +#> 72 1.18092139 -0.345183489 -0.80804496 +#> 73 -1.50057211 -0.143484529 0.56351319 +#> 74 -0.67605220 -0.161655297 0.70612018 +#> 75 -0.39630979 -0.711680593 -0.01193046 +#> 76 -1.06729240 0.790110596 0.42953674 +#> 77 0.25135374 0.004217116 -1.46584325 +#> 78 -0.62606452 -0.500831364 0.35233621 +#> 79 0.22130814 -1.054521838 -0.13425086 +#> 80 -0.82249462 -0.106502250 1.12587401 +#> 81 1.13659660 0.694685870 -0.63335312 +#> 82 0.63912932 -0.346001998 -0.15632373 +#> 83 0.50735668 -0.121686372 -0.35290927 +#> 84 0.95787156 0.663581444 -0.85623386 +#> 85 -0.91227314 -1.085103286 0.84931769 +#> 86 -0.23904032 0.820166860 0.16160248 +#> 87 -0.71341295 -0.149361316 0.56590330 +#> 88 1.64368977 -1.175172449 -2.12371033 +#> 89 -0.35317370 0.697507425 -0.12301334 +#> 90 -1.24671366 -0.243868992 1.74687093 +#> 91 -0.64148189 0.722090091 1.47996273 +#> 92 0.22026898 1.174067582 1.19864762 +#> 93 -0.88325604 0.271478223 0.35463827 +#> 94 0.07313435 1.405022266 1.00772946 +#> 95 -1.47150701 0.560249257 1.03483886 +#> 96 -1.10044697 -1.187067944 0.66307881 +#> 97 -2.23696083 0.102519789 2.21376158 +#> 98 -0.92624102 -1.158465566 0.37674556 +#> 99 -1.39758231 0.256387690 1.30028559 +#> 100 -0.21576305 -0.512530549 -0.64474625
    - 
    select_num_grp(dat, grp_by = NULL, cols = NULL)
    + 
    select_num_grp(dat, between = c(), cols = NULL)

    Arguments

    @@ -139,7 +136,7 @@

    Arg

    - + diff --git a/docs/reference/sim_design.html b/docs/reference/sim_design.html index fbf40eaf..c06a8398 100644 --- a/docs/reference/sim_design.html +++ b/docs/reference/sim_design.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/sim_design_.html b/docs/reference/sim_design_.html index 5b6d3a0e..2493ec46 100644 --- a/docs/reference/sim_design_.html +++ b/docs/reference/sim_design_.html @@ -85,9 +85,6 @@ diff --git a/docs/reference/sim_df.html b/docs/reference/sim_df.html new file mode 100644 index 00000000..b5b6d4e2 --- /dev/null +++ b/docs/reference/sim_df.html @@ -0,0 +1,194 @@ + + + + + + + + +Simulate an existing dataframe — sim_df • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + +
    + +
    +
    +
    +

    Simulate an existing dataframe

    + + +
    + +
    + +

    sim_df Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).

    + +
    + + 
    sim_df(dat, n = 100, between = c(), within = c(),
+  empirical = FALSE)
    + +

    Arguments

    +

    the existing dataframe
    grp_bybetween

    an optional list of column names to group by
    + + + + + + + + + + + + + + + + + + + + + +
    dat

    the existing dataframe

    n

    the number of samples to return per group

    between

    a list of the between-subject factors

    within

    a list of the within-subject factors

    empirical

    logical. Passed on to rnorm_multi

    + +

    Value

    + +

    tibble

    + + +

    Examples

    + 
    iris100 <- sim_df(iris, 100)
+iris_species <- sim_df(iris, 100, "Species")
    + + + + +
    +
    +

    Developed by Lisa DeBruine.

    +
    + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    + + + + + + + diff --git a/docs/reference/simdf_mixed.html b/docs/reference/simdf_mixed.html index 1515559f..2dfb5c19 100644 --- a/docs/reference/simdf_mixed.html +++ b/docs/reference/simdf_mixed.html @@ -85,9 +85,6 @@ @@ -167,107 +164,107 @@

    Value

    Examples

    - 
    simdf_mixed(faceratings, 10, 10, "rating", "rater_id", "face_id")
    #>     sub_id item_id      sub_i      item_i         dv
-#> 1        1       1  0.7171917 -0.16440417  2.7076926
-#> 2        2       1  1.5872296 -0.16440417  4.0497225
-#> 3        3       1 -0.7289360 -0.16440417  2.8124829
-#> 4        4       1  0.6003483 -0.16440417  1.6454961
-#> 5        5       1  0.5163209 -0.16440417  2.8399589
-#> 6        6       1  0.2563833 -0.16440417  3.0883100
-#> 7        7       1 -0.7781445 -0.16440417  3.0922434
-#> 8        8       1  1.1424562 -0.16440417  3.7438529
-#> 9        9       1  0.1711736 -0.16440417  1.9164980
-#> 10      10       1  1.0828005 -0.16440417  2.1094020
-#> 11       1       2  0.7171917  0.37636537  6.5942167
-#> 12       2       2  1.5872296  0.37636537  3.8486785
-#> 13       3       2 -0.7289360  0.37636537  3.4411992
-#> 14       4       2  0.6003483  0.37636537  5.3235771
-#> 15       5       2  0.5163209  0.37636537  4.2771207
-#> 16       6       2  0.2563833  0.37636537  3.3479907
-#> 17       7       2 -0.7781445  0.37636537  2.7002602
-#> 18       8       2  1.1424562  0.37636537  4.1520841
-#> 19       9       2  0.1711736  0.37636537  5.1249236
-#> 20      10       2  1.0828005  0.37636537  4.4809776
-#> 21       1       3  0.7171917 -0.82422093  3.2861012
-#> 22       2       3  1.5872296 -0.82422093  2.8786693
-#> 23       3       3 -0.7289360 -0.82422093  1.9902052
-#> 24       4       3  0.6003483 -0.82422093  2.0458923
-#> 25       5       3  0.5163209 -0.82422093  1.5216236
-#> 26       6       3  0.2563833 -0.82422093  3.3659935
-#> 27       7       3 -0.7781445 -0.82422093  2.9983831
-#> 28       8       3  1.1424562 -0.82422093  3.4864340
-#> 29       9       3  0.1711736 -0.82422093  2.7199936
-#> 30      10       3  1.0828005 -0.82422093  4.6086848
-#> 31       1       4  0.7171917 -0.91128136  1.6789966
-#> 32       2       4  1.5872296 -0.91128136  5.6968101
-#> 33       3       4 -0.7289360 -0.91128136 -1.0487718
-#> 34       4       4  0.6003483 -0.91128136  2.0597989
-#> 35       5       4  0.5163209 -0.91128136  2.3732315
-#> 36       6       4  0.2563833 -0.91128136  4.2745678
-#> 37       7       4 -0.7781445 -0.91128136  1.8469632
-#> 38       8       4  1.1424562 -0.91128136  4.1014591
-#> 39       9       4  0.1711736 -0.91128136  1.0318854
-#> 40      10       4  1.0828005 -0.91128136  4.2240532
-#> 41       1       5  0.7171917  1.66678147  5.0273318
-#> 42       2       5  1.5872296  1.66678147  3.5821303
-#> 43       3       5 -0.7289360  1.66678147  5.4538830
-#> 44       4       5  0.6003483  1.66678147  7.9807309
-#> 45       5       5  0.5163209  1.66678147  3.7473337
-#> 46       6       5  0.2563833  1.66678147  7.1439931
-#> 47       7       5 -0.7781445  1.66678147  4.8426279
-#> 48       8       5  1.1424562  1.66678147  6.6205912
-#> 49       9       5  0.1711736  1.66678147  6.7093920
-#> 50      10       5  1.0828005  1.66678147  4.3002833
-#> 51       1       6  0.7171917 -0.32253158  3.5070761
-#> 52       2       6  1.5872296 -0.32253158  4.5057448
-#> 53       3       6 -0.7289360 -0.32253158  0.9024044
-#> 54       4       6  0.6003483 -0.32253158  3.9435972
-#> 55       5       6  0.5163209 -0.32253158  4.3432880
-#> 56       6       6  0.2563833 -0.32253158  1.5616119
-#> 57       7       6 -0.7781445 -0.32253158  1.5701492
-#> 58       8       6  1.1424562 -0.32253158  1.8015241
-#> 59       9       6  0.1711736 -0.32253158  2.9668905
-#> 60      10       6  1.0828005 -0.32253158  4.5541003
-#> 61       1       7  0.7171917  0.06766548  3.6839723
-#> 62       2       7  1.5872296  0.06766548  3.5298722
-#> 63       3       7 -0.7289360  0.06766548  1.3879482
-#> 64       4       7  0.6003483  0.06766548  3.0134067
-#> 65       5       7  0.5163209  0.06766548  3.8805034
-#> 66       6       7  0.2563833  0.06766548  2.8650564
-#> 67       7       7 -0.7781445  0.06766548  3.9055132
-#> 68       8       7  1.1424562  0.06766548  5.1346467
-#> 69       9       7  0.1711736  0.06766548  2.2637057
-#> 70      10       7  1.0828005  0.06766548  3.8728360
-#> 71       1       8  0.7171917 -0.41971766  4.1334290
-#> 72       2       8  1.5872296 -0.41971766  4.1028115
-#> 73       3       8 -0.7289360 -0.41971766  2.0302724
-#> 74       4       8  0.6003483 -0.41971766  2.8500859
-#> 75       5       8  0.5163209 -0.41971766  1.2627937
-#> 76       6       8  0.2563833 -0.41971766  2.0276961
-#> 77       7       8 -0.7781445 -0.41971766  0.9953581
-#> 78       8       8  1.1424562 -0.41971766  4.0599506
-#> 79       9       8  0.1711736 -0.41971766  3.1317760
-#> 80      10       8  1.0828005 -0.41971766  3.4296845
-#> 81       1       9  0.7171917 -0.09253167  2.7561118
-#> 82       2       9  1.5872296 -0.09253167  4.7486546
-#> 83       3       9 -0.7289360 -0.09253167  0.9585535
-#> 84       4       9  0.6003483 -0.09253167  3.6791317
-#> 85       5       9  0.5163209 -0.09253167  2.7224103
-#> 86       6       9  0.2563833 -0.09253167  2.6014336
-#> 87       7       9 -0.7781445 -0.09253167  0.6113194
-#> 88       8       9  1.1424562 -0.09253167  5.3929322
-#> 89       9       9  0.1711736 -0.09253167  3.5604510
-#> 90      10       9  1.0828005 -0.09253167  3.8776255
-#> 91       1      10  0.7171917  1.38999715  5.8347359
-#> 92       2      10  1.5872296  1.38999715  6.5293892
-#> 93       3      10 -0.7289360  1.38999715  4.4470914
-#> 94       4      10  0.6003483  1.38999715  5.8856564
-#> 95       5      10  0.5163209  1.38999715  4.7294257
-#> 96       6      10  0.2563833  1.38999715  5.8323700
-#> 97       7      10 -0.7781445  1.38999715  3.5409873
-#> 98       8      10  1.1424562  1.38999715  6.7181566
-#> 99       9      10  0.1711736  1.38999715  4.5778140
-#> 100     10      10  1.0828005  1.38999715  4.5562784
    + 
    simdf_mixed(faceratings, 10, 10, "rating", "rater_id", "face_id")
    #>     sub_id item_id       sub_i     item_i        dv
+#> 1        1       1  0.15336871  0.1152507 3.4263347
+#> 2        2       1 -0.07546968  0.1152507 2.6552210
+#> 3        3       1 -0.09464565  0.1152507 2.5473771
+#> 4        4       1  0.13680669  0.1152507 4.5736075
+#> 5        5       1  0.46710613  0.1152507 4.6470300
+#> 6        6       1  0.71713231  0.1152507 5.0470723
+#> 7        7       1 -0.16377959  0.1152507 3.0286129
+#> 8        8       1 -0.03648029  0.1152507 3.0203508
+#> 9        9       1 -0.44490238  0.1152507 2.4007496
+#> 10      10       1 -0.50294582  0.1152507 1.4482325
+#> 11       1       2  0.15336871  0.9694343 4.2646276
+#> 12       2       2 -0.07546968  0.9694343 3.5117228
+#> 13       3       2 -0.09464565  0.9694343 1.9205807
+#> 14       4       2  0.13680669  0.9694343 3.3332175
+#> 15       5       2  0.46710613  0.9694343 4.1037056
+#> 16       6       2  0.71713231  0.9694343 4.8340290
+#> 17       7       2 -0.16377959  0.9694343 4.3931885
+#> 18       8       2 -0.03648029  0.9694343 3.5224828
+#> 19       9       2 -0.44490238  0.9694343 2.7403387
+#> 20      10       2 -0.50294582  0.9694343 4.9209943
+#> 21       1       3  0.15336871 -0.1809222 2.3955324
+#> 22       2       3 -0.07546968 -0.1809222 5.0649247
+#> 23       3       3 -0.09464565 -0.1809222 3.0032075
+#> 24       4       3  0.13680669 -0.1809222 2.4135067
+#> 25       5       3  0.46710613 -0.1809222 4.5656069
+#> 26       6       3  0.71713231 -0.1809222 2.8650467
+#> 27       7       3 -0.16377959 -0.1809222 3.6126806
+#> 28       8       3 -0.03648029 -0.1809222 0.9722492
+#> 29       9       3 -0.44490238 -0.1809222 0.6125408
+#> 30      10       3 -0.50294582 -0.1809222 3.8909402
+#> 31       1       4  0.15336871  1.0979794 5.9448229
+#> 32       2       4 -0.07546968  1.0979794 3.9574235
+#> 33       3       4 -0.09464565  1.0979794 4.8079004
+#> 34       4       4  0.13680669  1.0979794 5.0826263
+#> 35       5       4  0.46710613  1.0979794 3.3817108
+#> 36       6       4  0.71713231  1.0979794 3.2445244
+#> 37       7       4 -0.16377959  1.0979794 4.2746244
+#> 38       8       4 -0.03648029  1.0979794 4.7967650
+#> 39       9       4 -0.44490238  1.0979794 4.3737837
+#> 40      10       4 -0.50294582  1.0979794 4.2923638
+#> 41       1       5  0.15336871  0.2255086 5.9170173
+#> 42       2       5 -0.07546968  0.2255086 2.7538325
+#> 43       3       5 -0.09464565  0.2255086 3.1842397
+#> 44       4       5  0.13680669  0.2255086 4.2260520
+#> 45       5       5  0.46710613  0.2255086 1.5543152
+#> 46       6       5  0.71713231  0.2255086 3.9556215
+#> 47       7       5 -0.16377959  0.2255086 3.8447343
+#> 48       8       5 -0.03648029  0.2255086 4.5385200
+#> 49       9       5 -0.44490238  0.2255086 2.6456965
+#> 50      10       5 -0.50294582  0.2255086 2.2025837
+#> 51       1       6  0.15336871 -0.5688417 3.5177725
+#> 52       2       6 -0.07546968 -0.5688417 2.9838297
+#> 53       3       6 -0.09464565 -0.5688417 3.5923231
+#> 54       4       6  0.13680669 -0.5688417 3.1022826
+#> 55       5       6  0.46710613 -0.5688417 2.7535176
+#> 56       6       6  0.71713231 -0.5688417 5.4940446
+#> 57       7       6 -0.16377959 -0.5688417 2.5234260
+#> 58       8       6 -0.03648029 -0.5688417 0.3511046
+#> 59       9       6 -0.44490238 -0.5688417 1.8498874
+#> 60      10       6 -0.50294582 -0.5688417 3.5379786
+#> 61       1       7  0.15336871 -0.1003512 4.2409599
+#> 62       2       7 -0.07546968 -0.1003512 3.5556705
+#> 63       3       7 -0.09464565 -0.1003512 2.6899611
+#> 64       4       7  0.13680669 -0.1003512 2.0439883
+#> 65       5       7  0.46710613 -0.1003512 5.0354057
+#> 66       6       7  0.71713231 -0.1003512 3.5521496
+#> 67       7       7 -0.16377959 -0.1003512 2.3525066
+#> 68       8       7 -0.03648029 -0.1003512 3.5415480
+#> 69       9       7 -0.44490238 -0.1003512 4.1344729
+#> 70      10       7 -0.50294582 -0.1003512 2.3269392
+#> 71       1       8  0.15336871  1.6989795 6.4250556
+#> 72       2       8 -0.07546968  1.6989795 5.7450023
+#> 73       3       8 -0.09464565  1.6989795 3.8872425
+#> 74       4       8  0.13680669  1.6989795 4.5973502
+#> 75       5       8  0.46710613  1.6989795 5.7349937
+#> 76       6       8  0.71713231  1.6989795 2.6852695
+#> 77       7       8 -0.16377959  1.6989795 3.7919020
+#> 78       8       8 -0.03648029  1.6989795 4.7810577
+#> 79       9       8 -0.44490238  1.6989795 5.1999969
+#> 80      10       8 -0.50294582  1.6989795 5.4375538
+#> 81       1       9  0.15336871  1.2452224 5.3180720
+#> 82       2       9 -0.07546968  1.2452224 2.7003936
+#> 83       3       9 -0.09464565  1.2452224 4.3759073
+#> 84       4       9  0.13680669  1.2452224 6.5338902
+#> 85       5       9  0.46710613  1.2452224 3.8544295
+#> 86       6       9  0.71713231  1.2452224 5.2503336
+#> 87       7       9 -0.16377959  1.2452224 4.6527757
+#> 88       8       9 -0.03648029  1.2452224 3.6883031
+#> 89       9       9 -0.44490238  1.2452224 2.8378979
+#> 90      10       9 -0.50294582  1.2452224 4.8626384
+#> 91       1      10  0.15336871 -0.2064970 4.7460439
+#> 92       2      10 -0.07546968 -0.2064970 1.8389187
+#> 93       3      10 -0.09464565 -0.2064970 1.3021962
+#> 94       4      10  0.13680669 -0.2064970 2.4226626
+#> 95       5      10  0.46710613 -0.2064970 3.0711292
+#> 96       6      10  0.71713231 -0.2064970 2.2198113
+#> 97       7      10 -0.16377959 -0.2064970 0.9575364
+#> 98       8      10 -0.03648029 -0.2064970 2.6135275
+#> 99       9      10 -0.44490238 -0.2064970 1.2924662
+#> 100     10      10 -0.50294582 -0.2064970 0.8947152
    diff --git a/docs/articles/index.html b/docs/articles/index.html index ac00776b..edd0368b 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -60,7 +60,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/articles/rnorm_multi.html b/docs/articles/rnorm_multi.html index 4aa6a9b6..122f09b9 100644 --- a/docs/articles/rnorm_multi.html +++ b/docs/articles/rnorm_multi.html @@ -30,7 +30,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -82,7 +82,7 @@

    Simulate Correlated Variables

    Lisa DeBruine

    -

    2019-05-01

    +

    2019-05-02

    @@ -95,12 +95,12 @@

    2019-05-01

    Quick example

    -

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

    +

    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, 
-                  cors = c(0.5, 0.5, 0.25), 
                   mu = c(0, 20, 20),
                   sd = c(1, 5, 5),
+                  r = c(0.5, 0.5, 0.25), 
                   varnames = c("A", "B", "C"),
                   empirical = FALSE)
    @@ -144,10 +144,9 @@

    -
    +

    -Specify cors -

    +Specify correlations

    You can specify the correlations in one of four ways:

    • A single r for all pairs
      • @@ -159,7 +158,7 @@

      One Number

      If you want all the pairs to have the same correlation, just specify a single number.

      -
      bvn <- rnorm_multi(100, 5, .3, varnames = letters[1:5])
      +
      bvn <- rnorm_multi(100, 5, 0, 1, .3, varnames = letters[1:5])
      @@ -237,7 +236,7 @@

      Matrix

      If you already have a correlation matrix, such as the output of cor(), you can specify the simulated data with that.

      cmat <- cor(iris[,1:4])
-bvn <- rnorm_multi(100, 4, cmat, 
+bvn <- rnorm_multi(100, 4, 0, 1, cmat, 
                   varnames = colnames(cmat))
      Sample stats from a single rho
      @@ -302,7 +301,7 @@ 

      cmat <- c(1, .3, .5,
           .3, 1, 0,
           .5, 0, 1)
-bvn <- rnorm_multi(100, 3, cmat, 
+bvn <- rnorm_multi(100, 3, 0, 1, cmat, 
                   varnames = c("first", "second", "third"))

      Sample stats from a correlation matrix
      @@ -357,7 +356,7 @@

      rho2_4 <- 0 rho3_4 <- -.3 cmat <- c(rho1_2, rho1_3, rho1_4, rho2_3, rho2_4, rho3_4) -bvn <- rnorm_multi(100, 4, cmat, +bvn <- rnorm_multi(100, 4, 0, 1, cmat, varnames = letters[1:4])

      Sample stats from a vars*vars vector
      @@ -420,7 +419,7 @@

      empirical

      If you want your samples to have the exact correlations, means, and SDs you entered, set empirical to TRUE.

      -
      bvn <- rnorm_multi(100, 5, .3, 
+
      bvn <- rnorm_multi(100, 5, 0, 1, .3, 
                   varnames = letters[1:5], 
                   empirical = T)
      Sample stats from a (vars*(vars-1)/2) vector
      @@ -495,6 +494,43 @@

    +
    +
    +

    +Pre-existing variable

    +

    Us rnorm_pre() to create a vector with a specified correlation to a pre-existing variable. The following code creates a vector called sl.5 with a mean of 10, SD of 2 and a correaltion of r = 0.5 to the Sepal.Length column in the built-in dataset iris. This function only creates a vector with the exact parameters specified.

    +
    sl <- iris$Sepal.Length
+
+sl.5.v1 <- rnorm_pre(sl, mu = 10, sd = 2, r = 0.5)
+sl.5.v2 <- rnorm_pre(sl, mu = 10, sd = 2, r = 0.5)
    + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    slsl.5.v1sl.5.v2
    sl1.00.50000000.5000000
    sl.5.v10.51.00000000.1765035
    sl.5.v20.50.17650351.0000000
    @@ -504,6 +540,7 @@

    Contents

    diff --git a/docs/articles/sim_design.html b/docs/articles/sim_design.html index 091be14d..5e40bb1a 100644 --- a/docs/articles/sim_design.html +++ b/docs/articles/sim_design.html @@ -30,7 +30,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -82,7 +82,7 @@

    Simulate by Design

    Lisa DeBruine

    -

    2019-05-01

    +

    2019-05-02

    @@ -103,7 +103,7 @@

    dog = c(15, 25), row.names = within$time ) -df <- sim_design(within, between, n = 100, cors = 0.5, mu = mu, sd = 5) +df <- sim_design(within, between, n = 100, mu = mu, sd = 5, r = 0.5, empirical = TRUE) @@ -119,37 +119,37 @@

    - - - - + + + + - - - - + + + + - - - - + + + + - - - - + + + +
    petcat 100 day1.000.5610.234.121.00.5105
    cat 100 night0.561.0020.024.880.51.0205
    dog 100 day1.000.5515.275.001.00.5155
    dog 100 night0.551.0025.315.360.51.0255
    @@ -161,23 +161,8 @@

    Factor and level names

    -

    First, list your between-subject and within-subject factors. You can specify them like this:

    -
    between <- list(
-  "pet" = c("cat", "dog")
-)
-within <- list(
-  "time" = c("day", "night")
-)
    -

    Or like this:

    -
    between <- list(
-  pet = c(cat = "Is a cat person", dog = "Is a dog person")
-)
-within <- list(
-  time = c(day = "Tested during the day", night = "Tested at night")
-)
    -

    The long name for factor levels isn’t used yet, but will be used in future functions that create a codebook or plots.

    -

    If you don’t feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w*2w*2b withthe following code.

    -
    df <- sim_design(within = c(2,2), between = c(2), n = 5)
    +

    If you don’t feel like naming your factors and levels, you can just put in a vector of levels. So you can make a quick 2w*3w*2b with the following code.

    +
    df <- sim_design(within = c(2,3), between = c(2), n = 5, mu = 0, sd = 1, r = 0.5)
    @@ -186,95 +171,132 @@

    + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + + - - - - + + + + + +
    sub_idA2_B1 A1_B2 A2_B2A1_B3A2_B3
    S01 C10.1561683-0.3062499-2.91899381.4686767-2.7038494-2.0031140-1.24636190.2395364-0.3551418-0.6613917
    S02 C10.29577200.0918673-1.31257911.2485948-0.2574224-2.5189879-0.0945284-1.0963883-0.9231602-0.8312929
    S03 C10.11969822.1077554-0.7807512-1.38472270.93269600.57413980.94616071.51748110.13368172.2414371
    S04 C1-0.3682321-1.1357070-0.21892450.0516151-0.2846845-0.0003843-0.42058920.14290450.7309652-0.4047417
    S05 C1-2.7198911-1.04837260.80626740.01706790.02619540.5607634-1.2736072-1.01079321.33379950.2854269
    S06 C20.79303221.7506883-1.4776848-1.0363013-0.9116783-1.0939985-1.0655517-0.9173210-1.4847558-2.5493564
    S07 C2-0.3230935-0.47198801.03683592.1271924-0.06900600.47418050.3974194-0.18882210.09631501.4528341
    S08 C2-0.0863936-0.3977303-0.56213650.2723021-0.45911010.8723573-0.2708937-0.62128911.34803800.9535270
    S09 C21.61519191.0392466-0.6035584-0.31095350.1287775-0.3451130-0.5767718-1.8759247-0.8973256-1.1960685
    S10 C20.9005048-0.8744312-0.5389020-0.44654841.06233201.0175079-0.16751000.56205790.75133210.7814270
    +

    You can specify between-subject and within-subject factors as a list of vectors where the item names are the factor labels and the vectors are the level labels.

    +
    between <- list(
+  "pet" = c("cat", "dog")
+)
+within <- list(
+  "time" = c("day", "night")
+)
    +

    You can also specify factors as a list of named vectors where the item names are the factor labels, the vector names are the level labels, and the vector items are the long labels you would use in a codebook or plot.

    +
    between <- list(
+  pet = c(cat = "Is a cat person", dog = "Is a dog person")
+)
+within <- list(
+  time = c(day = "Tested during the day", night = "Tested at night")
+)
    +

    The long label for factor levels isn’t used yet, but will be used in future functions that create a codebook or plots.

    N, mean, SD

    -

    You can specify the Ns, means and standard deviations for each cell as a single number, vector, list, or data frame.

    +

    You can specify the Ns, means and standard deviations for each cell as a single number, list of vectors, or data frame.

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

    n <- 100 # n per cell, not total

    The levels of the between-subject factors are the list names and the levels of the within-subject factors are the vector names.

    @@ -312,13 +334,13 @@

    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.

    -
    cors <- list(
+
    r <- list(
   cat = .5,
   dog = .6
 )
    
 
    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).
    
 
    df <- sim_design(within, between, n = 100, 
-                 cors = cors, mu = mu, sd = sd,
+                 mu = mu, sd = sd, r = r,
                  empirical = TRUE)
    
 
@@ -395,31 +417,33 @@ 
    
   expert_night = c(10, 15, 12, 17, 14, 19)
 )
 
    You can set the correlation for each between-cell to a single number.
    
-
    cors <- list(
+
    r <- list(
   novice_day = 0.3,
   novice_night = 0.2,
   expert_day = 0.5,
   expert_night = 0.4
 )
    
-
    Or you can set the full correlation matrix with a vector or matrix (see section Specify cors). Since we have 6 within-cells, this is a 6x6 matrix or a vector of the upper right 15 values.
    
+
    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
-r <-  c(0.5, 0.2, 0.2, 0.2, 0.2, #con_easy
-             0.2, 0.2, 0.2, 0.2, #inc_easy
-                  0.5, 0.2, 0.2, #con_med
-                       0.2, 0.2, #inc_med
-                            0.5) #con_hard
-                                 #inc_hard
+#          ce,  ie,  cm,  im,  ch,  ih
+triangle <-  c(0.5, 0.2, 0.2, 0.2, 0.2, #con_easy
+                    0.2, 0.2, 0.2, 0.2, #inc_easy
+                         0.5, 0.2, 0.2, #con_med
+                              0.2, 0.2, #inc_med
+                                   0.5) #con_hard
+                                        #inc_hard
 
-cors <- list(
-  novice_day = r,
-  novice_night = r,
-  expert_day = r,
-  expert_night = r
+r <- list(
+  novice_day = triangle,
+  novice_night = triangle,
+  expert_day = triangle,
+  expert_night = triangle
 )
    
 
    You can set frame_long = TRUE to return the data frame in long format, which is usually easier for plotting.
    
-
    df <- sim_design(within, between, n = 100, cors = cors, mu = mu, sd = 2, frame_long = TRUE)
    
+
    df <- sim_design(within, between, n = 100, 
+                 mu = mu, sd = 2, r = r, 
+                 frame_long = TRUE)
    
 
    
 
    
   
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X@TYdGPaHppf
       
         faux
-        0.0.0.9005
+        0.0.0.9006
       
     
 
@@ -82,7 +82,7 @@
       
    Simulate from Existing Data
    
                         
    Lisa DeBruine
    
             
-            
    2019-05-01
    
+            
    2019-05-02
    
       
       
       
@@ -112,10 +112,10 @@ 
    2019-05-01
    
 Simulated cars dataset
 
    
 
-
    
-
    
-Grouping Variables
    
-
    You can also optionally add grouping variables. For example, here is the relationship between sepal length and width in the built-in dataset iris.
    
+
    
+
    
+Between-subject variables
    
+
    You can also optionally add between-subject variables. For example, here is the relationship between sepal length and width in the built-in dataset iris.
    
 
    iris %>%
   ggplot(aes(Sepal.Width, Sepal.Length, color = Species)) +
   geom_point() +
@@ -135,6 +135,16 @@ 
    
 Simulated iris dataset
 
    
 
    
+
    
+
    
+
    
+Empirical
    
+
    Set empirical = TRUE to return a data frame with exactly the same means, SDs, and correlations as the original dataset.
    
+
    exact_iris <- sim_df(iris, 50, between = "Species", empirical = TRUE)
    
+
    
+
    
+
    
+Rounding
    
 
    For now, the function only creates new variables sampled from a continuous normal distribution. I hope to add in other sampling distributions in the future. So you’d need to do any rounding or truncating yourself.
    
 
    sim_df(iris, 50, "Species") %>%
   mutate_if(is.numeric, round, 1) %>%
@@ -150,6 +160,15 @@ 
    
   
    
 
   
 
 
    
diff --git a/docs/articles/sim_df_files/figure-html/plot-iris-sim-round-1.png b/docs/articles/sim_df_files/figure-html/plot-iris-sim-round-1.png
index 8a058bd4738d9c131c4ae63e9585f1d445ac0f60..09ea4737d4444f52db1331d7cb627a0f84ef3a07 100644
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diff --git a/docs/authors.html b/docs/authors.html
index 3790b6b1..1c314be5 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -60,7 +60,7 @@
       
       
         faux
-        0.0.0.9005
+        0.0.0.9006
       
     
    
 
@@ -117,7 +117,7 @@ 
    Authors
    
 
     
      
       
    • 
-        
      Lisa DeBruine. Author, maintainer. 
+        
      Lisa DeBruine. Author, maintainer. ORCID
         
      
       
      • 
     
    
diff --git a/docs/index.html b/docs/index.html
index a33f5500..0e40054e 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -30,7 +30,7 @@
       
       
         faux
-        0.0.0.9005
+        0.0.0.9006
       
     
 
@@ -99,7 +99,7 @@ 
    
   row.names = within$time
 )
 df <- sim_design(within, between, 
-                 n = 100, cors = 0.5, mu = mu, sd = 5,
+                 n = 100, mu = mu, sd = 5, r = .5,
                  empirical = TRUE)
 
    
 
    
@@ -155,14 +155,14 @@ 
    
 
    
 
    
 rnorm_multi
    
-
    This function makes multiple normally distributed vectors with specified parameters and relationships.see vignette
    
-
    For example, the following creates a sample that has 100 observations of 3 variables, drawn from a population where where A correlates with B and C with r = 0.5, and B and C correlate with r = 0.25. A has a mean of 0 and SD of 1, while B and C have means of 20 and SDs of 5.
    
+
    This function makes multiple normally distributed vectors with specified parameters and relationships. see vignette
    
+
    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, 
-  cors = c(0.5, 0.5, 0.25), 
   mu = c(0, 20, 20),
   sd = c(1, 5, 5),
+  r = c(0.5, 0.5, 0.25), 
   varnames = c("A", "B", "C"),
   empirical = FALSE
 )
    
@@ -461,7 +461,7 @@ 
    License
    
 
    
 
    Developers
    
 
      
-
    • Lisa DeBruine 
       Author, maintainer   
      • 
+
    • Lisa DeBruine 
       Author, maintainer  ORCID 
      • 
 
    
 
    
 
diff --git a/docs/news/index.html b/docs/news/index.html
index f4aecd31..ff5eeb2d 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -60,7 +60,7 @@
       
       
         faux
-        0.0.0.9005
+        0.0.0.9006
       
     
    
 
diff --git a/docs/reference/check_design.html b/docs/reference/check_design.html
index 97b90867..801e75e0 100644
--- a/docs/reference/check_design.html
+++ b/docs/reference/check_design.html
@@ -63,7 +63,7 @@
       
       
         faux
-        0.0.0.9005
+        0.0.0.9006
       
     
 
@@ -126,8 +126,8 @@ 
    Validate design
    
     
     
 
-    
    check_design(within = list(), between = list(), n = 100, cors = 0,
-  mu = 0, sd = 1)
    
+    
    check_design(within = list(), between = list(), n = 100, mu = 0,
+  sd = 1, r = 0)
    
     
     
    Arguments
    
     
    
 Sample sim_design() stats
    
@@ -144,10 +144,6 @@ 
    Arg
       
    
-    
-      
-      
-    
@@ -156,6 +152,10 @@ 
    Arg
       
    
+    
+      
+      
+    
    n
       
    the number of samples required
    		
     
    cors
    the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
    
     
    
       mu
       
    a vector giving the means of the variables (numeric vector of length 1 or vars)
    		sd
       
    the standard deviations of the variables (numeric vector of length 1 or vars)
    		
     
    r
    the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)
    
     
    
     
     
    Value
    
diff --git a/docs/reference/check_sim_stats.html b/docs/reference/check_sim_stats.html
index 4f597133..8f5c2015 100644
--- a/docs/reference/check_sim_stats.html
+++ b/docs/reference/check_sim_stats.html
@@ -63,7 +63,7 @@
       
       
         faux
-        0.0.0.9005
+        0.0.0.9006
    diff --git a/docs/reference/convert_param.html b/docs/reference/convert_param.html index b4fc1268..ee54cc32 100644 --- a/docs/reference/convert_param.html +++ b/docs/reference/convert_param.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006
    @@ -126,7 +126,7 @@

    Convert parameter

    - 
    convert_param(param, cells_b, cells_w, type = "this parameter")
    + 
    convert_param(param, cells_w, cells_b, type = "this parameter")

    Arguments

    @@ -135,14 +135,14 @@

    Arg

    - - - - + + + + diff --git a/docs/reference/cormat.html b/docs/reference/cormat.html index cf441b57..6abdb450 100644 --- a/docs/reference/cormat.html +++ b/docs/reference/cormat.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/cormat_from_triangle.html b/docs/reference/cormat_from_triangle.html index 5f14ae71..d7d2db61 100644 --- a/docs/reference/cormat_from_triangle.html +++ b/docs/reference/cormat_from_triangle.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/faceratings.html b/docs/reference/faceratings.html index 600c9ea5..8ed382bd 100644 --- a/docs/reference/faceratings.html +++ b/docs/reference/faceratings.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/fix_name_labels.html b/docs/reference/fix_name_labels.html index 2c229b5b..a6041bfe 100644 --- a/docs/reference/fix_name_labels.html +++ b/docs/reference/fix_name_labels.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/index.html b/docs/reference/index.html index 8f003b04..85381922 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -60,7 +60,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -144,12 +144,6 @@

    convert_param()

    - -

    - - @@ -177,8 +171,7 @@

    long2wide()

    -

    + + + + +
    param

    the parameter (mu, sd, or n)
    cells_b

    a list of between-subject cell combinations

    cells_w

    a list of within-subject cells combinations
    cells_b

    a list of between-subject cell combinations

    type

    the name of the parameter (for error messages)

    Convert parameter

    cormat()

    Long to wide format -Converts a table from long to wide format

    Long to wide format
    @@ -224,9 +217,15 @@

    simdf_mixed()

    +

    sim_mixed_df()

    Generate a sample with random intercepts for subjects and items

    +

    wide2long()

    +

    Wide to long format
    diff --git a/docs/reference/is_pos_def.html b/docs/reference/is_pos_def.html index e7c50532..cc7e1e4c 100644 --- a/docs/reference/is_pos_def.html +++ b/docs/reference/is_pos_def.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/long2wide.html b/docs/reference/long2wide.html index f6a1ab50..b5ec7c90 100644 --- a/docs/reference/long2wide.html +++ b/docs/reference/long2wide.html @@ -6,8 +6,7 @@ -Long to wide format -Converts a table from long to wide format — long2wide • faux +Long to wide format — long2wide • faux @@ -31,11 +30,9 @@ - + - + @@ -66,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -118,55 +115,49 @@
    -

    Long to wide format -Converts a table from long to wide format

    +

    Long to wide format

    -

    Long to wide format -Converts a table from long to wide format

    +

    Converts data from long format to wide

    - 
    long2wide(dat, between = c(), within = c(), dv = c(), id = c())
    + 
    long2wide(dat, within = c(), between = c(), dv = c(), id = c())

    Arguments

    - + - - + + - - + + - + - +
    dat

    the existing dataframe in long format

    the long data frame to convert
    between

    a vector of column names for between-subject factors

    		within

    the names of the within column(s)
    within

    a vector of column names for within-subject factors

    		between

    the names of between column(s) (optional)
    dv

    the column name of the dv

    the name of the DV (value) column
    id

    the column name(s) of the subject ID

    the names of the column(s) for grouping observations

    Value

    -

    tibble in wide format

    +

    the data frame in wide format

    -

    Examples

    - 
    
-df_long <- sim_design(2,2, frame_long = TRUE)
-df_wide <- long2wide(df_long, "B", "A", "val", "sub_id")
    diff --git a/docs/reference/make_id.html b/docs/reference/make_id.html index d101cb3c..f1bf100d 100644 --- a/docs/reference/make_id.html +++ b/docs/reference/make_id.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 27b4a213..3fa0d792 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/pos_def_limits.html b/docs/reference/pos_def_limits.html index f89a849d..628cd5a6 100644 --- a/docs/reference/pos_def_limits.html +++ b/docs/reference/pos_def_limits.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 diff --git a/docs/reference/rnorm_multi.html b/docs/reference/rnorm_multi.html index 2c2b8f00..d09fc387 100644 --- a/docs/reference/rnorm_multi.html +++ b/docs/reference/rnorm_multi.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -126,8 +126,8 @@

    Multiple Normally Distributed Vectors

    - 
    rnorm_multi(n, vars = 3, cors = 0, mu = 0, sd = 1,
-  varnames = NULL, empirical = FALSE, as.matrix = FALSE)
    + 
    rnorm_multi(n, vars = 3, mu = 0, sd = 1, r = 0, varnames = NULL,
+  empirical = FALSE, as.matrix = FALSE, cors = NULL)

    Arguments

    @@ -140,10 +140,6 @@

    Arg

    - - - - @@ -152,18 +148,26 @@

    Arg

    + + + + - + - + + + + +
    vars

    the number of variables to return
    cors

    the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)

    mu

    a vector giving the means of the variables (numeric vector of length 1 or vars)

    		sd

    the standard deviations of the variables (numeric vector of length 1 or vars)
    r

    the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)

    varnames

    optional names for the variables (string vector of length vars) defaults if cors is a matrix with column names

    optional names for the variables (string vector of length vars) defaults if r is a matrix with column names
    empirical

    logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance

    logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
    as.matrix

    logical. If true, returns a matrix
    cors

    (deprecated; use r)

    Value

    @@ -172,207 +176,107 @@

    Value

    Examples

    - 
    rnorm_multi(100, 3, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
    #>                A            B           C
-#> 1   -1.711924939 -1.011599824 -1.32018668
-#> 2    0.887134792  0.043794457  0.99462051
-#> 3    0.748427556  0.410991509  1.01598343
-#> 4   -0.988547303 -1.612324193  0.10827445
-#> 5    0.250415810  1.967522573  1.12127447
-#> 6    1.012473136  1.329047164  0.46137529
-#> 7    0.290391321 -0.492076253 -1.07257786
-#> 8   -0.999779485 -2.143044059 -1.61909580
-#> 9    1.296369128  0.223297040  0.05172819
-#> 10   1.844675802  0.525323939  1.43792983
-#> 11  -0.594332509  1.134701125 -0.51513681
-#> 12   2.892055704 -0.024653246 -0.87388294
-#> 13   0.674912545  1.145791522 -0.16442269
-#> 14   0.114929596  0.751160451  0.22516804
-#> 15  -0.798672240 -0.534844472 -1.59558698
-#> 16  -1.031496700  0.049687627 -0.54144674
-#> 17  -0.004310488 -1.375989084  0.16455645
-#> 18   1.415566785  1.598106413  1.06560667
-#> 19  -1.642731803 -0.241490494 -0.33724621
-#> 20   0.910502722 -0.191788473 -0.98662517
-#> 21  -1.175150978 -0.479527388  0.14625695
-#> 22   0.305901881  0.761645013 -0.54205693
-#> 23  -0.367942472 -1.256019905 -0.12063644
-#> 24   0.892308000  0.193255995  1.16876289
-#> 25   2.538452134  2.240282666  2.04947504
-#> 26  -0.695068465  0.222697327 -1.11980217
-#> 27   0.512021718  0.508770162  0.57718656
-#> 28  -0.932274424  2.643562030  2.24879422
-#> 29  -0.585604074  0.622505249 -0.24917585
-#> 30  -1.355941412 -0.590585765 -0.21519908
-#> 31  -0.733846362  0.082328508 -0.42830930
-#> 32   1.264872249  0.715577491  0.12590367
-#> 33  -0.518786456  0.201122493 -0.92638734
-#> 34  -0.637506919 -1.122500780 -0.86513473
-#> 35   0.632266473 -0.010632207  1.05787745
-#> 36   1.104769095  0.406299810 -0.21379747
-#> 37   1.472185167  0.971669943  0.38215346
-#> 38   0.208642678  1.232615573 -0.26677942
-#> 39   0.194660642 -0.021702220 -0.36527459
-#> 40   1.781764439  0.408108369  1.86009067
-#> 41   2.042761937 -0.215878860  1.47377857
-#> 42  -0.490815530 -0.425984391 -0.68877135
-#> 43   1.655781000 -0.033659668  1.27356903
-#> 44  -0.025382344  0.905039301 -0.02548762
-#> 45  -0.665268785 -0.563017316  0.06761990
-#> 46  -0.276302646  0.608159422  0.33566043
-#> 47   0.484250987  1.126346311  1.25406657
-#> 48   1.282907646  0.720836758  0.47160057
-#> 49  -0.277030402 -1.021860386 -1.96611414
-#> 50  -0.819145743 -0.037232249 -0.04063296
-#> 51  -1.564663610 -0.636464908 -1.85042764
-#> 52  -0.898058218  0.284764624 -0.34268128
-#> 53   1.805225599 -0.408926161  0.83297444
-#> 54   1.365198223  1.113118376  1.19018362
-#> 55   0.216231182 -0.115908105  0.10565315
-#> 56   0.641593299  1.738105080  2.21714184
-#> 57   0.213674643  0.168441446 -0.31953324
-#> 58   0.253013272  0.862077906 -0.22789514
-#> 59   0.287070174 -1.951729555 -0.83911657
-#> 60  -0.481794003  0.239976721  0.61024139
-#> 61   0.025132801 -0.628056375 -1.53689844
-#> 62  -0.167673231 -0.101515541  0.75379782
-#> 63  -2.425362627 -0.498203692 -1.06272155
-#> 64   1.350643220 -0.486047718  0.95719724
-#> 65  -1.242626738  2.175710297  0.92752367
-#> 66  -0.338836433  1.113640640 -0.15518463
-#> 67  -0.026841819  0.966138768  0.37921477
-#> 68  -0.453608406  0.413598279  0.23549706
-#> 69  -0.493723033 -1.334332953 -0.84630857
-#> 70  -1.221646818 -1.238780109  0.72514498
-#> 71   0.321048652  1.743009432 -0.49620029
-#> 72  -0.527149031  1.184322665  1.00890307
-#> 73   0.070016525  1.224225492  0.62085618
-#> 74   1.134493406  2.219461421  1.58638671
-#> 75   1.504749960  0.614156332  1.09643083
-#> 76   0.446530671  0.052052719  0.61180283
-#> 77   1.818863849 -0.302817368  0.04494236
-#> 78  -0.791160513  0.179908256  0.79171348
-#> 79  -0.300914823 -0.253138072 -0.53330713
-#> 80   0.926355041  1.873512137  0.35236855
-#> 81   0.144573957  1.043953709 -0.49422615
-#> 82  -0.027810676  0.325938318 -1.58919459
-#> 83  -2.100930284  1.066836105 -0.83668769
-#> 84  -0.444333571 -0.715766312 -0.85121894
-#> 85  -0.968875193 -1.687613536 -0.79915130
-#> 86   0.959440062 -1.088246737  1.55349882
-#> 87  -0.345251762 -1.686853560  0.65311349
-#> 88   0.572082195 -0.813048594 -1.24514160
-#> 89  -1.365521552 -1.373149134 -1.59569125
-#> 90   1.176097394  2.119269863  1.42040520
-#> 91   1.221033387 -0.577310879 -0.56619463
-#> 92   0.108998277  0.648942202 -0.11418245
-#> 93   0.101151686  0.111384203  0.73298987
-#> 94   0.586259342  1.453725946  1.13935521
-#> 95   0.153452555  1.348353614 -0.27134221
-#> 96   0.235523130  0.480340620 -0.05456858
-#> 97   0.785445485  1.876836130 -0.04612489
-#> 98  -0.198978331 -0.525346365 -0.12181418
-#> 99   0.629380511 -0.357590811  0.51006913
-#> 100 -2.276017019 -0.001639159 -1.01436632
    rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
    #>               A            B           C
-#> 1    0.98053619  1.397345080  1.24702308
-#> 2    0.28494917 -0.929721452  0.11231993
-#> 3   -0.48926432 -0.317158657  0.91387543
-#> 4    0.62746163  0.690257228 -0.04676857
-#> 5    0.52497027 -0.006770402 -0.54982277
-#> 6   -0.08113113 -1.203695112 -0.38591526
-#> 7    0.10950230 -0.448304595 -0.72749899
-#> 8   -0.98054763 -1.413085775  0.44500300
-#> 9   -0.22717869  0.588496717  1.47955055
-#> 10  -0.08167748  0.129810359  0.62297867
-#> 11  -1.06794579  1.622348860  2.17266535
-#> 12   1.23713517  1.254934760  0.21802500
-#> 13   2.34268548 -0.127564780 -1.84686336
-#> 14  -0.79906111 -0.670100344 -0.78681222
-#> 15   1.14762821  0.425896059 -0.19353105
-#> 16  -0.68792927  0.746387469  1.17342419
-#> 17  -0.42239464 -2.279769415 -1.45252677
-#> 18  -0.71983873 -0.782993158 -0.48339888
-#> 19  -0.27644209 -0.078365574 -0.15548499
-#> 20   0.65702277 -2.167247198 -2.28768427
-#> 21   1.36346504  2.807540375  0.61058213
-#> 22   2.26453757 -0.107450280 -1.03851429
-#> 23   1.07057295  1.036447960  0.77105891
-#> 24  -0.75535093  0.189251094  1.72491550
-#> 25  -0.68100214 -0.665149580 -0.04357655
-#> 26  -0.47017350 -0.314580812 -0.27934568
-#> 27   0.73476477  1.147051694  0.19216533
-#> 28  -0.09149070  0.876867711 -0.08339957
-#> 29  -2.05839553 -1.187120062 -0.84369069
-#> 30  -0.62779679  0.680592120  0.13823150
-#> 31   0.82896856  0.492073064 -0.10702775
-#> 32   1.12230254  0.029101542 -0.69550195
-#> 33   1.41109327 -0.547954608 -1.55760838
-#> 34  -1.28998785 -0.644308963  0.45818445
-#> 35  -0.74524292  0.423205419  2.23457494
-#> 36   0.18163205  0.475178614  0.81660170
-#> 37  -1.29214617  0.507173875  0.60586872
-#> 38  -0.52453660 -0.422831247  0.14219949
-#> 39   2.71082498  1.025301778 -0.77900169
-#> 40   0.67234061 -0.927892656 -0.94160161
-#> 41  -0.22932465 -0.452949466 -0.75393801
-#> 42   0.79662820  0.012013809 -0.66456985
-#> 43  -1.48598921 -1.485862845 -0.34075299
-#> 44  -2.73794234 -0.333565773  1.35801078
-#> 45   1.04730267 -0.239862856 -0.94911295
-#> 46   0.79570220  0.409234356 -0.02512850
-#> 47  -0.29797799 -0.960151155  0.32858669
-#> 48   1.32398457  0.086940658 -0.84502893
-#> 49   0.70645034  0.763352966  0.66553042
-#> 50  -1.19011584 -0.898273386  0.01265418
-#> 51   0.17313328  0.375553597 -0.01675357
-#> 52   1.04061188 -0.219130525 -0.77337525
-#> 53  -0.43883826 -0.042922039  1.27360295
-#> 54   0.67987637 -1.058120498 -0.27502560
-#> 55  -0.51468288  0.062507127  0.76999444
-#> 56  -0.80241295 -0.945210621  0.34889021
-#> 57  -0.47060134 -1.268959331  0.70686003
-#> 58   2.49959376  0.682354301 -1.46377249
-#> 59  -0.76711289 -0.641906992  0.26363487
-#> 60   0.84037060 -0.130252667 -0.61136080
-#> 61  -0.47759972  0.405378724  0.06637905
-#> 62  -1.35795459  0.303024258  1.33414293
-#> 63  -0.86120089 -1.445283701  0.98588190
-#> 64   0.65405904 -1.262137582 -1.45782179
-#> 65  -1.52487511 -1.185720620 -0.29528001
-#> 66  -0.60310623  0.543202595  0.16109147
-#> 67  -1.34640940  0.111464903  0.03001872
-#> 68  -1.20726090 -0.211677096  0.84891723
-#> 69  -0.07838011  0.191885581  0.98564804
-#> 70   1.13262307  0.785543693 -0.06059885
-#> 71   1.36490199 -0.252005899 -2.00968693
-#> 72   1.18092139 -0.345183489 -0.80804496
-#> 73  -1.50057211 -0.143484529  0.56351319
-#> 74  -0.67605220 -0.161655297  0.70612018
-#> 75  -0.39630979 -0.711680593 -0.01193046
-#> 76  -1.06729240  0.790110596  0.42953674
-#> 77   0.25135374  0.004217116 -1.46584325
-#> 78  -0.62606452 -0.500831364  0.35233621
-#> 79   0.22130814 -1.054521838 -0.13425086
-#> 80  -0.82249462 -0.106502250  1.12587401
-#> 81   1.13659660  0.694685870 -0.63335312
-#> 82   0.63912932 -0.346001998 -0.15632373
-#> 83   0.50735668 -0.121686372 -0.35290927
-#> 84   0.95787156  0.663581444 -0.85623386
-#> 85  -0.91227314 -1.085103286  0.84931769
-#> 86  -0.23904032  0.820166860  0.16160248
-#> 87  -0.71341295 -0.149361316  0.56590330
-#> 88   1.64368977 -1.175172449 -2.12371033
-#> 89  -0.35317370  0.697507425 -0.12301334
-#> 90  -1.24671366 -0.243868992  1.74687093
-#> 91  -0.64148189  0.722090091  1.47996273
-#> 92   0.22026898  1.174067582  1.19864762
-#> 93  -0.88325604  0.271478223  0.35463827
-#> 94   0.07313435  1.405022266  1.00772946
-#> 95  -1.47150701  0.560249257  1.03483886
-#> 96  -1.10044697 -1.187067944  0.66307881
-#> 97  -2.23696083  0.102519789  2.21376158
-#> 98  -0.92624102 -1.158465566  0.37674556
-#> 99  -1.39758231  0.256387690  1.30028559
-#> 100 -0.21576305 -0.512530549 -0.64474625
    + 
    rnorm_multi(100, 3, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
    #>               A            B           C
+#> 1   -0.22938009  0.012786425 -0.90004352
+#> 2    1.56046133 -0.385432656  0.75531705
+#> 3    0.12914257 -0.656736867 -1.93726361
+#> 4   -0.07215368 -0.395541430  0.49442871
+#> 5   -2.24668003 -1.356275428  1.12155272
+#> 6    0.26548664 -0.290537897  1.64841161
+#> 7   -0.89850890 -0.158541994 -1.32181766
+#> 8   -0.43317818 -0.136663326  0.25267470
+#> 9   -1.86365445  0.627127133  0.25580039
+#> 10   2.84893203  1.378454920  0.21729455
+#> 11  -0.95339839  0.191117349 -0.05369938
+#> 12  -0.14063788 -0.999410460  1.12898204
+#> 13   0.98636258  0.658537288  2.56502490
+#> 14  -1.07051311 -0.041799453 -1.13098940
+#> 15   0.74214155  0.968599861  1.01242695
+#> 16   0.27510590  2.526850459 -1.36301149
+#> 17   0.75851442  0.824858441 -0.02201251
+#> 18   0.61540640 -1.284281532  0.44739809
+#> 19  -1.25229977  0.649401784  1.04299634
+#> 20   1.14120612  1.472838252 -0.41407483
+#> 21  -0.13893587  2.439369263  0.96815442
+#> 22   0.12442575  0.849453778  0.86295126
+#> 23   0.24020439  1.791814046 -0.80454355
+#> 24   0.32430107  0.826566547  1.23777632
+#> 25  -0.79843255  0.507583992  2.38850493
+#> 26   1.43339006  0.422294733  0.40255490
+#> 27   0.54042449  1.003611011 -0.43584735
+#> 28  -0.27270248  0.137349427  0.48404969
+#> 29   0.90875306 -0.128264082 -0.32678895
+#> 30  -1.32895871  0.592149422 -1.01239965
+#> 31   0.43742535 -0.746199669  1.43536319
+#> 32  -1.11281425  1.246184665  0.67648861
+#> 33   0.94702859  0.481719629  0.74368546
+#> 34  -1.36251843 -0.905117010  2.12354888
+#> 35   0.27105336 -0.544912060  0.61203808
+#> 36  -0.43953477  0.854341594  0.36600299
+#> 37  -0.64519574 -0.455202501 -1.41008747
+#> 38   0.87524470  0.113104781  0.22076276
+#> 39   1.35337579  1.294961626  0.18655402
+#> 40  -1.48650474  0.467304440  1.56730788
+#> 41  -0.70281495  0.237323663  0.57003485
+#> 42   1.51763370 -0.427310169 -0.13912332
+#> 43   1.30018974  2.276505621  0.45003510
+#> 44   1.40376784  1.166440199  0.24851656
+#> 45  -1.23127078  1.379956696  0.94479712
+#> 46   1.58291086  1.721780992  3.25541758
+#> 47   0.20312594 -0.719710829  0.54653138
+#> 48   0.12211318  0.914599819  1.07770907
+#> 49   0.64142823 -1.109099836  0.61819487
+#> 50   0.32892290  1.932741480 -1.41172049
+#> 51  -0.63021426  0.829147371  1.36208648
+#> 52  -0.30359291  0.522103414  0.25676326
+#> 53  -0.99364118 -0.738012401  0.29391281
+#> 54  -0.55172332 -0.158015129  0.51917759
+#> 55   1.65584140  1.452538537  0.52956075
+#> 56  -0.62860353  1.077683644  1.04982754
+#> 57   0.48977446  0.438499547 -1.77411486
+#> 58  -0.28005348  0.043618813  3.18255718
+#> 59  -0.40482935  1.182844102  0.13877874
+#> 60   1.66011018  1.204411616  0.71335575
+#> 61   0.34967935 -1.500060823  1.57434588
+#> 62  -1.23332110  1.335784286 -0.16508825
+#> 63   0.18969668  0.090948497  1.61395242
+#> 64  -0.01223603  0.663066677  0.25410359
+#> 65  -0.70634018 -1.390591856 -0.67756331
+#> 66  -1.90215248 -0.388258845 -0.47585062
+#> 67   2.09336046 -0.733021669  1.56505732
+#> 68  -0.76812584  0.763652568  0.63167063
+#> 69   0.09739696  0.114112086  0.98862881
+#> 70   0.43995957  0.917669134 -1.19945057
+#> 71   0.26089889  0.297091330 -0.97073631
+#> 72  -1.97757603 -0.574069593  0.78415034
+#> 73   0.08213986  1.670672301  1.83732041
+#> 74   0.31229479  1.360864787  0.73669628
+#> 75   0.20788620  1.168721370  1.81829338
+#> 76   2.07774387  1.435930771  1.02390979
+#> 77   2.35875655 -0.073887074  1.10674805
+#> 78   0.90971452 -0.875334875  0.39006433
+#> 79   0.96698338  0.094379326  0.67218172
+#> 80  -0.10821142  2.611769487  0.40967271
+#> 81   1.21200185 -0.641668381  2.42434334
+#> 82  -0.71905160 -0.746523850  1.79839276
+#> 83   0.76338008 -1.275327303  1.24879127
+#> 84   0.52248275  1.925938655  1.05622433
+#> 85   0.56667436  0.954185515 -0.04825726
+#> 86   1.32983515  2.393110265  1.61053489
+#> 87  -0.74149808  0.245879260 -2.11233433
+#> 88   0.41783764  2.964408338  0.34430622
+#> 89   1.61541229  1.461999145  0.93388979
+#> 90  -0.18373305  1.542694878  0.11804889
+#> 91   0.02591363  1.523838843  0.92418757
+#> 92  -0.02174452  0.002998507  1.56310200
+#> 93  -0.80952872 -0.423261151  1.54871262
+#> 94   0.68072527 -0.178884625  0.46189711
+#> 95   1.80440733  2.163789378  0.98614892
+#> 96  -1.31502453  0.532992146  2.17288261
+#> 97  -1.21602391  0.776499328  0.14563884
+#> 98   1.07677733  1.538707653  1.44634789
+#> 99   0.82413241  1.641263075  1.81682636
+#> 100  2.31227729  1.012090945  0.20335998
    rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
    #> Error in rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1),     varnames = c("A", "B", "C")): the length of mu must be 1 or vars
    @@ -126,7 +126,7 @@

    Correlated Normal Vector

    - 
    rnorm_pre(x, rho = 0, ymean = 0, ysd = 1)
    + 
    rnorm_pre(x, mu = 0, sd = 1, r = 0)

    Arguments

    @@ -136,17 +136,17 @@

    Arg

    - - - - - + - + + + + +

    the existing vector
    rho

    desired correlation between existing and returned vectors

    ymeanmu

    desired mean of returned vector
    ysdsd

    desired SD of returned vector
    r

    desired correlation between existing and returned vectors

    Value

    @@ -156,7 +156,7 @@

    Value

    Examples

    v1 <- rnorm(10)
-v2 <- rnorm_pre(v1, 0.5, 0, 1)
+v2 <- rnorm_pre(v1, 0, 1, 0.5)
 cor(v1, v2)
    #> [1] 0.5
    diff --git a/docs/reference/sim_design.html b/docs/reference/sim_design.html index c06a8398..d9efa198 100644 --- a/docs/reference/sim_design.html +++ b/docs/reference/sim_design.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -126,8 +126,8 @@

    Simulate Data from Design

    - 
    sim_design(within = list(), between = list(), n = 100, cors = 0,
-  mu = 0, sd = 1, empirical = FALSE, frame_long = FALSE)
    + 
    sim_design(within = list(), between = list(), n = 100, mu = 0,
+  sd = 1, r = 0, empirical = FALSE, frame_long = FALSE)

    Arguments

    @@ -144,10 +144,6 @@

    Arg

    - - - - @@ -156,9 +152,13 @@

    Arg

    + + + + - + diff --git a/docs/reference/sim_design_.html b/docs/reference/sim_design_.html index 2493ec46..f22f4165 100644 --- a/docs/reference/sim_design_.html +++ b/docs/reference/sim_design_.html @@ -32,7 +32,7 @@ - + @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -122,7 +122,7 @@

    Simulate Data from Design

    -

    sim_from_design generates a dataframe with a specified design

    +

    sim_design_ generates a dataframe with a specified design

    @@ -137,7 +137,7 @@

    Arg

    - + diff --git a/docs/reference/sim_df.html b/docs/reference/sim_df.html index b5b6d4e2..79deab5e 100644 --- a/docs/reference/sim_df.html +++ b/docs/reference/sim_df.html @@ -63,7 +63,7 @@ faux - 0.0.0.9005 + 0.0.0.9006 @@ -126,15 +126,15 @@

    Simulate an existing dataframe

    - 
    sim_df(dat, n = 100, between = c(), within = c(),
-  empirical = FALSE)
    + 
    sim_df(dat, n = 100, between = c(), empirical = FALSE,
+  grp_by = NULL)

    Arguments

    n

    the number of samples required
    cors

    the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)

    mu

    a vector giving the means of the variables (numeric vector of length 1 or vars)

    		sd

    the standard deviations of the variables (numeric vector of length 1 or vars)
    r

    the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector)

    empirical

    logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance

    logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
    frame_long
    empirical

    logical. If true, mu, sd and cors specify the empirical not population mean, sd and covariance

    logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
    frame_long
    - + @@ -142,16 +142,16 @@

    Arg

    - - - - - + + + + +
    dat

    the existing dataframe

    the existing dataframe (must be in wide format)
    n
    between

    a list of the between-subject factors

    within

    a list of the within-subject factors

    a list of the between-subject columns
    empirical

    logical. Passed on to rnorm_multi
    grp_by

    (deprecated; use between)

    Value

    @@ -161,7 +161,7 @@

    Value

    Examples

    iris100 <- sim_df(iris, 100)
-iris_species <- sim_df(iris, 100, "Species")
    +iris_species <- sim_df(iris, 100, between = "Species")     -

    You can set frame_long = TRUE to return the data frame in long format, which is usually easier for plotting.

    +

    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, 
-                 frame_long = TRUE)
    + long = TRUE)

    diff --git a/docs/articles/sim_df.html b/docs/articles/sim_df.html index b69b7f57..b970f643 100644 --- a/docs/articles/sim_df.html +++ b/docs/articles/sim_df.html @@ -82,7 +82,7 @@

    Simulate from Existing Data

    Lisa DeBruine

    -

    2019-05-02

    +

    2019-05-03

    diff --git a/docs/index.html b/docs/index.html index 0e40054e..6c9528ff 100644 --- a/docs/index.html +++ b/docs/index.html @@ -395,6 +395,23 @@ 

    make_id(n = 10, prefix = "ITEM_")
 #>  [1] "ITEM_01" "ITEM_02" "ITEM_03" "ITEM_04" "ITEM_05" "ITEM_06" "ITEM_07"
 #>  [8] "ITEM_08" "ITEM_09" "ITEM_10"
    +

    You can also manually set the number of digits and set n to a range of integers.

    +
    make_id(n = 10:20, digits = 3)
+#>  [1] "S010" "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019"
+#> [11] "S020"
    + +
    +

    +long2wide

    +
    between <- list("pet" = c("cat", "dog"))
+within <- list("time" = c("day", "night"))
+df_long <- sim_design(within, between, long = TRUE)
+
+df_wide <- long2wide(df_long, 
+                     within = "time", 
+                     between = "pet", 
+                     dv = "val", 
+                     id = "sub_id")

    diff --git a/docs/news/index.html b/docs/news/index.html index ff5eeb2d..b8338901 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -130,6 +130,14 @@

    • Bug fixes for sim_design() (failed when within or between factor number was 0)
    +

    +
    +

    +faux 0.0.0.9006

    +
      +
    • Changes to argument order and names (more consistent, but may break old scripts)
      • +
    • Updated vignettes
      • +
    @@ -139,6 +147,7 @@

    Contents

    diff --git a/docs/reference/check_design.html b/docs/reference/check_design.html index 801e75e0..b6769873 100644 --- a/docs/reference/check_design.html +++ b/docs/reference/check_design.html @@ -167,7 +167,90 @@

    Examp 
    
 within <- list(time = c("day", "night"))
 between <- list(pet = c("dog", "cat"))
-design <- check_design(within, between)
    +check_design(within, between)
    #> $within +#> $within$time +#> [1] "day" "night" +#> +#> +#> $between +#> $between$pet +#> [1] "dog" "cat" +#> +#> +#> $within_factors +#> [1] "time" +#> +#> $between_factors +#> [1] "pet" +#> +#> $within_labels +#> $within_labels$time +#> day night +#> "day" "night" +#> +#> +#> $between_labels +#> $between_labels$pet +#> dog cat +#> "dog" "cat" +#> +#> +#> $cell_n +#> dog cat +#> day 100 100 +#> night 100 100 +#> +#> $cell_mu +#> dog cat +#> day 0 0 +#> night 0 0 +#> +#> $cell_sd +#> dog cat +#> day 1 1 +#> night 1 1 +#> +#> $cell_r +#> $cell_r$dog +#> [,1] [,2] +#> [1,] 1 0 +#> [2,] 0 1 +#> +#> $cell_r$cat +#> [,1] [,2] +#> [1,] 1 0 +#> [2,] 0 1 +#> +#> +#> $cells_w +#> [1] "day" "night" +#> +#> $cells_b +#> [1] "dog" "cat" +#> +#> $sub_id +#> [1] "S001" "S002" "S003" "S004" "S005" "S006" "S007" "S008" "S009" "S010" +#> [11] "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019" "S020" +#> [21] "S021" "S022" "S023" "S024" "S025" "S026" "S027" "S028" "S029" "S030" +#> [31] "S031" "S032" "S033" "S034" "S035" "S036" "S037" "S038" "S039" "S040" +#> [41] "S041" "S042" "S043" "S044" "S045" "S046" "S047" "S048" "S049" "S050" +#> [51] "S051" "S052" "S053" "S054" "S055" "S056" "S057" "S058" "S059" "S060" +#> [61] "S061" "S062" "S063" "S064" "S065" "S066" "S067" "S068" "S069" "S070" +#> [71] "S071" "S072" "S073" "S074" "S075" "S076" "S077" "S078" "S079" "S080" +#> [81] "S081" "S082" "S083" "S084" "S085" "S086" "S087" "S088" "S089" "S090" +#> [91] "S091" "S092" "S093" "S094" "S095" "S096" "S097" "S098" "S099" "S100" +#> [101] "S101" "S102" "S103" "S104" "S105" "S106" "S107" "S108" "S109" "S110" +#> [111] "S111" "S112" "S113" "S114" "S115" "S116" "S117" "S118" "S119" "S120" +#> [121] "S121" "S122" "S123" "S124" "S125" "S126" "S127" "S128" "S129" "S130" +#> [131] "S131" "S132" "S133" "S134" "S135" "S136" "S137" "S138" "S139" "S140" +#> [141] "S141" "S142" "S143" "S144" "S145" "S146" "S147" "S148" "S149" "S150" +#> [151] "S151" "S152" "S153" "S154" "S155" "S156" "S157" "S158" "S159" "S160" +#> [161] "S161" "S162" "S163" "S164" "S165" "S166" "S167" "S168" "S169" "S170" +#> [171] "S171" "S172" "S173" "S174" "S175" "S176" "S177" "S178" "S179" "S180" +#> [181] "S181" "S182" "S183" "S184" "S185" "S186" "S187" "S188" "S189" "S190" +#> [191] "S191" "S192" "S193" "S194" "S195" "S196" "S197" "S198" "S199" "S200" +#>
    +
    - 
    check_sim_stats(dat, between = c(), within = c(), dv = c(),
+    
    check_sim_stats(.data, between = c(), within = c(), dv = c(),
   id = c(), digits = 2, usekable = FALSE)
    
     
     
    Arguments
    
     
-      
-      
+      
+      
@@ -164,26 +164,26 @@ 
    Arg
     
     
    Value
    
 
-    
    tibble or kable
    
+    
    a tbl or kable
    Examples
    
-    
    check_sim_stats(iris, "Species")
    #> # A tibble: 12 x 9
+    
    check_sim_stats(iris, "Species")
    #> # A tibble: 12 x 9
 #>    Species     n var   Sepal.Length Sepal.Width Petal.Length Petal.Width  mean
-#>    <fct>   <dbl> <chr>        <dbl>       <dbl>        <dbl>       <dbl> <dbl>
-#>  1 setosa     50 Sepa…         1           0.74         0.27        0.28  5.01
-#>  2 setosa     50 Sepa…         0.74        1            0.18        0.23  3.43
-#>  3 setosa     50 Peta…         0.27        0.18         1           0.33  1.46
-#>  4 setosa     50 Peta…         0.28        0.23         0.33        1     0.25
-#>  5 versic…    50 Sepa…         1           0.53         0.75        0.55  5.94
-#>  6 versic…    50 Sepa…         0.53        1            0.56        0.66  2.77
-#>  7 versic…    50 Peta…         0.75        0.56         1           0.79  4.26
-#>  8 versic…    50 Peta…         0.55        0.66         0.79        1     1.33
-#>  9 virgin…    50 Sepa…         1           0.46         0.86        0.28  6.59
-#> 10 virgin…    50 Sepa…         0.46        1            0.4         0.54  2.97
-#> 11 virgin…    50 Peta…         0.86        0.4          1           0.32  5.55
-#> 12 virgin…    50 Peta…         0.28        0.54         0.32        1     2.03
-#> # … with 1 more variable: sd <dbl>
    
+#>        <fct>   <dbl> <chr>        <dbl>       <dbl>        <dbl>       <dbl> <dbl>
+#>  1 setosa     50 Sepa…         1           0.74         0.27        0.28  5.01
+#>  2 setosa     50 Sepa…         0.74        1            0.18        0.23  3.43
+#>  3 setosa     50 Peta…         0.27        0.18         1           0.33  1.46
+#>  4 setosa     50 Peta…         0.28        0.23         0.33        1     0.25
+#>  5 versic…    50 Sepa…         1           0.53         0.75        0.55  5.94
+#>  6 versic…    50 Sepa…         0.53        1            0.56        0.66  2.77
+#>  7 versic…    50 Peta…         0.75        0.56         1           0.79  4.26
+#>  8 versic…    50 Peta…         0.55        0.66         0.79        1     1.33
+#>  9 virgin…    50 Sepa…         1           0.46         0.86        0.28  6.59
+#> 10 virgin…    50 Sepa…         0.46        1            0.4         0.54  2.97
+#> 11 virgin…    50 Peta…         0.86        0.4          1           0.32  5.55
+#> 12 virgin…    50 Peta…         0.28        0.54         0.32        1     2.03
+#> # … with 1 more variable: sd <dbl>
    
     
     
    dat
    the existing dataframe
    		.data
    the existing tbl
    		
     
    
     
    
       between
     
 
     
   
   
    
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
+      
+      
+    
+    
    .data
    the data frame (in long format)
    id
    the column name(s) that identify a unit of analysis
    dv
    the column name that identifies the DV
    
+    
+    
    Value
    
+
+    
    the data frame in long format
    
+    
+
+  
+  
+
+
+      
    
+      
    
+  
    Developed by Lisa DeBruine.
    
+
    
+
+
    
+  
    Site built with pkgdown 1.3.0.
    
+
    
+      
    
+   
+
+  
+
+  
+
+
diff --git a/docs/reference/index.html b/docs/reference/index.html
index 85381922..bedc5427 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -162,6 +162,12 @@ 
    get_design_long() 
    
+        
+        
    Get design from long data
    
+      
+        
         
           
    is_pos_def() 
    
         
diff --git a/docs/reference/long2wide.html b/docs/reference/long2wide.html
index b5ec7c90..654b00f5 100644
--- a/docs/reference/long2wide.html
+++ b/docs/reference/long2wide.html
@@ -126,14 +126,15 @@ 
    Long to wide format
    
     
     
 
-    
    long2wide(dat, within = c(), between = c(), dv = c(), id = c())
    
+    
    long2wide(.data, within = c(), between = c(), dv = "val",
+  id = "sub_id")
    
     
     
    Arguments
    
     
-      
-      
+      
+      
@@ -155,9 +156,26 @@ 
    Arg
     
     
    Value
    
 
-    
    the data frame in wide format
    
+    
    a tbl in wide format
    
     
 
+    
    Examples
    
+    
    df_long <- sim_design(2, 2, long = TRUE)
+long2wide(df_long, "A", "B", "val", "sub_id")
    #> # A tibble: 200 x 4
+#>    sub_id B         A1       A2
+#>    <chr>  <fct>  <dbl>    <dbl>
+#>  1 S001   B1     0.387 -1.40   
+#>  2 S002   B1     0.785  0.255  
+#>  3 S003   B1     1.06  -2.44   
+#>  4 S004   B1     0.796 -0.00557
+#>  5 S005   B1     1.76   0.622  
+#>  6 S006   B1     0.691  1.15   
+#>  7 S007   B1     0.559 -1.82   
+#>  8 S008   B1     0.537 -0.247  
+#>  9 S009   B1    -0.227 -0.244  
+#> 10 S010   B1    -0.978 -0.283  
+#> # … with 190 more rows
    
+
    
diff --git a/docs/reference/make_id.html b/docs/reference/make_id.html
index f1bf100d..999c632b 100644
--- a/docs/reference/make_id.html
+++ b/docs/reference/make_id.html
@@ -126,19 +126,23 @@ 
    Make ID
    
 
-    
    make_id(n = 100, prefix = "S")
    
+    
    make_id(n = 100, prefix = "S", digits = 0)
    Arguments
    
     
     
    dat
    the long data frame to convert
    		.data
    the tbl in long format
    		
     
    
     
    
       within
   
   
 
     
     
     
     
     
    
-      
+      
+    
+      
+      
+    
    
     
     
    
       n
    the number of IDs to generate
    the number of IDs to generate (or a vector of numbers)
    		
     
    
     
    
       prefix
       
    the letter prefix to the number
    		
     
    digits
    the number of digits to use for the numeric part. Only used if this is larger than the number of digits in n.
    
     
    
     
     
    Value
    
@@ -151,7 +155,9 @@ 
    Examp
 make_id(20, "SUBJECT_")
    #>  [1] "SUBJECT_01" "SUBJECT_02" "SUBJECT_03" "SUBJECT_04" "SUBJECT_05"
 #>  [6] "SUBJECT_06" "SUBJECT_07" "SUBJECT_08" "SUBJECT_09" "SUBJECT_10"
 #> [11] "SUBJECT_11" "SUBJECT_12" "SUBJECT_13" "SUBJECT_14" "SUBJECT_15"
-#> [16] "SUBJECT_16" "SUBJECT_17" "SUBJECT_18" "SUBJECT_19" "SUBJECT_20"
    
+#> [16] "SUBJECT_16" "SUBJECT_17" "SUBJECT_18" "SUBJECT_19" "SUBJECT_20"
    make_id(10:30, digits = 3)
    #>  [1] "S010" "S011" "S012" "S013" "S014" "S015" "S016" "S017" "S018" "S019"
+#> [11] "S020" "S021" "S022" "S023" "S024" "S025" "S026" "S027" "S028" "S029"
+#> [21] "S030"
    - 
    select_num_grp(dat, between = c(), cols = NULL)
    + 
    select_num_grp(.data, between = c(), cols = NULL)

    Arguments

    - - + + @@ -147,25 +147,25 @@

    Arg

    Value

    -

    tibble

    +

    a tbl

    Examples

    - 
    select_num_grp(iris, "Species")
    #> # A tibble: 150 x 5
-#> # Groups:   Species [3]
+    
    select_num_grp(iris, "Species")
    #> # A tibble: 150 x 5
+#> # Groups:   Species [3]
 #>    Species Sepal.Length Sepal.Width Petal.Length Petal.Width
-#>    <fct>          <dbl>       <dbl>        <dbl>       <dbl>
-#>  1 setosa           5.1         3.5          1.4         0.2
-#>  2 setosa           4.9         3            1.4         0.2
-#>  3 setosa           4.7         3.2          1.3         0.2
-#>  4 setosa           4.6         3.1          1.5         0.2
-#>  5 setosa           5           3.6          1.4         0.2
-#>  6 setosa           5.4         3.9          1.7         0.4
-#>  7 setosa           4.6         3.4          1.4         0.3
-#>  8 setosa           5           3.4          1.5         0.2
-#>  9 setosa           4.4         2.9          1.4         0.2
-#> 10 setosa           4.9         3.1          1.5         0.1
-#> # … with 140 more rows
    
+#>        <fct>          <dbl>       <dbl>        <dbl>       <dbl>
+#>  1 setosa           5.1         3.5          1.4         0.2
+#>  2 setosa           4.9         3            1.4         0.2
+#>  3 setosa           4.7         3.2          1.3         0.2
+#>  4 setosa           4.6         3.1          1.5         0.2
+#>  5 setosa           5           3.6          1.4         0.2
+#>  6 setosa           5.4         3.9          1.7         0.4
+#>  7 setosa           4.6         3.4          1.4         0.3
+#>  8 setosa           5           3.4          1.5         0.2
+#>  9 setosa           4.4         2.9          1.4         0.2
+#> 10 setosa           4.9         3.1          1.5         0.1
+#> # … with 140 more rows
    dat

    the existing dataframe

    		.data

    the existing tbl
    between
    @@ -161,14 +161,14 @@

    Arg

    - - + +

    logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
    frame_long

    Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format

    		long

    Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format

    Value

    -

    dataframe

    +

    a tbl

    diff --git a/docs/reference/sim_design_.html b/docs/reference/sim_design_.html index f22f4165..997e7e70 100644 --- a/docs/reference/sim_design_.html +++ b/docs/reference/sim_design_.html @@ -32,7 +32,7 @@ - + @@ -122,11 +122,11 @@

    Simulate Data from Design

    -

    sim_design_ generates a dataframe with a specified design

    +

    sim_design_ generates a data table with a specified design

    - 
    sim_design_(design, empirical = FALSE, frame_long = FALSE)
    + 
    sim_design_(design, empirical = FALSE, long = FALSE)

    Arguments

    @@ -140,14 +140,14 @@

    Arg

    - - + +

    logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance
    frame_long

    Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format

    		long

    Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format

    Value

    -

    dataframe

    +

    a tbl

    diff --git a/docs/reference/sim_df.html b/docs/reference/sim_df.html index 79deab5e..e3ea6eb6 100644 --- a/docs/reference/sim_df.html +++ b/docs/reference/sim_df.html @@ -32,7 +32,7 @@ - + @@ -122,19 +122,19 @@

    Simulate an existing dataframe

    -

    sim_df Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).

    +

    sim_df Produces a data table with the same distributions and correlations as an existing data table Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).

    - 
    sim_df(dat, n = 100, between = c(), empirical = FALSE,
+    
    sim_df(.data, n = 100, between = c(), empirical = FALSE,
   grp_by = NULL)
    
     
     
    Arguments
    
     
-      
-      
+      
+      
@@ -156,7 +156,7 @@ 
    Arg
     
     
    Value
    
 
-    
    tibble
    
+    
    a tbl
    Examples
    
diff --git a/docs/reference/sim_mixed_df.html b/docs/reference/sim_mixed_df.html
index 480535c1..36852235 100644
--- a/docs/reference/sim_mixed_df.html
+++ b/docs/reference/sim_mixed_df.html
@@ -32,7 +32,7 @@
 
 
 
-
+
 
 
@@ -122,19 +122,19 @@ 
    Generate a sample with random intercepts for subjects and items
    
     
-    
    sim_mixed_df Produces a dataframe with the same distributions of by-subject and by-item random intercepts as an existing dataframe
    
+    
    sim_mixed_df() produces a data table with the same distributions of by-subject and by-item random intercepts as an existing data table.
    
     
     
    
 
-    
    sim_mixed_df(dat, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
+    
    sim_mixed_df(.data, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
   item_id = 3)
    
     
     
    Arguments
    
     
    
     
     
    dat
    the existing dataframe (must be in wide format)
    		.data
    the existing tbl (must be in wide format)
    		
     
    
     
    
       n
     
 
     
 
 
     
    
-      
-      
+      
+      
@@ -160,111 +160,111 @@ 
    Arg
     
     
    Value
    
 
-    
    tibble
    
+    
    a tbl
    Examples
    
-    
    sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")
    #>     sub_id item_id       sub_i      item_i         dv
-#> 1        1       1 -1.31204794 -0.91527468 -0.5821852
-#> 2        2       1  0.58686412 -0.91527468  3.6621355
-#> 3        3       1  0.03209700 -0.91527468  1.1597510
-#> 4        4       1  0.19719216 -0.91527468  2.9041436
-#> 5        5       1  0.85825196 -0.91527468  1.6956532
-#> 6        6       1  1.23958914 -0.91527468  3.3635152
-#> 7        7       1 -0.04067057 -0.91527468  2.4206597
-#> 8        8       1 -0.49972036 -0.91527468  1.2830612
-#> 9        9       1 -0.39832107 -0.91527468  2.4032570
-#> 10      10       1 -0.27425086 -0.91527468  2.8374416
-#> 11       1       2 -1.31204794 -0.75442016  2.8801949
-#> 12       2       2  0.58686412 -0.75442016  4.6594123
-#> 13       3       2  0.03209700 -0.75442016  2.3269642
-#> 14       4       2  0.19719216 -0.75442016  4.2190505
-#> 15       5       2  0.85825196 -0.75442016  1.6568714
-#> 16       6       2  1.23958914 -0.75442016  3.7147063
-#> 17       7       2 -0.04067057 -0.75442016  2.6823381
-#> 18       8       2 -0.49972036 -0.75442016  1.2207087
-#> 19       9       2 -0.39832107 -0.75442016  0.1317110
-#> 20      10       2 -0.27425086 -0.75442016  3.5317627
-#> 21       1       3 -1.31204794 -0.52699466  2.3226909
-#> 22       2       3  0.58686412 -0.52699466  2.4430166
-#> 23       3       3  0.03209700 -0.52699466  1.9510354
-#> 24       4       3  0.19719216 -0.52699466  2.5561268
-#> 25       5       3  0.85825196 -0.52699466  2.7123415
-#> 26       6       3  1.23958914 -0.52699466  3.4618648
-#> 27       7       3 -0.04067057 -0.52699466  3.3228664
-#> 28       8       3 -0.49972036 -0.52699466  4.7390622
-#> 29       9       3 -0.39832107 -0.52699466  2.3873381
-#> 30      10       3 -0.27425086 -0.52699466  1.2534605
-#> 31       1       4 -1.31204794 -0.02510984  0.8446420
-#> 32       2       4  0.58686412 -0.02510984  4.0745936
-#> 33       3       4  0.03209700 -0.02510984  2.2571747
-#> 34       4       4  0.19719216 -0.02510984  2.1582137
-#> 35       5       4  0.85825196 -0.02510984  2.6919926
-#> 36       6       4  1.23958914 -0.02510984  3.2690829
-#> 37       7       4 -0.04067057 -0.02510984  3.6804353
-#> 38       8       4 -0.49972036 -0.02510984  0.2449312
-#> 39       9       4 -0.39832107 -0.02510984  4.4384063
-#> 40      10       4 -0.27425086 -0.02510984  2.5131083
-#> 41       1       5 -1.31204794  1.14363329  3.3097340
-#> 42       2       5  0.58686412  1.14363329  4.0280504
-#> 43       3       5  0.03209700  1.14363329  3.4736644
-#> 44       4       5  0.19719216  1.14363329  4.4829729
-#> 45       5       5  0.85825196  1.14363329  6.0465548
-#> 46       6       5  1.23958914  1.14363329  4.0794730
-#> 47       7       5 -0.04067057  1.14363329  4.0704548
-#> 48       8       5 -0.49972036  1.14363329  3.4539076
-#> 49       9       5 -0.39832107  1.14363329  4.4518836
-#> 50      10       5 -0.27425086  1.14363329  3.7230423
-#> 51       1       6 -1.31204794  0.02181121  1.7452042
-#> 52       2       6  0.58686412  0.02181121  4.5943491
-#> 53       3       6  0.03209700  0.02181121  3.0840564
-#> 54       4       6  0.19719216  0.02181121  3.6131343
-#> 55       5       6  0.85825196  0.02181121  2.9055922
-#> 56       6       6  1.23958914  0.02181121  3.2747244
-#> 57       7       6 -0.04067057  0.02181121  2.5952921
-#> 58       8       6 -0.49972036  0.02181121  2.2170451
-#> 59       9       6 -0.39832107  0.02181121  4.3551644
-#> 60      10       6 -0.27425086  0.02181121  0.6118565
-#> 61       1       7 -1.31204794 -0.53113887  0.9948724
-#> 62       2       7  0.58686412 -0.53113887  3.2054074
-#> 63       3       7  0.03209700 -0.53113887  1.8000139
-#> 64       4       7  0.19719216 -0.53113887  2.1077354
-#> 65       5       7  0.85825196 -0.53113887  3.0181285
-#> 66       6       7  1.23958914 -0.53113887  3.0721583
-#> 67       7       7 -0.04067057 -0.53113887  2.1009421
-#> 68       8       7 -0.49972036 -0.53113887  2.3233091
-#> 69       9       7 -0.39832107 -0.53113887  2.9582357
-#> 70      10       7 -0.27425086 -0.53113887  4.1178312
-#> 71       1       8 -1.31204794 -0.69069006  1.3048000
-#> 72       2       8  0.58686412 -0.69069006  3.2600878
-#> 73       3       8  0.03209700 -0.69069006  2.1985137
-#> 74       4       8  0.19719216 -0.69069006  2.8759249
-#> 75       5       8  0.85825196 -0.69069006  2.8337853
-#> 76       6       8  1.23958914 -0.69069006  2.7156484
-#> 77       7       8 -0.04067057 -0.69069006  2.2678694
-#> 78       8       8 -0.49972036 -0.69069006  1.6444369
-#> 79       9       8 -0.39832107 -0.69069006  1.7506832
-#> 80      10       8 -0.27425086 -0.69069006  2.8302596
-#> 81       1       9 -1.31204794 -0.41158246  1.5533167
-#> 82       2       9  0.58686412 -0.41158246  0.6675154
-#> 83       3       9  0.03209700 -0.41158246  2.7254063
-#> 84       4       9  0.19719216 -0.41158246  2.3380499
-#> 85       5       9  0.85825196 -0.41158246  3.0601278
-#> 86       6       9  1.23958914 -0.41158246  3.7980488
-#> 87       7       9 -0.04067057 -0.41158246  4.0910659
-#> 88       8       9 -0.49972036 -0.41158246  1.7803072
-#> 89       9       9 -0.39832107 -0.41158246  1.1584322
-#> 90      10       9 -0.27425086 -0.41158246  4.1641162
-#> 91       1      10 -1.31204794  0.45071102  1.3128191
-#> 92       2      10  0.58686412  0.45071102  4.3255009
-#> 93       3      10  0.03209700  0.45071102  2.4496289
-#> 94       4      10  0.19719216  0.45071102  2.5378068
-#> 95       5      10  0.85825196  0.45071102  3.1817382
-#> 96       6      10  1.23958914  0.45071102  4.4856500
-#> 97       7      10 -0.04067057  0.45071102  4.8521086
-#> 98       8      10 -0.49972036  0.45071102  3.5287348
-#> 99       9      10 -0.39832107  0.45071102  1.5642742
-#> 100     10      10 -0.27425086  0.45071102  2.1061951
    
+    
    sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")
    #>     sub_id item_id       sub_i     item_i        dv
+#> 1        1       1  0.15336871  0.1152507 3.4263347
+#> 2        2       1 -0.07546968  0.1152507 2.6552210
+#> 3        3       1 -0.09464565  0.1152507 2.5473771
+#> 4        4       1  0.13680669  0.1152507 4.5736075
+#> 5        5       1  0.46710613  0.1152507 4.6470300
+#> 6        6       1  0.71713231  0.1152507 5.0470723
+#> 7        7       1 -0.16377959  0.1152507 3.0286129
+#> 8        8       1 -0.03648029  0.1152507 3.0203508
+#> 9        9       1 -0.44490238  0.1152507 2.4007496
+#> 10      10       1 -0.50294582  0.1152507 1.4482325
+#> 11       1       2  0.15336871  0.9694343 4.2646276
+#> 12       2       2 -0.07546968  0.9694343 3.5117228
+#> 13       3       2 -0.09464565  0.9694343 1.9205807
+#> 14       4       2  0.13680669  0.9694343 3.3332175
+#> 15       5       2  0.46710613  0.9694343 4.1037056
+#> 16       6       2  0.71713231  0.9694343 4.8340290
+#> 17       7       2 -0.16377959  0.9694343 4.3931885
+#> 18       8       2 -0.03648029  0.9694343 3.5224828
+#> 19       9       2 -0.44490238  0.9694343 2.7403387
+#> 20      10       2 -0.50294582  0.9694343 4.9209943
+#> 21       1       3  0.15336871 -0.1809222 2.3955324
+#> 22       2       3 -0.07546968 -0.1809222 5.0649247
+#> 23       3       3 -0.09464565 -0.1809222 3.0032075
+#> 24       4       3  0.13680669 -0.1809222 2.4135067
+#> 25       5       3  0.46710613 -0.1809222 4.5656069
+#> 26       6       3  0.71713231 -0.1809222 2.8650467
+#> 27       7       3 -0.16377959 -0.1809222 3.6126806
+#> 28       8       3 -0.03648029 -0.1809222 0.9722492
+#> 29       9       3 -0.44490238 -0.1809222 0.6125408
+#> 30      10       3 -0.50294582 -0.1809222 3.8909402
+#> 31       1       4  0.15336871  1.0979794 5.9448229
+#> 32       2       4 -0.07546968  1.0979794 3.9574235
+#> 33       3       4 -0.09464565  1.0979794 4.8079004
+#> 34       4       4  0.13680669  1.0979794 5.0826263
+#> 35       5       4  0.46710613  1.0979794 3.3817108
+#> 36       6       4  0.71713231  1.0979794 3.2445244
+#> 37       7       4 -0.16377959  1.0979794 4.2746244
+#> 38       8       4 -0.03648029  1.0979794 4.7967650
+#> 39       9       4 -0.44490238  1.0979794 4.3737837
+#> 40      10       4 -0.50294582  1.0979794 4.2923638
+#> 41       1       5  0.15336871  0.2255086 5.9170173
+#> 42       2       5 -0.07546968  0.2255086 2.7538325
+#> 43       3       5 -0.09464565  0.2255086 3.1842397
+#> 44       4       5  0.13680669  0.2255086 4.2260520
+#> 45       5       5  0.46710613  0.2255086 1.5543152
+#> 46       6       5  0.71713231  0.2255086 3.9556215
+#> 47       7       5 -0.16377959  0.2255086 3.8447343
+#> 48       8       5 -0.03648029  0.2255086 4.5385200
+#> 49       9       5 -0.44490238  0.2255086 2.6456965
+#> 50      10       5 -0.50294582  0.2255086 2.2025837
+#> 51       1       6  0.15336871 -0.5688417 3.5177725
+#> 52       2       6 -0.07546968 -0.5688417 2.9838297
+#> 53       3       6 -0.09464565 -0.5688417 3.5923231
+#> 54       4       6  0.13680669 -0.5688417 3.1022826
+#> 55       5       6  0.46710613 -0.5688417 2.7535176
+#> 56       6       6  0.71713231 -0.5688417 5.4940446
+#> 57       7       6 -0.16377959 -0.5688417 2.5234260
+#> 58       8       6 -0.03648029 -0.5688417 0.3511046
+#> 59       9       6 -0.44490238 -0.5688417 1.8498874
+#> 60      10       6 -0.50294582 -0.5688417 3.5379786
+#> 61       1       7  0.15336871 -0.1003512 4.2409599
+#> 62       2       7 -0.07546968 -0.1003512 3.5556705
+#> 63       3       7 -0.09464565 -0.1003512 2.6899611
+#> 64       4       7  0.13680669 -0.1003512 2.0439883
+#> 65       5       7  0.46710613 -0.1003512 5.0354057
+#> 66       6       7  0.71713231 -0.1003512 3.5521496
+#> 67       7       7 -0.16377959 -0.1003512 2.3525066
+#> 68       8       7 -0.03648029 -0.1003512 3.5415480
+#> 69       9       7 -0.44490238 -0.1003512 4.1344729
+#> 70      10       7 -0.50294582 -0.1003512 2.3269392
+#> 71       1       8  0.15336871  1.6989795 6.4250556
+#> 72       2       8 -0.07546968  1.6989795 5.7450023
+#> 73       3       8 -0.09464565  1.6989795 3.8872425
+#> 74       4       8  0.13680669  1.6989795 4.5973502
+#> 75       5       8  0.46710613  1.6989795 5.7349937
+#> 76       6       8  0.71713231  1.6989795 2.6852695
+#> 77       7       8 -0.16377959  1.6989795 3.7919020
+#> 78       8       8 -0.03648029  1.6989795 4.7810577
+#> 79       9       8 -0.44490238  1.6989795 5.1999969
+#> 80      10       8 -0.50294582  1.6989795 5.4375538
+#> 81       1       9  0.15336871  1.2452224 5.3180720
+#> 82       2       9 -0.07546968  1.2452224 2.7003936
+#> 83       3       9 -0.09464565  1.2452224 4.3759073
+#> 84       4       9  0.13680669  1.2452224 6.5338902
+#> 85       5       9  0.46710613  1.2452224 3.8544295
+#> 86       6       9  0.71713231  1.2452224 5.2503336
+#> 87       7       9 -0.16377959  1.2452224 4.6527757
+#> 88       8       9 -0.03648029  1.2452224 3.6883031
+#> 89       9       9 -0.44490238  1.2452224 2.8378979
+#> 90      10       9 -0.50294582  1.2452224 4.8626384
+#> 91       1      10  0.15336871 -0.2064970 4.7460439
+#> 92       2      10 -0.07546968 -0.2064970 1.8389187
+#> 93       3      10 -0.09464565 -0.2064970 1.3021962
+#> 94       4      10  0.13680669 -0.2064970 2.4226626
+#> 95       5      10  0.46710613 -0.2064970 3.0711292
+#> 96       6      10  0.71713231 -0.2064970 2.2198113
+#> 97       7      10 -0.16377959 -0.2064970 0.9575364
+#> 98       8      10 -0.03648029 -0.2064970 2.6135275
+#> 99       9      10 -0.44490238 -0.2064970 1.2924662
+#> 100     10      10 -0.50294582 -0.2064970 0.8947152
    
 
-    
    wide2long(dat, within_factors = c(), within_cols = c(),
+    
    wide2long(.data, within_factors = c(), within_cols = c(),
   sep = "[^[:alnum:]]+")
    
     
     
    Arguments
    
     
    
     
     
    dat
    the existing dataframe
    		.data
    the existing tbl
    		
     
    
     
    
       sub_n
     
 
     
   
   
    
-      
-      
+      
+      
-      
-      
+      
+      
-      
-      
+      
+      
-      
-      
+      
+      
    
     
     
    dat
    the wide data frame to convert
    		.data
    the tbl in wide format
    		
     
    
     
    sep
    Separator between columns (see tidyr::separate)
    		within_factors
    the names of the within factors
    		
     
    
     
    within
    the names of the within factors
    		within_cols
    the names (or indices) of the within-subject (value) columns
    		
     
    
     
    dv
    the names of the DV (value) columns
    		sep
    Separator for within-columns (see tidyr::separate)
    		
     
    
     
    
     
     
    Value
    
 
-    
    the data frame in long format
    
+    
    a tbl in long format
    
     
 
+    
    Examples
    
+    
    wide2long(iris, c("Feature", "Measure"), 1:4)
    #>        Species Feature Measure val
+#> 1       setosa   Sepal  Length 5.1
+#> 2       setosa   Sepal  Length 4.9
+#> 3       setosa   Sepal  Length 4.7
+#> 4       setosa   Sepal  Length 4.6
+#> 5       setosa   Sepal  Length 5.0
+#> 6       setosa   Sepal  Length 5.4
+#> 7       setosa   Sepal  Length 4.6
+#> 8       setosa   Sepal  Length 5.0
+#> 9       setosa   Sepal  Length 4.4
+#> 10      setosa   Sepal  Length 4.9
+#> 11      setosa   Sepal  Length 5.4
+#> 12      setosa   Sepal  Length 4.8
+#> 13      setosa   Sepal  Length 4.8
+#> 14      setosa   Sepal  Length 4.3
+#> 15      setosa   Sepal  Length 5.8
+#> 16      setosa   Sepal  Length 5.7
+#> 17      setosa   Sepal  Length 5.4
+#> 18      setosa   Sepal  Length 5.1
+#> 19      setosa   Sepal  Length 5.7
+#> 20      setosa   Sepal  Length 5.1
+#> 21      setosa   Sepal  Length 5.4
+#> 22      setosa   Sepal  Length 5.1
+#> 23      setosa   Sepal  Length 4.6
+#> 24      setosa   Sepal  Length 5.1
+#> 25      setosa   Sepal  Length 4.8
+#> 26      setosa   Sepal  Length 5.0
+#> 27      setosa   Sepal  Length 5.0
+#> 28      setosa   Sepal  Length 5.2
+#> 29      setosa   Sepal  Length 5.2
+#> 30      setosa   Sepal  Length 4.7
+#> 31      setosa   Sepal  Length 4.8
+#> 32      setosa   Sepal  Length 5.4
+#> 33      setosa   Sepal  Length 5.2
+#> 34      setosa   Sepal  Length 5.5
+#> 35      setosa   Sepal  Length 4.9
+#> 36      setosa   Sepal  Length 5.0
+#> 37      setosa   Sepal  Length 5.5
+#> 38      setosa   Sepal  Length 4.9
+#> 39      setosa   Sepal  Length 4.4
+#> 40      setosa   Sepal  Length 5.1
+#> 41      setosa   Sepal  Length 5.0
+#> 42      setosa   Sepal  Length 4.5
+#> 43      setosa   Sepal  Length 4.4
+#> 44      setosa   Sepal  Length 5.0
+#> 45      setosa   Sepal  Length 5.1
+#> 46      setosa   Sepal  Length 4.8
+#> 47      setosa   Sepal  Length 5.1
+#> 48      setosa   Sepal  Length 4.6
+#> 49      setosa   Sepal  Length 5.3
+#> 50      setosa   Sepal  Length 5.0
+#> 51  versicolor   Sepal  Length 7.0
+#> 52  versicolor   Sepal  Length 6.4
+#> 53  versicolor   Sepal  Length 6.9
+#> 54  versicolor   Sepal  Length 5.5
+#> 55  versicolor   Sepal  Length 6.5
+#> 56  versicolor   Sepal  Length 5.7
+#> 57  versicolor   Sepal  Length 6.3
+#> 58  versicolor   Sepal  Length 4.9
+#> 59  versicolor   Sepal  Length 6.6
+#> 60  versicolor   Sepal  Length 5.2
+#> 61  versicolor   Sepal  Length 5.0
+#> 62  versicolor   Sepal  Length 5.9
+#> 63  versicolor   Sepal  Length 6.0
+#> 64  versicolor   Sepal  Length 6.1
+#> 65  versicolor   Sepal  Length 5.6
+#> 66  versicolor   Sepal  Length 6.7
+#> 67  versicolor   Sepal  Length 5.6
+#> 68  versicolor   Sepal  Length 5.8
+#> 69  versicolor   Sepal  Length 6.2
+#> 70  versicolor   Sepal  Length 5.6
+#> 71  versicolor   Sepal  Length 5.9
+#> 72  versicolor   Sepal  Length 6.1
+#> 73  versicolor   Sepal  Length 6.3
+#> 74  versicolor   Sepal  Length 6.1
+#> 75  versicolor   Sepal  Length 6.4
+#> 76  versicolor   Sepal  Length 6.6
+#> 77  versicolor   Sepal  Length 6.8
+#> 78  versicolor   Sepal  Length 6.7
+#> 79  versicolor   Sepal  Length 6.0
+#> 80  versicolor   Sepal  Length 5.7
+#> 81  versicolor   Sepal  Length 5.5
+#> 82  versicolor   Sepal  Length 5.5
+#> 83  versicolor   Sepal  Length 5.8
+#> 84  versicolor   Sepal  Length 6.0
+#> 85  versicolor   Sepal  Length 5.4
+#> 86  versicolor   Sepal  Length 6.0
+#> 87  versicolor   Sepal  Length 6.7
+#> 88  versicolor   Sepal  Length 6.3
+#> 89  versicolor   Sepal  Length 5.6
+#> 90  versicolor   Sepal  Length 5.5
+#> 91  versicolor   Sepal  Length 5.5
+#> 92  versicolor   Sepal  Length 6.1
+#> 93  versicolor   Sepal  Length 5.8
+#> 94  versicolor   Sepal  Length 5.0
+#> 95  versicolor   Sepal  Length 5.6
+#> 96  versicolor   Sepal  Length 5.7
+#> 97  versicolor   Sepal  Length 5.7
+#> 98  versicolor   Sepal  Length 6.2
+#> 99  versicolor   Sepal  Length 5.1
+#> 100 versicolor   Sepal  Length 5.7
+#> 101  virginica   Sepal  Length 6.3
+#> 102  virginica   Sepal  Length 5.8
+#> 103  virginica   Sepal  Length 7.1
+#> 104  virginica   Sepal  Length 6.3
+#> 105  virginica   Sepal  Length 6.5
+#> 106  virginica   Sepal  Length 7.6
+#> 107  virginica   Sepal  Length 4.9
+#> 108  virginica   Sepal  Length 7.3
+#> 109  virginica   Sepal  Length 6.7
+#> 110  virginica   Sepal  Length 7.2
+#> 111  virginica   Sepal  Length 6.5
+#> 112  virginica   Sepal  Length 6.4
+#> 113  virginica   Sepal  Length 6.8
+#> 114  virginica   Sepal  Length 5.7
+#> 115  virginica   Sepal  Length 5.8
+#> 116  virginica   Sepal  Length 6.4
+#> 117  virginica   Sepal  Length 6.5
+#> 118  virginica   Sepal  Length 7.7
+#> 119  virginica   Sepal  Length 7.7
+#> 120  virginica   Sepal  Length 6.0
+#> 121  virginica   Sepal  Length 6.9
+#> 122  virginica   Sepal  Length 5.6
+#> 123  virginica   Sepal  Length 7.7
+#> 124  virginica   Sepal  Length 6.3
+#> 125  virginica   Sepal  Length 6.7
+#> 126  virginica   Sepal  Length 7.2
+#> 127  virginica   Sepal  Length 6.2
+#> 128  virginica   Sepal  Length 6.1
+#> 129  virginica   Sepal  Length 6.4
+#> 130  virginica   Sepal  Length 7.2
+#> 131  virginica   Sepal  Length 7.4
+#> 132  virginica   Sepal  Length 7.9
+#> 133  virginica   Sepal  Length 6.4
+#> 134  virginica   Sepal  Length 6.3
+#> 135  virginica   Sepal  Length 6.1
+#> 136  virginica   Sepal  Length 7.7
+#> 137  virginica   Sepal  Length 6.3
+#> 138  virginica   Sepal  Length 6.4
+#> 139  virginica   Sepal  Length 6.0
+#> 140  virginica   Sepal  Length 6.9
+#> 141  virginica   Sepal  Length 6.7
+#> 142  virginica   Sepal  Length 6.9
+#> 143  virginica   Sepal  Length 5.8
+#> 144  virginica   Sepal  Length 6.8
+#> 145  virginica   Sepal  Length 6.7
+#> 146  virginica   Sepal  Length 6.7
+#> 147  virginica   Sepal  Length 6.3
+#> 148  virginica   Sepal  Length 6.5
+#> 149  virginica   Sepal  Length 6.2
+#> 150  virginica   Sepal  Length 5.9
+#> 151     setosa   Sepal   Width 3.5
+#> 152     setosa   Sepal   Width 3.0
+#> 153     setosa   Sepal   Width 3.2
+#> 154     setosa   Sepal   Width 3.1
+#> 155     setosa   Sepal   Width 3.6
+#> 156     setosa   Sepal   Width 3.9
+#> 157     setosa   Sepal   Width 3.4
+#> 158     setosa   Sepal   Width 3.4
+#> 159     setosa   Sepal   Width 2.9
+#> 160     setosa   Sepal   Width 3.1
+#> 161     setosa   Sepal   Width 3.7
+#> 162     setosa   Sepal   Width 3.4
+#> 163     setosa   Sepal   Width 3.0
+#> 164     setosa   Sepal   Width 3.0
+#> 165     setosa   Sepal   Width 4.0
+#> 166     setosa   Sepal   Width 4.4
+#> 167     setosa   Sepal   Width 3.9
+#> 168     setosa   Sepal   Width 3.5
+#> 169     setosa   Sepal   Width 3.8
+#> 170     setosa   Sepal   Width 3.8
+#> 171     setosa   Sepal   Width 3.4
+#> 172     setosa   Sepal   Width 3.7
+#> 173     setosa   Sepal   Width 3.6
+#> 174     setosa   Sepal   Width 3.3
+#> 175     setosa   Sepal   Width 3.4
+#> 176     setosa   Sepal   Width 3.0
+#> 177     setosa   Sepal   Width 3.4
+#> 178     setosa   Sepal   Width 3.5
+#> 179     setosa   Sepal   Width 3.4
+#> 180     setosa   Sepal   Width 3.2
+#> 181     setosa   Sepal   Width 3.1
+#> 182     setosa   Sepal   Width 3.4
+#> 183     setosa   Sepal   Width 4.1
+#> 184     setosa   Sepal   Width 4.2
+#> 185     setosa   Sepal   Width 3.1
+#> 186     setosa   Sepal   Width 3.2
+#> 187     setosa   Sepal   Width 3.5
+#> 188     setosa   Sepal   Width 3.6
+#> 189     setosa   Sepal   Width 3.0
+#> 190     setosa   Sepal   Width 3.4
+#> 191     setosa   Sepal   Width 3.5
+#> 192     setosa   Sepal   Width 2.3
+#> 193     setosa   Sepal   Width 3.2
+#> 194     setosa   Sepal   Width 3.5
+#> 195     setosa   Sepal   Width 3.8
+#> 196     setosa   Sepal   Width 3.0
+#> 197     setosa   Sepal   Width 3.8
+#> 198     setosa   Sepal   Width 3.2
+#> 199     setosa   Sepal   Width 3.7
+#> 200     setosa   Sepal   Width 3.3
+#> 201 versicolor   Sepal   Width 3.2
+#> 202 versicolor   Sepal   Width 3.2
+#> 203 versicolor   Sepal   Width 3.1
+#> 204 versicolor   Sepal   Width 2.3
+#> 205 versicolor   Sepal   Width 2.8
+#> 206 versicolor   Sepal   Width 2.8
+#> 207 versicolor   Sepal   Width 3.3
+#> 208 versicolor   Sepal   Width 2.4
+#> 209 versicolor   Sepal   Width 2.9
+#> 210 versicolor   Sepal   Width 2.7
+#> 211 versicolor   Sepal   Width 2.0
+#> 212 versicolor   Sepal   Width 3.0
+#> 213 versicolor   Sepal   Width 2.2
+#> 214 versicolor   Sepal   Width 2.9
+#> 215 versicolor   Sepal   Width 2.9
+#> 216 versicolor   Sepal   Width 3.1
+#> 217 versicolor   Sepal   Width 3.0
+#> 218 versicolor   Sepal   Width 2.7
+#> 219 versicolor   Sepal   Width 2.2
+#> 220 versicolor   Sepal   Width 2.5
+#> 221 versicolor   Sepal   Width 3.2
+#> 222 versicolor   Sepal   Width 2.8
+#> 223 versicolor   Sepal   Width 2.5
+#> 224 versicolor   Sepal   Width 2.8
+#> 225 versicolor   Sepal   Width 2.9
+#> 226 versicolor   Sepal   Width 3.0
+#> 227 versicolor   Sepal   Width 2.8
+#> 228 versicolor   Sepal   Width 3.0
+#> 229 versicolor   Sepal   Width 2.9
+#> 230 versicolor   Sepal   Width 2.6
+#> 231 versicolor   Sepal   Width 2.4
+#> 232 versicolor   Sepal   Width 2.4
+#> 233 versicolor   Sepal   Width 2.7
+#> 234 versicolor   Sepal   Width 2.7
+#> 235 versicolor   Sepal   Width 3.0
+#> 236 versicolor   Sepal   Width 3.4
+#> 237 versicolor   Sepal   Width 3.1
+#> 238 versicolor   Sepal   Width 2.3
+#> 239 versicolor   Sepal   Width 3.0
+#> 240 versicolor   Sepal   Width 2.5
+#> 241 versicolor   Sepal   Width 2.6
+#> 242 versicolor   Sepal   Width 3.0
+#> 243 versicolor   Sepal   Width 2.6
+#> 244 versicolor   Sepal   Width 2.3
+#> 245 versicolor   Sepal   Width 2.7
+#> 246 versicolor   Sepal   Width 3.0
+#> 247 versicolor   Sepal   Width 2.9
+#> 248 versicolor   Sepal   Width 2.9
+#> 249 versicolor   Sepal   Width 2.5
+#> 250 versicolor   Sepal   Width 2.8
+#>  [ reached 'max' / getOption("max.print") -- omitted 350 rows ]
    
+
    
   
   
 
diff --git a/man/check_design.Rd b/man/check_design.Rd
index f8bd5fad..140a0c8e 100644
--- a/man/check_design.Rd
+++ b/man/check_design.Rd
@@ -30,6 +30,6 @@ list
 
 within <- list(time = c("day", "night"))
 between <- list(pet = c("dog", "cat"))
-design <- check_design(within, between)
+check_design(within, between)
 
 }
diff --git a/man/check_sim_stats.Rd b/man/check_sim_stats.Rd
index 5c5e2e2a..88c46544 100644
--- a/man/check_sim_stats.Rd
+++ b/man/check_sim_stats.Rd
@@ -4,11 +4,11 @@
 \alias{check_sim_stats}
 \title{Check table stats}
 \usage{
-check_sim_stats(dat, between = c(), within = c(), dv = c(),
+check_sim_stats(.data, between = c(), within = c(), dv = c(),
   id = c(), digits = 2, usekable = FALSE)
 }
 \arguments{
-\item{dat}{the existing dataframe}
+\item{.data}{the existing tbl}
 
 \item{between}{a vector of column names for between-subject factors}
 
@@ -23,7 +23,7 @@ check_sim_stats(dat, between = c(), within = c(), dv = c(),
 \item{usekable}{logical. If TRUE, output with knitr::kable}
 }
 \value{
-tibble or kable
+a tbl or kable
 }
 \description{
 \code{check_sim_stats} Generates a table of the correlations and means of numeric columns in a data frame
diff --git a/man/get_design_long.Rd b/man/get_design_long.Rd
new file mode 100644
index 00000000..9560b4ae
--- /dev/null
+++ b/man/get_design_long.Rd
@@ -0,0 +1,24 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/check_design.R
+\name{get_design_long}
+\alias{get_design_long}
+\title{Get design from long data}
+\usage{
+get_design_long(.data, id = "sub_id", dv = "val")
+}
+\arguments{
+\item{.data}{the data frame (in long format)}
+
+\item{id}{the column name(s) that identify a unit of analysis}
+
+\item{dv}{the column name that identifies the DV}
+}
+\value{
+the data frame in long format
+}
+\description{
+Makes a best guess at the design of a long-format data frame. 
+Finds all columns that contain a single value per unit of analysis (between factors), 
+all columns that contain the same values per unit of analysis (within factors), and 
+all columns that differ over units of analysis (dv, continuous factors)
+}
diff --git a/man/long2wide.Rd b/man/long2wide.Rd
index a567b147..717b59a3 100644
--- a/man/long2wide.Rd
+++ b/man/long2wide.Rd
@@ -4,10 +4,11 @@
 \alias{long2wide}
 \title{Long to wide format}
 \usage{
-long2wide(dat, within = c(), between = c(), dv = c(), id = c())
+long2wide(.data, within = c(), between = c(), dv = "val",
+  id = "sub_id")
 }
 \arguments{
-\item{dat}{the long data frame to convert}
+\item{.data}{the tbl in long format}
 
 \item{within}{the names of the within column(s)}
 
@@ -18,8 +19,13 @@ long2wide(dat, within = c(), between = c(), dv = c(), id = c())
 \item{id}{the names of the column(s) for grouping observations}
 }
 \value{
-the data frame in wide format
+a tbl in wide format
 }
 \description{
 Converts data from long format to wide
 }
+\examples{
+df_long <- sim_design(2, 2, long = TRUE)
+long2wide(df_long, "A", "B", "val", "sub_id")
+
+}
diff --git a/man/make_id.Rd b/man/make_id.Rd
index 8678d9d6..55894a0e 100644
--- a/man/make_id.Rd
+++ b/man/make_id.Rd
@@ -4,12 +4,14 @@
 \alias{make_id}
 \title{Make ID}
 \usage{
-make_id(n = 100, prefix = "S")
+make_id(n = 100, prefix = "S", digits = 0)
 }
 \arguments{
-\item{n}{the number of IDs to generate}
+\item{n}{the number of IDs to generate (or a vector of numbers)}
 
 \item{prefix}{the letter prefix to the number}
+
+\item{digits}{the number of digits to use for the numeric part. Only used if this is larger than the number of digits in n.}
 }
 \value{
 a vector of IDs
@@ -20,5 +22,6 @@ Make IDs with fixed length and a letter prefix for random effects (e.g., S001, S
 \examples{
 
 make_id(20, "SUBJECT_")
+make_id(10:30, digits = 3)
 
 }
diff --git a/man/rnorm_multi.Rd b/man/rnorm_multi.Rd
index 63000d3e..b1a8ef80 100644
--- a/man/rnorm_multi.Rd
+++ b/man/rnorm_multi.Rd
@@ -27,12 +27,13 @@ rnorm_multi(n, vars = 3, mu = 0, sd = 1, r = 0, varnames = NULL,
 \item{cors}{(deprecated; use r)}
 }
 \value{
-dataframe of vars vectors
+a tbl of vars vectors
 }
 \description{
-\code{rnorm_multi} makes multiple normally distributed vectors with specified relationships
+\code{rnorm_multi()} makes multiple normally distributed vectors with specified relationships.
 }
 \examples{
-rnorm_multi(100, 3, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
-rnorm_multi(100, 3, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
+rnorm_multi(100, 3, 0, 1, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
+rnorm_multi(100, 3, 0, 1, c(1, 0.2, -0.5, 0.2, 1, 0.5, -0.5, 0.5, 1), varnames=c("A", "B", "C"))
+
 }
diff --git a/man/select_num_grp.Rd b/man/select_num_grp.Rd
index ee801214..3831209a 100644
--- a/man/select_num_grp.Rd
+++ b/man/select_num_grp.Rd
@@ -4,17 +4,17 @@
 \alias{select_num_grp}
 \title{Select grouping and numeric columns and group}
 \usage{
-select_num_grp(dat, between = c(), cols = NULL)
+select_num_grp(.data, between = c(), cols = NULL)
 }
 \arguments{
-\item{dat}{the existing dataframe}
+\item{.data}{the existing tbl}
 
 \item{between}{an optional list of column names to group by}
 
 \item{cols}{an optional list of column names to return (default of NULL returns all numeric columns)}
 }
 \value{
-tibble
+a tbl
 }
 \description{
 \code{select_num_grp} Select grouping and (optionally specified) numeric columns and group
diff --git a/man/sim_design.Rd b/man/sim_design.Rd
index 1127b4a9..4d88a826 100644
--- a/man/sim_design.Rd
+++ b/man/sim_design.Rd
@@ -5,7 +5,7 @@
 \title{Simulate Data from Design}
 \usage{
 sim_design(within = list(), between = list(), n = 100, mu = 0,
-  sd = 1, r = 0, empirical = FALSE, frame_long = FALSE)
+  sd = 1, r = 0, empirical = FALSE, long = FALSE)
 }
 \arguments{
 \item{within}{a list of the within-subject factors}
@@ -22,11 +22,11 @@ sim_design(within = list(), between = list(), n = 100, mu = 0,
 
 \item{empirical}{logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance}
 
-\item{frame_long}{Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format}
+\item{long}{Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format}
 }
 \value{
-dataframe
+a tbl
 }
 \description{
-\code{sim_design} generates a dataframe with a specified within and between design
+\code{sim_design()} generates a data table with a specified within and between design.
 }
diff --git a/man/sim_design_.Rd b/man/sim_design_.Rd
index 92b7ff67..b27f96ac 100644
--- a/man/sim_design_.Rd
+++ b/man/sim_design_.Rd
@@ -4,19 +4,19 @@
 \alias{sim_design_}
 \title{Simulate Data from Design}
 \usage{
-sim_design_(design, empirical = FALSE, frame_long = FALSE)
+sim_design_(design, empirical = FALSE, long = FALSE)
 }
 \arguments{
 \item{design}{A list of design parameters created by check_design()}
 
 \item{empirical}{logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance}
 
-\item{frame_long}{Whether the returned dataframe is in wide (default = FALSE) or long (TRUE) format}
+\item{long}{Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format}
 }
 \value{
-dataframe
+a tbl
 }
 \description{
-\code{sim_design_} generates a dataframe with a specified design
+\code{sim_design_} generates a data table with a specified design
 }
 \keyword{internal}
diff --git a/man/sim_df.Rd b/man/sim_df.Rd
index 6a2de59b..ef95ffc0 100644
--- a/man/sim_df.Rd
+++ b/man/sim_df.Rd
@@ -4,11 +4,11 @@
 \alias{sim_df}
 \title{Simulate an existing dataframe}
 \usage{
-sim_df(dat, n = 100, between = c(), empirical = FALSE,
+sim_df(.data, n = 100, between = c(), empirical = FALSE,
   grp_by = NULL)
 }
 \arguments{
-\item{dat}{the existing dataframe (must be in wide format)}
+\item{.data}{the existing tbl (must be in wide format)}
 
 \item{n}{the number of samples to return per group}
 
@@ -19,10 +19,10 @@ sim_df(dat, n = 100, between = c(), empirical = FALSE,
 \item{grp_by}{(deprecated; use between)}
 }
 \value{
-tibble
+a tbl
 }
 \description{
-\code{sim_df} Produces a dataframe with the same distributions and correlations as an existing dataframe. Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
+\code{sim_df} Produces a data table with the same distributions and correlations as an existing data table Only returns numeric columns and simulates all numeric variables from a continuous normal distribution (for now).
 }
 \examples{
 iris100 <- sim_df(iris, 100)
diff --git a/man/sim_mixed_df.Rd b/man/sim_mixed_df.Rd
index d63ba25b..6b4d73de 100644
--- a/man/sim_mixed_df.Rd
+++ b/man/sim_mixed_df.Rd
@@ -4,11 +4,11 @@
 \alias{sim_mixed_df}
 \title{Generate a sample with random intercepts for subjects and items}
 \usage{
-sim_mixed_df(dat, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
+sim_mixed_df(.data, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
   item_id = 3)
 }
 \arguments{
-\item{dat}{the existing dataframe}
+\item{.data}{the existing tbl}
 
 \item{sub_n}{the number of subjects to simulate}
 
@@ -21,10 +21,10 @@ sim_mixed_df(dat, sub_n = 100, item_n = 25, dv = 1, sub_id = 2,
 \item{item_id}{the column name or index for the item IDs}
 }
 \value{
-tibble
+a tbl
 }
 \description{
-\code{sim_mixed_df} Produces a dataframe with the same distributions of by-subject and by-item random intercepts as an existing dataframe
+\code{sim_mixed_df()} produces a data table with the same distributions of by-subject and by-item random intercepts as an existing data table.
 }
 \examples{
 \donttest{sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")}
diff --git a/man/wide2long.Rd b/man/wide2long.Rd
index e461d709..0574bb41 100644
--- a/man/wide2long.Rd
+++ b/man/wide2long.Rd
@@ -4,21 +4,25 @@
 \alias{wide2long}
 \title{Wide to long format}
 \usage{
-wide2long(dat, within_factors = c(), within_cols = c(),
+wide2long(.data, within_factors = c(), within_cols = c(),
   sep = "[^[:alnum:]]+")
 }
 \arguments{
-\item{dat}{the wide data frame to convert}
+\item{.data}{the tbl in wide format}
 
-\item{sep}{Separator between columns (see tidyr::separate)}
+\item{within_factors}{the names of the within factors}
 
-\item{within}{the names of the within factors}
+\item{within_cols}{the names (or indices) of the within-subject (value) columns}
 
-\item{dv}{the names of the DV (value) columns}
+\item{sep}{Separator for within-columns (see tidyr::separate)}
 }
 \value{
-the data frame in long format
+a tbl in long format
 }
 \description{
 Converts data from wide format to long
 }
+\examples{
+wide2long(iris, c("Feature", "Measure"), 1:4)
+
+}
diff --git a/tests/testthat/test-check_design.R b/tests/testthat/test-check_design.R
index 19389732..d0e7906c 100644
--- a/tests/testthat/test-check_design.R
+++ b/tests/testthat/test-check_design.R
@@ -1,5 +1,6 @@
 context("test-check_design")
 
+# basic ----
 test_that("basic", {
   within <- list(time = c("day", "night"))
   between <- list(pet = c("dog", "cat"))
@@ -13,20 +14,21 @@ test_that("basic", {
     magrittr::set_rownames(c("day", "night"))
     
   
-  testthat::expect_equal(design$within, within)
-  testthat::expect_equal(design$between, between)
-  testthat::expect_equal(design$within_labels, list(time = c(day = "day", night = "night")))
-  testthat::expect_equal(design$between_labels, list(pet = c(dog = "dog", cat = "cat")))
+  expect_equal(design$within, within)
+  expect_equal(design$between, between)
+  expect_equal(design$within_labels, list(time = c(day = "day", night = "night")))
+  expect_equal(design$between_labels, list(pet = c(dog = "dog", cat = "cat")))
   
-  testthat::expect_equal(design$cell_n, cell_n)
-  testthat::expect_equal(design$cell_mu, cell_mu)
-  testthat::expect_equal(design$cell_sd, cell_sd)
+  expect_equal(design$cell_n, cell_n)
+  expect_equal(design$cell_mu, cell_mu)
+  expect_equal(design$cell_sd, cell_sd)
   
-  testthat::expect_equal(design$cells_w, c("day", "night"))
-  testthat::expect_equal(design$cells_b, c("dog", "cat"))
-  testthat::expect_equal(design$sub_id, c(paste0("S0", 1:9), paste0("S", 10:20)))
+  expect_equal(design$cells_w, c("day", "night"))
+  expect_equal(design$cells_b, c("dog", "cat"))
+  expect_equal(design$sub_id, c(paste0("S0", 1:9), paste0("S", 10:20)))
 })
 
+# design spec ----
 test_that("design spec", {
   between <- list(
     "B" = c("B1", "B2")
@@ -54,12 +56,13 @@ test_that("design spec", {
   design <- check_design(within, between, n, mu, sd, r)
   
   design_elements <- c("within", "between", "within_factors", "between_factors", 
-                       "within_labels", "between_labels", "cell_n", "cell_mu", "cell_sd",
-                       "cell_r", "cells_w", "cells_b", "sub_id")
+                       "within_labels", "between_labels", "cells_w", "cells_b", 
+                       "cell_n", "cell_mu", "cell_sd", "cell_r", "sub_id")
   
   expect_equal(names(design), design_elements)
 })
 
+# anon factors ----
 test_that("anon factors", {
   design <- check_design(c(2, 4), c(2, 2))
   
@@ -77,6 +80,7 @@ test_that("anon factors", {
   expect_equal(design$between, b)
 })
 
+# make_id ----
 test_that("make_id", {
   expect_equal(make_id(10), c("S01", "S02", "S03", "S04", "S05", 
                               "S06", "S07", "S08", "S09", "S10"))
@@ -87,4 +91,13 @@ test_that("make_id", {
   expect_equal(make_id(100)[[1]], "S001")
   expect_equal(make_id(1000)[[1]], "S0001")
   expect_equal(make_id(1000, "pokemon_")[[1]], "pokemon_0001")
+  expect_equal(make_id(100, digits = 4)[[1]], "S0001")
+  
+  # named arguments
+  expect_equal(make_id(n = 100, prefix = "A", digits = 4)[[1]], "A0001")
+  expect_equal(make_id(digits = 4, prefix = "A", n = 100)[[1]], "A0001")
+  
+  # vector
+  expect_equal(make_id(2:4), c("S2", "S3", "S4"))
+  expect_equal(make_id(100:200)[[1]], "S100")
 })
diff --git a/tests/testthat/test-check_sim_stats.R b/tests/testthat/test-check_sim_stats.R
index d511c721..bd48d53b 100644
--- a/tests/testthat/test-check_sim_stats.R
+++ b/tests/testthat/test-check_sim_stats.R
@@ -1,11 +1,13 @@
 context("check_sim_stats")
 
+# error messages ----
 test_that("error messages", {
-  expect_error(check_sim_stats("A"), "dat must be a data frame or matrix")
+  expect_error(check_sim_stats("A"), ".data must be a data frame or matrix")
   expect_error(check_sim_stats(iris, FALSE), "between must be a numeric or character vector")
 })
 
-test_that("correct defaults", {
+# defaults ----
+test_that("defaults", {
   checkiris <- check_sim_stats(iris)
   irisnames <- c("n", "var", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "mean", "sd")
   
@@ -14,7 +16,8 @@ test_that("correct defaults", {
   expect_equal(names(checkiris), irisnames)
 })
 
-test_that("correct defaults with group", {
+# defaults with between ----
+test_that("defaults with between", {
   checkiris <- check_sim_stats(iris, "Species")
   irisnames <- c("Species", "n", "var", "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "mean", "sd")
   
@@ -23,6 +26,7 @@ test_that("correct defaults with group", {
   expect_equal(names(checkiris), irisnames)
 })
 
+# is_pos_def ----
 test_that("is_pos_def", {
   expect_equal(is_pos_def(matrix(c(1, .5, .5, 1), 2)), TRUE)
   
@@ -33,29 +37,33 @@ test_that("is_pos_def", {
 })
 
 
-test_that("long", {
-  df_long <- sim_design(2, 2, frame_long = TRUE)
-  df_wide <- long2wide(df_long, "A", "B", "val", "sub_id")
+# get_design_long ----
+test_that("get_design_long", {
+  df_long <- sim_design(2, 2, n = 10, mu = 5, sd = 2, r = 0.5, long = TRUE, empirical = TRUE)
+  d <- get_design_long(df_long)
   
-  expect_equal(names(df_wide), c("sub_id", "B", "A1", "A2"))
-  expect_equal(nrow(df_wide), 200)
+  n <- data.frame(A1 = c(10, 10), A2 = c(10, 10), row.names = c("B1", "B2"))
+  mu <- data.frame(A1 = c(5, 5), A2 = c(5, 5), row.names = c("B1", "B2"))
+  sd <- data.frame(A1 = c(2, 2), A2 = c(2, 2), row.names = c("B1", "B2"))
+  r <- data.frame(A1 = c(1, .5), A2 = c(.5, 1), row.names = c("A1", "A2"))
   
-  iris_long <- iris %>%
-    dplyr::mutate(id = make_id(nrow(.), "I")) %>%
-    tidyr::gather(var, val, Sepal.Length:Petal.Width) %>%
-    tidyr::separate(var, c("Feature", "Measure"))
-
-  iris_wide <- long2wide(iris_long, within = c("Feature", "Measure"), 
-                         between = "Species", dv = "val", id = "id")
-  inames <- c("id", "Species", "Petal_Length", "Petal_Width", "Sepal_Length", "Sepal_Width")
-  testthat::expect_equal(names(iris_wide), inames)
-  testthat::expect_equal(nrow(iris_wide), 150)
-  
-  long <- check_sim_stats(iris_long, within = c("Feature", "Measure"), 
-                          between = "Species", dv = "val", id = "id")
-  
-  wide <- check_sim_stats(iris, between = "Species")
+  expect_equal(d$within, list(A = c("A1", "A2")))
+  expect_equal(d$between, list(B = c("B1", "B2")))
+  expect_equal(d$within_factors, "A")
+  expect_equal(d$between_factors, "B")
+  expect_equal(d$cell_n, n)
+  expect_equal(d$cell_mu, mu)
+  expect_equal(d$cell_sd, sd)
+  expect_equal(d$cell_r$B1, r)
+  expect_equal(d$cell_r$B2, r)
   
+  df_long <- sim_design(c(2, 2, 2), c(2, 2, 2), long = TRUE, empirical = TRUE)
+  d <- get_design_long(df_long)
   
-  testthat::expect_equal(nrow(long), nrow(wide))
+  expect_equal(d$within_factors, c("A", "B", "C"))
+  expect_equal(d$between_factors, c("D", "E", "F"))
+  expect_equal(d$cell_n  %>% sum(), 6400)
+  expect_equal(d$cell_mu %>% sum(), 0)
+  expect_equal(d$cell_sd %>% sum(), 64)
+  expect_equal(d$cell_r[[1]] %>% sum(), 8)
 })
diff --git a/tests/testthat/test-long2wide.R b/tests/testthat/test-long2wide.R
new file mode 100644
index 00000000..9932d1f5
--- /dev/null
+++ b/tests/testthat/test-long2wide.R
@@ -0,0 +1,51 @@
+context("long2wide")
+
+# 2w*2b ----
+test_that("2w*2b", {
+  df_long <- sim_design(2, 2, long = TRUE)
+  df_wide <- long2wide(df_long, "A", "B", "val", "sub_id")
+  
+  expect_equal(names(df_wide), c("sub_id", "B", "A1", "A2"))
+  expect_equal(nrow(df_wide), 200)
+})
+
+# named arguments ----
+test_that("named arguments", {
+  df_long <- sim_design(2, 2, long = TRUE)
+  df_wide <- long2wide(dv = "val", id = "sub_id", .data = df_long, between = "B", within = "A")
+  
+  expect_equal(names(df_wide), c("sub_id", "B", "A1", "A2"))
+  expect_equal(nrow(df_wide), 200)
+})
+
+# 2w*2w*2b*2b ----
+test_that("2w*2w*2b*2b", {
+  df_long <- sim_design(c(2, 2), c(2, 2), long = TRUE)
+  df_wide <- long2wide(df_long, c("A", "B"), c("C","D"), "val", "sub_id")
+  
+  expect_equal(names(df_wide), c("sub_id", "C", "D", "A1_B1", "A1_B2", "A2_B1", "A2_B2"))
+  expect_equal(nrow(df_wide), 400)
+})
+
+
+# iris ----
+test_that("iris", {
+  iris_long <- iris %>%
+    dplyr::mutate(id = make_id(nrow(.), "I")) %>%
+    tidyr::gather(var, val, Sepal.Length:Petal.Width) %>%
+    tidyr::separate(var, c("Feature", "Measure"))
+  
+  iris_wide <- long2wide(iris_long, within = c("Feature", "Measure"), 
+                         between = "Species", dv = "val", id = "id")
+  inames <- c("id", "Species", "Petal_Length", "Petal_Width", "Sepal_Length", "Sepal_Width")
+  expect_equal(names(iris_wide), inames)
+  expect_equal(nrow(iris_wide), 150)
+  
+  long <- check_sim_stats(iris_long, within = c("Feature", "Measure"), 
+                          between = "Species", dv = "val", id = "id")
+  
+  wide <- check_sim_stats(iris, between = "Species")
+  
+  
+  expect_equal(nrow(long), nrow(wide))
+})
diff --git a/tests/testthat/test-rnorm_multi.R b/tests/testthat/test-rnorm_multi.R
index 774dee07..d56d4122 100644
--- a/tests/testthat/test-rnorm_multi.R
+++ b/tests/testthat/test-rnorm_multi.R
@@ -56,7 +56,7 @@ test_that("correct default parameters", {
   dat <- rnorm_multi(n)
   r <- cor(dat)
   means <- dplyr::summarise_all(dat, mean)
-  sds <- dplyr::summarise_all(dat, sd)
+  sds <- dplyr::summarise_all(dat, stats::sd)
   
   expect_equal(nrow(dat), n)
   expect_equal(ncol(dat), 3)
@@ -69,7 +69,7 @@ test_that("correct default parameters with empirical = TRUE", {
   dat <- rnorm_multi(n, empirical = TRUE)
   r <- cor(dat)
   means <- dplyr::summarise_all(dat, mean)
-  sds <- dplyr::summarise_all(dat, sd)
+  sds <- dplyr::summarise_all(dat, stats::sd)
   
   expect_equal(nrow(dat), n)
   expect_equal(ncol(dat), 3)
diff --git a/tests/testthat/test-select_num_grp.R b/tests/testthat/test-select_num_grp.R
index 53fe24a8..a8c1a7af 100644
--- a/tests/testthat/test-select_num_grp.R
+++ b/tests/testthat/test-select_num_grp.R
@@ -1,7 +1,7 @@
 context("select_num_grp")
 
 test_that("error messages", {
-  expect_error(select_num_grp("A"), "dat must be a data frame or matrix")
+  expect_error(select_num_grp("A"), ".data must be a data frame or matrix")
   expect_error(select_num_grp(iris, FALSE), "between must be a numeric or character vector")
 })
 
diff --git a/tests/testthat/test-sim_df.R b/tests/testthat/test-sim_df.R
index 51559cbe..6bf668e8 100644
--- a/tests/testthat/test-sim_df.R
+++ b/tests/testthat/test-sim_df.R
@@ -1,13 +1,15 @@
 context("sim_df")
 
+# error messages ----
 test_that("error messages", {
-  expect_error( sim_df("A"), "dat must be a data frame or matrix" )
+  expect_error( sim_df("A"), ".data must be a data frame or matrix" )
   expect_error( sim_df(iris, "A"), "n must be an integer > 2" )
   expect_error( sim_df(iris, 2), "n must be an integer > 2" )
   expect_error( sim_df(iris, 10, FALSE), "between must be a numeric or character vector" )
 })
 
-test_that("correct default parameters", {
+# default parameters ----
+test_that("default parameters", {
   newdf <- sim_df(iris)
 
   expect_equal(nrow(newdf), 100)
@@ -15,7 +17,8 @@ test_that("correct default parameters", {
   expect_equal(names(newdf), names(iris)[1:4])
 })
 
-test_that("correct specified parameters", {
+# specified parameters ----
+test_that("specified parameters", {
   n <- 100
   dat <- tibble::as_tibble(iris) %>%
     dplyr::select_if(is.numeric)
@@ -25,6 +28,7 @@ test_that("correct specified parameters", {
   sds <- dplyr::summarise_all(dat, sd) %>%
     as.data.frame()
   
+  # unnamed arguments in order
   newdf <- sim_df(iris, n, c(), TRUE)
   newdat <- dplyr::select_if(newdf, is.numeric)
   newcors <- cor(newdat)
@@ -40,9 +44,28 @@ test_that("correct specified parameters", {
   expect_equal(cors, newcors)
   expect_equal(means, newmeans)
   expect_equal(sds, newsds)
+  
+  # named arguments out of order
+  newdf <- sim_df(between = c(), empirical = TRUE, .data = iris, n = n)
+  newdat <- dplyr::select_if(newdf, is.numeric)
+  newcors <- cor(newdat)
+  newmeans <- dplyr::summarise_all(newdat, mean) %>%
+    as.data.frame()
+  newsds <- dplyr::summarise_all(newdat, sd) %>%
+    as.data.frame()
+  
+  expect_equal(nrow(newdf), n)
+  expect_equal(ncol(newdf), 4)
+  expect_equal(names(newdf), names(iris)[1:4])
+  
+  expect_equal(cors, newcors)
+  expect_equal(means, newmeans)
+  expect_equal(sds, newsds)
+  
 })
 
-test_that("grouping by col name", {
+# grouping by name ----
+test_that("grouping by name", {
   newdf <- sim_df(iris, 20, "Species")
   
   expect_equal(nrow(newdf), 60)
@@ -50,6 +73,7 @@ test_that("grouping by col name", {
   expect_equal(names(newdf) %>% sort(), names(iris) %>% sort())
 })
 
+# grouping by col number ----
 test_that("grouping by col number", {
   newdf <- sim_df(iris, 20, 5)
   
diff --git a/vignettes/sim_design.Rmd b/vignettes/sim_design.Rmd
index 6b0c02ab..77261c76 100644
--- a/vignettes/sim_design.Rmd
+++ b/vignettes/sim_design.Rmd
@@ -208,12 +208,12 @@ r <- list(
 )
 ```
 
-You can set `frame_long = TRUE` to return the data frame in long format, which is usually easier for plotting. 
+You can set `long = TRUE` to return the data frame in long format, which is usually easier for plotting. 
 
 ```{r}
 df <- sim_design(within, between, n = 100, 
                  mu = mu, sd = 2, r = r, 
-                 frame_long = TRUE)
+                 long = TRUE)
 ```