diff --git a/DESCRIPTION b/DESCRIPTION index 86fa2b15..11780d61 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,7 +1,7 @@ Package: faux Title: Simulation Functions -Version: 0.0.0.9006 -Date: 2019-05-02 +Version: 0.0.0.9007 +Date: 2019-05-06 Authors@R: person(given = "Lisa", family = "DeBruine", role = c("aut", "cre"), email = "debruine@gmail.com", comment = c(ORCID = "0000-0002-7523-5539")) Description: Provides functions for simulating multiple variables with specified relationships. Depends: @@ -16,12 +16,14 @@ Imports: tibble, tidyselect, stats, - ggplot2 + ggplot2, + rlang License: MIT + file LICENSE Suggests: testthat (>= 2.1.0), knitr, - rmarkdown + rmarkdown, + forcats VignetteBuilder: knitr RoxygenNote: 6.1.1 Encoding: UTF-8 diff --git a/NAMESPACE b/NAMESPACE index 2bada45d..fa88da6e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -9,6 +9,7 @@ export(get_design_long) export(is_pos_def) export(long2wide) export(make_id) +export(plot_design) export(pos_def_limits) export(rnorm_multi) export(rnorm_pre) diff --git a/NEWS.md b/NEWS.md index 940dca9c..7633459d 100644 --- a/NEWS.md +++ b/NEWS.md @@ -10,4 +10,11 @@ # faux 0.0.0.9006 * Changes to argument order and names (more consistent, but may break old scripts) -* Updated vignettes \ No newline at end of file +* Updated vignettes + +# faux 0.0.0.9007 + +* Added a plot option to `check_design()` and `sim_design()` +* Design lists returned by `check_design()` have a more consistent format + - n, mu, and sd are all data frames with between-cells as rows and within-cells as columns + - `within` and `between` are named lists; factors and labels are no longer separately named \ No newline at end of file diff --git a/R/check_design.R b/R/check_design.R index 33db726b..3631d1c4 100644 --- a/R/check_design.R +++ b/R/check_design.R @@ -8,6 +8,7 @@ #' @param mu a vector giving the means of the variables (numeric vector of length 1 or vars) #' @param sd the standard deviations of the variables (numeric vector of length 1 or vars) #' @param r the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector) +#' @param plot whether to show a plot of the design #' #' @return list #' @@ -20,7 +21,7 @@ #' @export #' check_design <- function(within = list(), between = list(), - n = 100, mu = 0, sd = 1, r = 0) { + n = 100, mu = 0, sd = 1, r = 0, plot = TRUE) { # name anonymous factors if (is.numeric(within) && within %in% 2:10 %>% mean() == 1) { # vector of level numbers within_names <- LETTERS[1:length(within)] @@ -42,20 +43,11 @@ check_design <- function(within = list(), between = list(), # if within or between factors are named vectors, # use their names as column names and values as labels for plots - between_labels <- purrr::map(between, fix_name_labels) - between <- lapply(between_labels, names) - within_labels <- purrr::map(within, fix_name_labels) - within <- lapply(within_labels, names) - - within_factors <- names(within) - between_factors <- names(between) - - # handle no w/in or btwn - if (length(between_factors) == 0) between_factors <- ".tmpvar." - if (length(within_factors) == 0) within_factors <- ".tmpvar." + between <- purrr::map(between, fix_name_labels) + within <- purrr::map(within, fix_name_labels) # check for duplicate factor names - factor_overlap <- intersect(within_factors, between_factors) + factor_overlap <- intersect(names(within), names(between)) if (length(factor_overlap)) { stop("You have multiple factors with the same name (", paste(factor_overlap, collapse = ", "), @@ -78,52 +70,61 @@ check_design <- function(within = list(), between = list(), } # define columns - if (length(within) == 0) { - cells_w = "val" - } else { - cells_w <- do.call(expand.grid, within) %>% - tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b") - } - if (length(between) == 0) { - cells_b = ".tmpvar." - } else { - cells_b <- do.call(expand.grid, between) %>% - tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b") - } + cells_w <- cell_combos(within) + cells_b <- cell_combos(between) # convert n, mu and sd from vector/list formats cell_n <- convert_param(n, cells_w, cells_b, "Ns") cell_mu <- convert_param(mu, cells_w, cells_b, "means") cell_sd <- convert_param(sd, cells_w, cells_b, "SDs") - # figure out number of subjects and their IDs - sub_n <- sum(cell_n[1,]) - sub_id <- make_id(sub_n) - # set up cell correlations from r (number, vector, matrix or list styles) cell_r <- list() if (length(within)) { for (cell in cells_b) { cell_cor <- if(is.list(r)) r[[cell]] else r - cell_r[[cell]] <- cormat(cell_cor, length(cells_w)) + mat <- cormat(cell_cor, length(cells_w)) + rownames(mat) <- cells_w + colnames(mat) <- cells_w + cell_r[[cell]] <- mat } } - list( + design <- list( within = within, between = between, - within_factors = within_factors, - between_factors = between_factors, - within_labels = within_labels, - between_labels = between_labels, cells_w = cells_w, cells_b = cells_b, cell_n = cell_n, cell_mu = cell_mu, cell_sd = cell_sd, - cell_r = cell_r, - sub_id = sub_id + cell_r = cell_r ) + + if (plot) { plot_design(design) %>% print() } + + invisible(design) +} + +#' Cell combos +#' +#' Creates wide cell combination names, such as A1_B1, A2_B1, A1_B2, A2_B2. +#' +#' @param factors A list of factors +#' +#' @return a list +#' @keywords internal +#' +cell_combos <- function(factors) { + if (length(factors) == 0) { + cells = "val" + } else { + cells <- lapply(factors, names) %>% + do.call(expand.grid, .) %>% + tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b") + } + + cells } #' Convert parameter @@ -203,121 +204,9 @@ convert_param <- function (param, cells_w, cells_b, type = "this parameter") { } } - dd <- matrix(param2, ncol = b_n) %>% as.data.frame() - names(dd) <- cells_b + dd <- matrix(param2, ncol = b_n) + colnames(dd) <- cells_b rownames(dd) <- cells_w - dd -} - - -#' Get design from long data -#' -#' 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) -#' -#' @param .data the data frame (in long format) -#' @param id the column name(s) that identify a unit of analysis -#' @param dv the column name that identifies the DV -#' -#' @return the data frame in long format -#' -#' @export -#' -get_design_long <- function(.data, id = "sub_id", dv = "val") { - between_factors <- .data %>% - dplyr::group_by_at(dplyr::vars(tidyselect::one_of(id))) %>% - dplyr::summarise_all(dplyr::n_distinct) %>% - dplyr::ungroup() %>% - dplyr::select(-tidyselect::one_of(id)) %>% - dplyr::summarise_all(max) %>% - dplyr::select_if(~ . == 1) %>% - names() - - within_factors <- .data %>% - dplyr::select(-tidyselect::one_of(between_factors)) %>% - dplyr::group_by_at(dplyr::vars(tidyselect::one_of(id))) %>% - dplyr::summarise_all(paste0, collapse = ",") %>% - dplyr::ungroup() %>% - dplyr::select(-tidyselect::one_of(id)) %>% - dplyr::summarise_all(dplyr::n_distinct) %>% - dplyr::select_if(~ . == 1) %>% - names() - - within <- .data %>% - dplyr::select(tidyselect::one_of(within_factors)) %>% - dplyr::mutate_all(as.factor) %>% - dplyr::summarise_all(~levels(.) %>% paste0(collapse = ".|.")) %>% - as.list() %>% - sapply(strsplit, split=".|.", fixed = TRUE) - - between <- .data %>% - dplyr::select(tidyselect::one_of(between_factors)) %>% - dplyr::mutate_all(as.factor) %>% - dplyr::summarise_all(~levels(.) %>% paste0(collapse = ".|.")) %>% - as.list() %>% - sapply(strsplit, split=".|.", fixed = TRUE) - - between_labels <- purrr::map(between, fix_name_labels) - within_labels <- purrr::map(within, fix_name_labels) - - cells_b <- do.call(expand.grid, between) %>% - tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b") - - cells_w <- do.call(expand.grid, within) %>% - tidyr::unite("b", 1:ncol(.)) %>% dplyr::pull("b") - - # get n, mu, sd, r per cell - chk <- check_sim_stats(.data, between_factors, within_factors, dv, id) - - n <- chk %>% - tidyr::unite(".within", tidyselect::one_of(between_factors)) %>% - dplyr::select(.within, var, n) %>% - tidyr::spread(var, n) %>% - tibble::column_to_rownames(".within") - - mu <- chk %>% - tidyr::unite(".within", tidyselect::one_of(between_factors)) %>% - dplyr::select(.within, var, mean) %>% - tidyr::spread(var, mean) %>% - tibble::column_to_rownames(".within") - - sd <- chk %>% - tidyr::unite(".within", tidyselect::one_of(between_factors)) %>% - dplyr::select(.within, var, sd) %>% - tidyr::spread(var, sd) %>% - tibble::column_to_rownames(".within") - - cors <- chk %>% - tidyr::unite(".between", tidyselect::one_of(between_factors)) %>% - dplyr::select(tidyselect::one_of(c(".between", "var", cells_w))) %>% - dplyr::mutate(var = forcats::fct_relevel(var, cells_w)) %>% - dplyr::arrange(var) %>% - dplyr::group_by(.between) %>% - tidyr::nest(.key = "r") %>% - as.list() - - r <- purrr::map(cors$r, ~tibble::column_to_rownames(., "var")) - names(r) <- cors$.between - - design <- list( - within = within, - between = between, - within_factors = within_factors, - between_factors = between_factors, - within_labels = within_labels, - between_labels = between_labels, - cells_w = cells_w, - cells_b = cells_b, - cell_n = n, - cell_mu = mu, - cell_sd = sd, - cell_r = r, - sub_id = id - ) - - design + t(dd) %>% as.data.frame() } - diff --git a/R/get_design_long.R b/R/get_design_long.R new file mode 100644 index 00000000..4666b49a --- /dev/null +++ b/R/get_design_long.R @@ -0,0 +1,112 @@ +#' Get design from long data +#' +#' 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) +#' +#' @param .data the data frame (in long format) +#' @param id the column name(s) that identify a unit of analysis +#' @param dv the column name that identifies the DV +#' +#' @return the data frame in long format +#' +#' @export +#' +get_design_long <- function(.data, id = "sub_id", dv = "val") { + between_factors <- .data %>% + dplyr::group_by_at(dplyr::vars(tidyselect::one_of(id))) %>% + dplyr::summarise_all(dplyr::n_distinct) %>% + dplyr::ungroup() %>% + dplyr::select(-tidyselect::one_of(id)) %>% + dplyr::summarise_all(max) %>% + dplyr::select_if(~ . == 1) %>% + names() + + within_factors <- .data %>% + dplyr::select(-tidyselect::one_of(between_factors)) %>% + dplyr::group_by_at(dplyr::vars(tidyselect::one_of(id))) %>% + dplyr::summarise_all(paste0, collapse = ",") %>% + dplyr::ungroup() %>% + dplyr::select(-tidyselect::one_of(id)) %>% + dplyr::summarise_all(dplyr::n_distinct) %>% + dplyr::select_if(~ . == 1) %>% + names() + + within <- .data %>% + dplyr::select(tidyselect::one_of(within_factors)) %>% + dplyr::mutate_all(as.factor) %>% + dplyr::summarise_all(~levels(.) %>% paste0(collapse = ".|.")) %>% + as.list() %>% + sapply(strsplit, split=".|.", fixed = TRUE) %>% + purrr::map(fix_name_labels) + + between <- .data %>% + dplyr::select(tidyselect::one_of(between_factors)) %>% + dplyr::mutate_all(as.factor) %>% + dplyr::summarise_all(~levels(.) %>% paste0(collapse = ".|.")) %>% + as.list() %>% + sapply(strsplit, split=".|.", fixed = TRUE) %>% + purrr::map(fix_name_labels) + + # define columns + cells_w <- cell_combos(within) + cells_b <- cell_combos(between) + + # get n, mu, sd, r per cell + chk <- check_sim_stats(.data, between_factors, within_factors, dv, id) + + if (length(between_factors)) { + chk_b <- tidyr::unite(chk, ".between", tidyselect::one_of(between_factors)) %>% + dplyr::mutate(.between = forcats::fct_relevel(.between, cells_b)) %>% + dplyr::arrange(.between) + } else { + chk_b <- dplyr::mutate(chk, ".between" = dv) + } + + n <- chk_b %>% + dplyr::select(.between, var, n) %>% + tidyr::spread(var, n) %>% + dplyr::select(tidyselect::one_of(c(".between", cells_w))) %>% + tibble::column_to_rownames(".between") %>% + as.data.frame() + + mu <- chk_b %>% + dplyr::select(.between, var, mean) %>% + tidyr::spread(var, mean) %>% + dplyr::select(tidyselect::one_of(c(".between", cells_w))) %>% + tibble::column_to_rownames(".between") %>% + as.data.frame() + + sd <- chk_b %>% + dplyr::select(.between, var, sd) %>% + tidyr::spread(var, sd) %>% + dplyr::select(tidyselect::one_of(c(".between", cells_w))) %>% + tibble::column_to_rownames(".between") %>% + as.data.frame() + + cors <- chk_b %>% + dplyr::select(tidyselect::one_of(c(".between", "var", cells_w))) %>% + dplyr::mutate(var = forcats::fct_relevel(var, cells_w)) %>% + dplyr::arrange(var) %>% + dplyr::group_by(.between) %>% + tidyr::nest(.key = "r") %>% + as.list() + + r <- purrr::map(cors$r, ~tibble::column_to_rownames(., "var") %>% as.matrix()) + names(r) <- cors$.between + + design <- list( + within = within, + between = between, + cells_w = cells_w, + cells_b = cells_b, + cell_n = n, + cell_mu = mu, + cell_sd = sd, + cell_r = r + ) + + design +} + diff --git a/R/plot_design.R b/R/plot_design.R new file mode 100644 index 00000000..9860613a --- /dev/null +++ b/R/plot_design.R @@ -0,0 +1,66 @@ +#' Plot design +#' +#' \code{plot_design()} plots the specified within and between design +#' +#' @param design A list of design parameters created by check_design() or a data tbl (in long format) +#' @param id the column name(s) that identify a unit of analysis +#' @param dv the column name that identifies the DV +#' +#' @return plot +#' +#' @examples +#' +#' within <- list(time = c("day", "night")) +#' between <- list(pet = c("dog", "cat")) +#' check_design(within, between) %>% plot_design() +#' +#' @export +#' +plot_design <- function(design, id = "sub_id", dv = "val") { + if (!is.data.frame(design) && is.list(design)) { + data <- sim_design_(design, empirical = TRUE, long = TRUE) + } else if (is.data.frame(design)) { + data <- design + design <- get_design_long(data, id = id, dv = dv) + } else { + stop("design must be a design list or a data frame") + } + + factors <- c(names(design$within), names(design$between)) + factor_n <- length(factors) + f <- dplyr::syms(factors) # make it possible to use strings to specify columns + + if (factor_n == 1) { + p <- ggplot2::ggplot(data, ggplot2::aes(!!f[[1]], val)) + } else if (factor_n == 2) { + p <- ggplot2::ggplot(data, ggplot2::aes(!!f[[1]], val, color = !!f[[2]])) + } else if (factor_n == 3) { + p <- ggplot2::ggplot(data, ggplot2::aes(!!f[[1]], val, color = !!f[[2]])) + + ggplot2::facet_grid( + eval(rlang::expr(!!f[[3]] ~ .)), + labeller = "label_both" + ) + } else { + p <- ggplot2::ggplot(data, ggplot2::aes(!!f[[1]], val, color = !!f[[2]])) + + ggplot2::facet_grid( + eval(rlang::expr(!!f[[3]] ~ !!f[[4]])), + labeller = "label_both" + ) + + if (factor_n > 4) message("Can't plot more than 4 factors") + } + + minsd <- function(x) { mean(x) - sd(x) } + maxsd <- function(x) { mean(x) + sd(x) } + + p <- p + ggplot2::stat_summary(fun.y = mean, + fun.ymin = minsd, + fun.ymax = maxsd, + geom='pointrange', + shape = 10, + size = 1, + position = ggplot2::position_dodge(width = 0.9)) + p <- p + ggplot2::theme_bw() + + p +} diff --git a/R/rnorm_multi.R b/R/rnorm_multi.R index 6e44c8ce..6e04e753 100644 --- a/R/rnorm_multi.R +++ b/R/rnorm_multi.R @@ -43,12 +43,18 @@ rnorm_multi <- function(n, vars = 3, mu = 0, sd = 1, r = 0, mu <- rep(mu, vars) } else if (length(mu) != vars) { stop("the length of mu must be 1 or vars"); + } else { + # get rid of names + mu <- as.matrix(mu) %>% as.vector() } if (length(sd) == 1) { sd <- rep(sd, vars) } else if (length(sd) != vars) { stop("the length of sd must be 1 or vars"); + } else { + # get rid of names + sd <- as.matrix(sd) %>% as.vector() } cor_mat <- cormat(r, vars) diff --git a/R/sim_design.R b/R/sim_design.R index 103d530a..0c7ac012 100644 --- a/R/sim_design.R +++ b/R/sim_design.R @@ -10,6 +10,7 @@ #' @param r the correlations among the variables (can be a single number, vars\*vars matrix, vars\*vars vector, or a vars\*(vars-1)/2 vector) #' @param empirical logical. If true, mu, sd and r specify the empirical not population mean, sd and covariance #' @param long Whether the returned tbl is in wide (default = FALSE) or long (TRUE) format +#' @param plot whether to show a plot of the design #' #' @return a tbl #' @@ -17,10 +18,10 @@ #' sim_design <- function(within = list(), between = list(), n = 100, mu = 0, sd = 1, r = 0, - empirical = FALSE, long = FALSE) { + empirical = FALSE, long = FALSE, plot = FALSE) { # check the design is specified correctly design <- check_design(within = within, between = between, - n = n, mu = mu, sd = sd, r = r) + n = n, mu = mu, sd = sd, r = r, plot = plot) # simulate the data sim_design_(design, empirical = empirical, long = long) @@ -57,20 +58,33 @@ fix_name_labels <- function(x) { sim_design_ <- function(design, empirical = FALSE, long = FALSE) { list2env(design, envir = environment()) + # get factor names + within_factors <- names(within) + between_factors <- names(between) + + # handle no w/in or btwn + if (length(between_factors) == 0) between_factors <- ".tmpvar." + if (length(within_factors) == 0) within_factors <- ".tmpvar." + + # figure out number of subjects and their IDs + sub_n <- sum(cell_n[,1]) + sub_id <- make_id(sub_n) + # simulate data for each between-cell for (cell in cells_b) { if (length(within)) { cell_vars <- rnorm_multi( - cell_n[1,cell], length(cells_w), - cell_mu[[cell]], cell_sd[[cell]], cell_r[[cell]], - cells_w, empirical + n = cell_n[cell,1], vars = length(cells_w), + mu = cell_mu[cell,], sd = cell_sd[cell,], r = cell_r[[cell]], + varnames = cells_w, empirical = empirical ) %>% dplyr::mutate("btwn" = cell) } else { # fully between design - val <- MASS::mvrnorm(n = cell_n[1,cell], - mu = cell_mu[[cell]], - Sigma = cell_sd[[cell]] %*% t(cell_sd[[cell]]), + sd2 <- cell_sd[cell,] %>% as.matrix() %>% as.vector() + val <- MASS::mvrnorm(n = cell_n[cell,1], + mu = cell_mu[cell,] %>% as.matrix() %>% as.vector(), + Sigma = sd2 %*% t(sd2), empirical = empirical) cell_vars <- data.frame("val" = val) %>% dplyr::mutate("btwn" = cell) @@ -95,10 +109,11 @@ sim_design_ <- function(design, empirical = FALSE, long = FALSE) { dplyr::mutate_at(c(between_factors), ~as.factor(.)) %>% dplyr::select(tidyselect::one_of(col_order)) - # FIX: put factors in order - #for (f in between_factors) { - # df_wide[[f]] <- factor(df_wide[[f]], levels = between[[f]]) - #} + # put factors in order + factors_to_order <- setdiff(between_factors, ".tmpvar.") + for (f in factors_to_order) { + df_wide[[f]] <- factor(df_wide[[f]], levels = names(between[[f]])) + } if (long == TRUE && length(within)) { # not necessary for fully between designs @@ -111,10 +126,11 @@ sim_design_ <- function(design, empirical = FALSE, long = FALSE) { dplyr::select(tidyselect::one_of(col_order)) %>% dplyr::mutate_at(within_factors, ~as.factor(.)) - #FIX: put factors in order - #for (f in within_factors) { - # df_long[[f]] <- factor(df_long[[f]], levels = within[[f]]) - #} + # put factors in order + factors_to_order <- setdiff(within_factors, ".tmpvar.") + for (f in factors_to_order) { + df_long[[f]] <- factor(df_long[[f]], levels = names(within[[f]])) + } return(df_long) } diff --git a/README.Rmd b/README.Rmd index c2c1f805..867ede2d 100644 --- a/README.Rmd +++ b/README.Rmd @@ -27,16 +27,24 @@ set.seed(200) ``` -It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier. +It is useful to be able to simulate data with a specified structure. The `faux` package provides some functions to make this process easier. See the [package website](https://debruine.github.io/faux/) for more details. + +## Installation + +You can install the newest version of faux from [GitHub](https://github.com/debruine/faux) with: + +``` r +devtools::install_github("debruine/faux") +``` ## sim_design -This function creates a dataset with a specific between- and within-subjects design. [see vignette](articles/sim_design.html) +This function creates a dataset with a specific between- and within-subjects design. [see vignette](https://debruine.github.io/faux/articles/sim_design.html) -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). +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). Set `plot = TRUE` to show a plot of means and SDs. -```{r} +```{r, fig.width = 8, fig.height = 4} between <- list("pet" = c("cat", "dog")) within <- list("time" = c("day", "night")) mu <- data.frame( @@ -46,7 +54,7 @@ mu <- data.frame( ) df <- sim_design(within, between, n = 100, mu = mu, sd = 5, r = .5, - empirical = TRUE) + empirical = TRUE, plot = TRUE) ``` `r check_sim_stats(df, between = "pet", usekable = TRUE)` @@ -54,7 +62,7 @@ Table: Sample `sim_design()` stats ## rnorm_multi -This function makes multiple normally distributed vectors with specified parameters and relationships. [see vignette](articles/rnorm_multi.html) +This function makes multiple normally distributed vectors with specified parameters and relationships. [see vignette](https://debruine.github.io/faux/articles/rnorm_multi.html) 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. @@ -78,7 +86,7 @@ Table: Sample `rnorm_multi()` stats ## 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](articles/sim_df.html) +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](https://debruine.github.io/faux/articles/sim_df.html) For example, the following code creates a new sample from the built-in dataset `iris` with 50 observations of each species. diff --git a/README_files/figure-markdown_github/plot-iris-sim-1.png b/README_files/figure-markdown_github/plot-iris-sim-1.png deleted file mode 100644 index 819df6be..00000000 Binary files a/README_files/figure-markdown_github/plot-iris-sim-1.png and /dev/null differ diff --git a/docs/LICENSE-text.html b/docs/LICENSE-text.html index e30bd682..e02e9438 100644 --- a/docs/LICENSE-text.html +++ b/docs/LICENSE-text.html @@ -60,7 +60,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/articles/index.html b/docs/articles/index.html index edd0368b..29027970 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -60,7 +60,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/articles/rnorm_multi.html b/docs/articles/rnorm_multi.html index ee3f4a82..acc49b5c 100644 --- a/docs/articles/rnorm_multi.html +++ b/docs/articles/rnorm_multi.html @@ -30,7 +30,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -82,7 +82,7 @@

Simulate Correlated Variables

Lisa DeBruine

-

2019-05-03

+

2019-05-06

diff --git a/docs/articles/sim_design.html b/docs/articles/sim_design.html index fe2d0404..64febb04 100644 --- a/docs/articles/sim_design.html +++ b/docs/articles/sim_design.html @@ -30,7 +30,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -82,7 +82,7 @@

Simulate by Design

Lisa DeBruine

-

2019-05-03

+

2019-05-06

@@ -95,7 +95,7 @@

2019-05-03

Quick example

-

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).

+

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). Set plot = TRUE to show a plot of means and SDs.

between <- list("pet" = c("cat", "dog"))
 within <- list("time" = c("day", "night"))
 mu <- data.frame(
@@ -103,7 +103,9 @@ 

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

@@ -162,7 +164,8 @@

Factor and level names

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)
+
df <- sim_design(within = c(2,3), between = c(2), 
+                 n = 5, mu = 0, sd = 1, r = 0.5)
pet
@@ -178,102 +181,102 @@

- - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + + - - - - - - + + + + + +
sub_id
S01 C1-2.7038494-2.0031140-1.24636190.2395364-0.3551418-0.6613917-0.57669120.08560080.9838375-0.0126229-1.55280520.0167980
S02 C1-0.2574224-2.5189879-0.0945284-1.0963883-0.9231602-0.83129291.36731091.88752751.56260661.93938480.75114182.1436454
S03 C10.93269600.57413980.94616071.51748110.13368172.24143710.0834231-1.0287178-0.62258900.30709571.59000140.8677457
S04 C1-0.2846845-0.0003843-0.42058920.14290450.7309652-0.4047417-1.3635503-1.2068872-0.7472159-2.2844708-1.3691766-1.9300476
S05 C10.02619540.5607634-1.2736072-1.01079321.33379950.28542690.0874826-0.44721250.07600420.26925480.61089160.1959173
S06 C2-0.9116783-1.0939985-1.0655517-0.9173210-1.4847558-2.5493564-0.44252740.76537330.7165515-0.75366280.0169834-0.3760313
S07 C2-0.06900600.47418050.3974194-0.18882210.09631501.45283411.12507221.0306602-0.04469200.8599006-0.34647500.2145361
S08 C2-0.45911010.8723573-0.2708937-0.62128911.34803800.9535270-0.02995561.08191441.14541490.96073330.32889861.7718617
S09 C20.1287775-0.3451130-0.5767718-1.8759247-0.8973256-1.19606850.53638771.16555950.72895940.40444980.50816341.5661115
S10 C21.06233201.0175079-0.16751000.56205790.75133210.7814270-0.5297062-2.1894619-2.2085771-1.3405989-1.4645263-1.5222258
diff --git a/docs/articles/sim_design_files/figure-html/unnamed-chunk-1-1.png b/docs/articles/sim_design_files/figure-html/unnamed-chunk-1-1.png new file mode 100644 index 00000000..018e0b28 Binary files /dev/null and b/docs/articles/sim_design_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/articles/sim_design_files/figure-html/unnamed-chunk-17-1.png b/docs/articles/sim_design_files/figure-html/unnamed-chunk-17-1.png index 0082dcfe..044cb988 100644 Binary files a/docs/articles/sim_design_files/figure-html/unnamed-chunk-17-1.png and b/docs/articles/sim_design_files/figure-html/unnamed-chunk-17-1.png differ diff --git a/docs/articles/sim_design_files/figure-html/unnamed-chunk-3-1.png b/docs/articles/sim_design_files/figure-html/unnamed-chunk-3-1.png index e4bb4a24..78f6f82f 100644 Binary files a/docs/articles/sim_design_files/figure-html/unnamed-chunk-3-1.png and b/docs/articles/sim_design_files/figure-html/unnamed-chunk-3-1.png differ diff --git a/docs/articles/sim_df.html b/docs/articles/sim_df.html index b970f643..6de413cd 100644 --- a/docs/articles/sim_df.html +++ b/docs/articles/sim_df.html @@ -30,7 +30,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -82,7 +82,7 @@

Simulate from Existing Data

Lisa DeBruine

-

2019-05-03

+

2019-05-06

diff --git a/docs/authors.html b/docs/authors.html index 1c314be5..14bbba2a 100644 --- a/docs/authors.html +++ b/docs/authors.html @@ -60,7 +60,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/index.html b/docs/index.html index 6c9528ff..34b1ae99 100644 --- a/docs/index.html +++ b/docs/index.html @@ -30,7 +30,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -85,12 +85,18 @@ -

It is useful to be able to simulate data with a specified structure. The faux package provides some functions to make this process easier.

+

It is useful to be able to simulate data with a specified structure. The faux package provides some functions to make this process easier. See the package website for more details.

+
+

+Installation

+

You can install the newest version of faux from GitHub with:

+
devtools::install_github("debruine/faux")
+

sim_design

-

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).

+

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). Set plot = TRUE to show a plot of means and SDs.

between <- list("pet" = c("cat", "dog"))
 within <- list("time" = c("day", "night"))
 mu <- data.frame(
@@ -100,7 +106,8 @@ 

 )
 df <- sim_design(within, between, 
                  n = 100, mu = mu, sd = 5, r = .5,
-                 empirical = TRUE)
+ empirical = TRUE, plot = TRUE)
+

@@ -155,7 +162,7 @@

rnorm_multi

-

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

+

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(
@@ -182,28 +189,28 @@

- - - - + + + + - + - - - + + + - - + + - - + +
Sample sim_design() stats
100 A 1.000.620.46-0.051.080.360.500.150.96
100 B0.620.36 1.000.1919.955.380.1819.375.20
100 C0.460.190.500.18 1.0019.815.1520.565.29
@@ -211,7 +218,7 @@

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

+

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")
diff --git a/docs/index_files/figure-html/plot-iris-sim-1.png b/docs/index_files/figure-html/plot-iris-sim-1.png index 29468f8d..ef74157c 100644 Binary files a/docs/index_files/figure-html/plot-iris-sim-1.png and b/docs/index_files/figure-html/plot-iris-sim-1.png differ diff --git a/docs/index_files/figure-html/unnamed-chunk-1-1.png b/docs/index_files/figure-html/unnamed-chunk-1-1.png new file mode 100644 index 00000000..018e0b28 Binary files /dev/null and b/docs/index_files/figure-html/unnamed-chunk-1-1.png differ diff --git a/docs/index_files/figure-html/unnamed-chunk-7-1.png b/docs/index_files/figure-html/unnamed-chunk-7-1.png new file mode 100644 index 00000000..0209b321 Binary files /dev/null and b/docs/index_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/docs/news/index.html b/docs/news/index.html index b8338901..13bd8b49 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -60,7 +60,7 @@ faux - 0.0.0.9006 + 0.0.0.9007
@@ -138,6 +138,21 @@

  • Changes to argument order and names (more consistent, but may break old scripts)
  • Updated vignettes
    • +

    +
    +

    +faux 0.0.0.9007

    +
      +
    • Added a plot option to check_design() and sim_design() +
      • +
    • Design lists returned by check_design() have a more consistent format +
        +
      • n, mu, and sd are all data frames with between-cells as rows and within-cells as columns
        • +
      • +within and between are named lists; factors and labels are no longer separately named
        • +
      +
      • +
    @@ -148,6 +163,7 @@

    Contents

  • 0.0.0.9004
  • 0.0.0.9005
  • 0.0.0.9006
    • +
  • 0.0.0.9007
    • diff --git a/docs/reference/cell_combos.html b/docs/reference/cell_combos.html new file mode 100644 index 00000000..2538a4ca --- /dev/null +++ b/docs/reference/cell_combos.html @@ -0,0 +1,172 @@ + + + + + + + + +Cell combos — cell_combos • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + +
    + +
    +
    +
    +

    Cell combos

    + + +
    + +
    + +

    Creates wide cell combination names, such as A1_B1, A2_B1, A1_B2, A2_B2.

    + +
    + + 
    cell_combos(factors)
    + +

    Arguments

    + + + + + + +
    factors

    A list of factors

    + +

    Value

    + +

    a list

    + + +
    + +
    + +
    +
    +

    Developed by Lisa DeBruine.

    +
    + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    +
    + + + + + + diff --git a/docs/reference/check_design-1.png b/docs/reference/check_design-1.png new file mode 100644 index 00000000..6ae7c014 Binary files /dev/null and b/docs/reference/check_design-1.png differ diff --git a/docs/reference/check_design.html b/docs/reference/check_design.html index b6769873..55a780e0 100644 --- a/docs/reference/check_design.html +++ b/docs/reference/check_design.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -127,7 +127,7 @@

    Validate design

    check_design(within = list(), between = list(), n = 100, mu = 0,
-  sd = 1, r = 0)
    + sd = 1, r = 0, plot = TRUE)

    Arguments

    @@ -156,6 +156,10 @@

    Arg

    + + + +
    r

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

    whether to show a plot of the design

    Value

    @@ -167,89 +171,7 @@

    Examp 
    
 within <- list(time = c("day", "night"))
 between <- list(pet = c("dog", "cat"))
-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_design(within, between)
    @@ -168,22 +168,22 @@

    Value

    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>
    diff --git a/docs/reference/cormat.html b/docs/reference/cormat.html index 6abdb450..111b644f 100644 --- a/docs/reference/cormat.html +++ b/docs/reference/cormat.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/cormat_from_triangle.html b/docs/reference/cormat_from_triangle.html index d7d2db61..c48e1c57 100644 --- a/docs/reference/cormat_from_triangle.html +++ b/docs/reference/cormat_from_triangle.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/faceratings.html b/docs/reference/faceratings.html index 8ed382bd..209fed57 100644 --- a/docs/reference/faceratings.html +++ b/docs/reference/faceratings.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/fix_name_labels.html b/docs/reference/fix_name_labels.html index a6041bfe..c17f5e09 100644 --- a/docs/reference/fix_name_labels.html +++ b/docs/reference/fix_name_labels.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/get_design_long.html b/docs/reference/get_design_long.html index 5518b325..501f7b9f 100644 --- a/docs/reference/get_design_long.html +++ b/docs/reference/get_design_long.html @@ -66,7 +66,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/index.html b/docs/reference/index.html index bedc5427..56fe4444 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -60,7 +60,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -186,6 +186,12 @@

    plot_design()

    + +

    Plot design

    + +

    pos_def_limits()

    diff --git a/docs/reference/is_pos_def.html b/docs/reference/is_pos_def.html index cc7e1e4c..f14eecdf 100644 --- a/docs/reference/is_pos_def.html +++ b/docs/reference/is_pos_def.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/long2wide-1.png b/docs/reference/long2wide-1.png new file mode 100644 index 00000000..77973ea3 Binary files /dev/null and b/docs/reference/long2wide-1.png differ diff --git a/docs/reference/long2wide.html b/docs/reference/long2wide.html index 654b00f5..8fce0d5e 100644 --- a/docs/reference/long2wide.html +++ b/docs/reference/long2wide.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -161,20 +161,20 @@

    Value

    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
    
+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     0.712   1.74 
+#>  2 S002   B1    -0.662  -0.845
+#>  3 S003   B1    -0.291  -0.962
+#>  4 S004   B1    -0.198   1.02 
+#>  5 S005   B1     1.20   -1.50 
+#>  6 S006   B1     0.0398 -1.18 
+#>  7 S007   B1    -0.687   0.630
+#>  8 S008   B1    -0.705   2.10 
+#>  9 S009   B1    -0.991  -0.614
+#> 10 S010   B1    -1.14   -1.63 
+#> # … with 190 more rows
    diff --git a/docs/reference/pipe.html b/docs/reference/pipe.html index 3fa0d792..0de6aca8 100644 --- a/docs/reference/pipe.html +++ b/docs/reference/pipe.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/plot_design-1.png b/docs/reference/plot_design-1.png new file mode 100644 index 00000000..6ae7c014 Binary files /dev/null and b/docs/reference/plot_design-1.png differ diff --git a/docs/reference/plot_design-2.png b/docs/reference/plot_design-2.png new file mode 100644 index 00000000..6ae7c014 Binary files /dev/null and b/docs/reference/plot_design-2.png differ diff --git a/docs/reference/plot_design.html b/docs/reference/plot_design.html new file mode 100644 index 00000000..8a972a06 --- /dev/null +++ b/docs/reference/plot_design.html @@ -0,0 +1,188 @@ + + + + + + + + +Plot design — plot_design • faux + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + +
    + +
    +
    +
    +

    Plot design

    + + +
    + +
    + +

    plot_design() plots the specified within and between design

    + +
    + + 
    plot_design(design, id = "sub_id", dv = "val")
    + +

    Arguments

    + + + + + + + + + + + + + + +
    design

    A list of design parameters created by check_design() or a data tbl (in long format)

    id

    the column name(s) that identify a unit of analysis

    dv

    the column name that identifies the DV

    + +

    Value

    + +

    plot

    + + +

    Examples

    + 
    
+within <- list(time = c("day", "night"))
+between <- list(pet = c("dog", "cat"))
+check_design(within, between) %>% plot_design()
    
+
    +
    + +
    + + +
    + + + + + + diff --git a/docs/reference/pos_def_limits.html b/docs/reference/pos_def_limits.html index 628cd5a6..ad1d1650 100644 --- a/docs/reference/pos_def_limits.html +++ b/docs/reference/pos_def_limits.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/rnorm_multi.html b/docs/reference/rnorm_multi.html index da60ab24..ed51959b 100644 --- a/docs/reference/rnorm_multi.html +++ b/docs/reference/rnorm_multi.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -176,207 +176,207 @@

    Value

    Examples

    - 
    rnorm_multi(100, 3, 0, 1, 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, 0, 1, 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, 0, 1, c(0.2, 0.4, 0.5), varnames=c("A", "B", "C"))
    #>                A           B             C
+#> 1    0.580063420 -0.09872452  1.0784038131
+#> 2    0.090745757 -2.21205847 -1.1273322465
+#> 3   -0.853981885 -1.05175966 -0.1363566233
+#> 4   -1.211615762 -0.80127979 -0.9921388410
+#> 5    0.192619570 -0.83783332 -0.0197866182
+#> 6   -0.114386798  0.66023232  0.3335085124
+#> 7   -0.062983007 -0.27383834  0.5704329171
+#> 8    0.193590660  0.14185678  0.1785742297
+#> 9   -0.340948097 -1.43084148 -2.0317941800
+#> 10   2.133504111 -0.93541542 -0.6585662765
+#> 11   0.396551231  0.52315754 -1.0162089235
+#> 12  -0.384779171  0.38412907 -0.8367164291
+#> 13   0.561300727 -0.28195229 -0.9540677145
+#> 14   0.675929362 -0.93428998 -0.0396729591
+#> 15   0.283146696 -2.36093571 -0.4917625244
+#> 16   0.280137890  0.67161321 -0.8344114511
+#> 17  -0.526655044  0.44061761 -0.0237941410
+#> 18   0.408507595  1.26118948  0.2582611048
+#> 19  -0.388734127 -0.35053585  0.5105104491
+#> 20  -0.244792403 -0.71618395  1.4115771521
+#> 21  -1.106792267  0.24557872 -1.8149257666
+#> 22  -0.943906760  0.60176480  0.1351054811
+#> 23  -0.267375172 -0.29709120  0.1000263582
+#> 24   0.452204594 -1.17153794  0.5214464384
+#> 25   0.536259704 -1.09615222 -0.5867834183
+#> 26  -0.236333920  0.49410299  0.0880169640
+#> 27   0.738830878 -0.03269877 -0.0777189754
+#> 28  -0.376182790  1.04535590 -1.1190239099
+#> 29  -0.405648929  0.69822409  1.5864914799
+#> 30  -1.160958357 -1.35009209 -0.9999216075
+#> 31   1.006528656  0.61391014  0.3346503048
+#> 32  -1.067981697 -0.75249566 -1.1806269747
+#> 33  -0.357735257 -0.50998132 -0.9337983791
+#> 34   0.788906573 -0.46545444 -0.4438848537
+#> 35  -0.398840231 -0.91834381 -0.6787544321
+#> 36   1.375238390  0.31725327  2.3153530303
+#> 37   1.309298537  1.06693175 -0.0002758011
+#> 38  -0.530013223  0.71744745  0.7425554627
+#> 39   1.474483534  2.06301787  0.8343343677
+#> 40  -1.418679613  0.55912804  0.0881185330
+#> 41   1.987764609 -1.66214704  0.3601230636
+#> 42   2.552996783  1.74737931  1.3149925548
+#> 43   0.524412448 -0.01102079 -0.4011135037
+#> 44  -0.235328699 -0.60681908 -0.6471093657
+#> 45  -0.552260875  0.48935906 -0.4639006189
+#> 46  -0.808378301  1.56206858  0.4561830091
+#> 47   0.376584113  0.17787723  0.3772892838
+#> 48  -1.523135326 -1.12397467 -0.7134540706
+#> 49   0.184684633 -0.30179709 -0.3685977391
+#> 50   0.708403805  0.89554929  1.5859329703
+#> 51   0.254379268  0.43257257  0.0637306254
+#> 52   1.570630317 -1.16270365  0.7844504392
+#> 53   0.744131290 -0.76748954 -1.5993367811
+#> 54   1.131117657 -0.21915384 -0.5733556287
+#> 55  -0.664541255  0.55462340 -1.9073133381
+#> 56  -1.232069852 -0.76910659 -1.4118222935
+#> 57   0.392813673  0.94396233 -0.4308654690
+#> 58  -0.144178008 -0.64698290 -0.6334769218
+#> 59   1.164475659  1.45358400  0.9452965010
+#> 60  -0.233225524 -0.68819951 -0.4350951565
+#> 61   0.062137521  0.31379011  0.7014771458
+#> 62   0.637040390  0.58403290 -0.1763327235
+#> 63  -0.270598875  0.15623892  1.0077853602
+#> 64  -1.481668036 -1.54096019 -2.0061784034
+#> 65  -0.919249237 -2.08932046 -0.4806280284
+#> 66   0.825289240 -0.24405615  0.2990468946
+#> 67  -1.216081227 -0.96032094 -0.0007913805
+#> 68   0.206769111  0.24427677  0.0626262792
+#> 69  -1.048041344 -1.13750948 -2.2996884331
+#> 70   1.527935152  0.97355428  1.7036046674
+#> 71   0.250693735 -0.41953515 -0.1767846545
+#> 72  -1.293875018  1.76704580  0.9987815774
+#> 73   0.390036316  1.64125076  0.5327712176
+#> 74  -1.469682995 -1.17649262 -0.2694275683
+#> 75  -0.887855165 -0.01672167 -1.2938965114
+#> 76  -1.090398999 -0.88722919 -0.8025775367
+#> 77  -0.736544396 -0.52134426 -0.2607690775
+#> 78  -0.420887465 -0.74300358 -0.1066756230
+#> 79   1.318045818  0.06358653  0.5941506545
+#> 80  -0.416977414 -0.73062603  1.2852225469
+#> 81   0.318201763  0.27278899 -0.4486797764
+#> 82  -0.254429189  0.34508490  0.6871103199
+#> 83   1.934886554 -1.00513721  0.2076976831
+#> 84  -2.219295612 -0.31100630 -0.6100749433
+#> 85  -0.178935745 -1.18453086 -0.4916501471
+#> 86   0.800414923 -0.87700750 -0.7008907125
+#> 87   0.083590913 -2.54822932 -1.3076891318
+#> 88   0.810749904  1.74466727  2.3461962818
+#> 89  -0.446075261  1.02050310 -0.1750381996
+#> 90   0.748926181 -1.34145000 -0.7647587615
+#> 91   0.996404056  0.44301890  0.4587844649
+#> 92   2.093318974  0.42018888  1.1995588949
+#> 93  -1.929648067  0.54120179 -1.4522228536
+#> 94  -1.846602547 -1.01815754 -0.7388437472
+#> 95  -0.009962785  0.96157126  1.5401153622
+#> 96  -0.669352258  0.61491480 -1.2687851047
+#> 97  -1.241527096 -1.03666393 -1.1485718522
+#> 98  -0.595984914  0.39510322  1.0143729066
+#> 99   0.042787189 -0.01241021 -0.5676048913
+#> 100  1.578485546  0.94305738  0.4515601245
    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"))
    #>                A            B             C
+#> 1    0.484658341 -0.742205822 -0.4449783380
+#> 2    1.186786630 -1.000544878 -0.9497220323
+#> 3   -0.381113323 -0.644041064 -0.6810782917
+#> 4   -1.339846455  0.517809498  1.2797721443
+#> 5   -0.181445384 -0.285510466 -0.7970645955
+#> 6   -0.242856053 -0.690225584  0.2714375811
+#> 7   -1.407509023  0.914543179  1.5745573922
+#> 8   -0.608448700  1.085903958  0.6950162330
+#> 9    1.006665710  0.230597685 -0.5005835875
+#> 10   2.688694138  0.023284165 -1.0438745075
+#> 11   0.620829156 -0.209781278 -0.3128392194
+#> 12   0.489134064  0.002199792 -0.9675840914
+#> 13  -0.484478368  0.254056963  0.6661222175
+#> 14  -0.606086667  1.105266125  0.1869479128
+#> 15  -0.356848057 -0.145414572  1.0999266440
+#> 16  -0.207898361 -1.005676610 -0.3997856883
+#> 17   0.696548011 -0.724568456 -1.1672952581
+#> 18  -0.128278497 -0.133843063 -1.0397383583
+#> 19  -0.668768791  0.413550026  0.6493829976
+#> 20   0.639193185  0.466345909  0.2680866712
+#> 21   1.198673387 -0.831190728 -1.3807877910
+#> 22  -2.233381972 -1.364908356  0.6418456875
+#> 23  -0.006388825  0.128843689 -0.0187768989
+#> 24  -0.327426061 -0.336877882  0.4006878327
+#> 25  -0.901291393  0.323884749  0.9662414449
+#> 26  -1.155951524  1.086363905  1.1052114866
+#> 27   1.251580671  0.919370611  0.1225804313
+#> 28  -0.069191573 -0.396917498 -0.7994340803
+#> 29  -0.280314246 -1.215826046 -0.2231116973
+#> 30   1.750322871  0.857307988 -0.0007566503
+#> 31   0.423765513 -1.626792490 -0.7358762655
+#> 32   0.882765629 -0.499647999 -0.7955647900
+#> 33  -0.644431348 -0.853866273 -1.0201752962
+#> 34  -0.596586156 -1.011250991  0.1999263842
+#> 35  -1.850151884  0.217921810  1.2229191838
+#> 36  -0.258891740 -0.059479010 -0.0749215286
+#> 37   1.261662599  0.765095233 -0.8528689506
+#> 38   0.663405728  0.132448812 -1.1795552736
+#> 39  -0.888862937 -1.257320157 -0.6709252565
+#> 40  -1.429992304 -0.121120191  0.1786727354
+#> 41   0.827544340 -0.360732604 -1.3953480577
+#> 42  -0.778549707  1.456603829  0.1303537042
+#> 43   0.101137027 -1.108538951 -0.7668516668
+#> 44  -2.083907549 -1.664642951  0.6758047495
+#> 45   0.784731572 -0.357857436 -1.2570525548
+#> 46  -0.396700814  0.059989238 -0.2490777923
+#> 47  -0.142656675  0.794573153 -0.0222581321
+#> 48   0.883357520 -0.086309086  0.0991830449
+#> 49  -1.275348327 -0.236850668  0.4418877639
+#> 50  -0.063148286  1.335536045  0.7004060631
+#> 51  -0.189248763  2.493607894  1.3344436428
+#> 52  -0.379607355  1.199938987  1.8268748851
+#> 53   2.136125617  0.784385418  0.8771598668
+#> 54  -0.133719304  1.254436644  1.8660023148
+#> 55   0.055052583 -0.233683471 -2.0902631686
+#> 56   0.863391065  0.347070631  0.6429265108
+#> 57   1.266501199  1.526597049  0.5495661158
+#> 58   0.110814965 -0.709871332 -0.3375972250
+#> 59   2.873482195  1.333414640 -1.7372521577
+#> 60  -0.121849296  2.057985608  1.0459021110
+#> 61  -1.969534427 -1.061842864  1.2104195769
+#> 62   0.954160571  0.078773527 -0.4458596260
+#> 63  -0.450664181 -0.381147771 -0.9290400544
+#> 64   0.659332635  1.078532099 -0.4631530029
+#> 65   1.753360709  0.406460694 -0.1594744993
+#> 66   0.356176961 -0.182342767 -0.0863782273
+#> 67  -0.378471770  0.786100705  0.6331283834
+#> 68  -0.544572435  2.104225395  2.6224708911
+#> 69  -0.197379421  0.125805280  0.2559255452
+#> 70  -0.303661009 -0.706925285 -1.2438850305
+#> 71  -0.159819774 -1.354128104 -0.5614660445
+#> 72  -0.312157889 -0.090535005  0.6279715098
+#> 73  -0.268737731 -2.137525108 -0.0414486934
+#> 74   0.100258193 -0.170363547 -1.4314265220
+#> 75   1.132417516 -0.083062632 -1.0480427931
+#> 76   0.786918873  0.043642424 -1.0352274354
+#> 77  -0.001441801  0.957098100  0.5364156134
+#> 78   0.874144243 -1.898837326 -1.7762561578
+#> 79   0.075263017  1.998466596  1.6697597415
+#> 80  -0.522652730 -1.209063287  0.1022117196
+#> 81   0.213258376  0.847526255  0.3208845313
+#> 82  -0.136484929 -0.809384890 -0.7026234504
+#> 83   0.059288270 -0.600432078 -0.7101281773
+#> 84   0.737873426  1.018864267  0.0180777342
+#> 85   1.076905586  1.299179888  1.4502742459
+#> 86   1.069959862  0.644484824 -1.7847490104
+#> 87   0.455682746  0.077335425  0.1528263410
+#> 88  -1.552552000 -1.605602164 -0.2905896920
+#> 89  -0.677481263 -0.086980016  0.7011170010
+#> 90  -0.025159957 -0.069020087 -0.2286867488
+#> 91   0.333055133  0.345835151  0.0177301819
+#> 92  -1.188859716 -0.693241686  1.1911399347
+#> 93   0.272511523  0.499988896 -0.1223121118
+#> 94  -0.831075700 -0.846499224  0.5899186297
+#> 95   0.702452657 -0.048435204 -1.0755290853
+#> 96   0.553022585 -0.898320302 -0.6642299409
+#> 97  -0.076327485 -0.698772198 -0.2438553542
+#> 98  -0.831051326 -0.887911590 -0.4662089586
+#> 99  -0.302220904  1.710761438  1.4132701210
+#> 100  0.728187681 -1.090645135 -2.2160014589
    
 
    
   
    
   
 
diff --git a/docs/reference/select_num_grp.html b/docs/reference/select_num_grp.html
index 6541896e..2f6bb5b8 100644
--- a/docs/reference/select_num_grp.html
+++ b/docs/reference/select_num_grp.html
@@ -63,7 +63,7 @@
       
       
         faux
-        0.0.0.9006
+        0.0.0.9007
       
     
 
@@ -151,21 +151,21 @@ 
    Value
    
     
 
     
    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
    
   
   
 
@@ -127,7 +127,7 @@ 
    Simulate Data from Design
    
     
 
     
    sim_design(within = list(), between = list(), n = 100, mu = 0,
-  sd = 1, r = 0, empirical = FALSE, long = FALSE)
    
+  sd = 1, r = 0, empirical = FALSE, long = FALSE, plot = FALSE)

    Arguments

    @@ -164,6 +164,10 @@

    Arg

    + + + +
    long

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

    whether to show a plot of the design

    Value

    diff --git a/docs/reference/sim_design_.html b/docs/reference/sim_design_.html index 997e7e70..b52f01a0 100644 --- a/docs/reference/sim_design_.html +++ b/docs/reference/sim_design_.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/sim_df.html b/docs/reference/sim_df.html index e3ea6eb6..bf31bade 100644 --- a/docs/reference/sim_df.html +++ b/docs/reference/sim_df.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 diff --git a/docs/reference/sim_mixed_df.html b/docs/reference/sim_mixed_df.html index 36852235..b488c9a8 100644 --- a/docs/reference/sim_mixed_df.html +++ b/docs/reference/sim_mixed_df.html @@ -63,7 +63,7 @@ faux - 0.0.0.9006 + 0.0.0.9007 @@ -164,107 +164,107 @@

    Value

    Examples

    - 
    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
    + 
    sim_mixed_df(faceratings, 10, 10, "rating", "rater_id", "face_id")
    #>     sub_id item_id      sub_i        item_i         dv
+#> 1        1       1 -0.2396229 -0.6240398122  2.8518221
+#> 2        2       1 -0.5442409 -0.6240398122  1.9668874
+#> 3        3       1 -1.0690789 -0.6240398122 -0.2629545
+#> 4        4       1 -0.1472031 -0.6240398122  2.7428086
+#> 5        5       1 -1.8498327 -0.6240398122  1.9288361
+#> 6        6       1  0.4872866 -0.6240398122  0.7987672
+#> 7        7       1  0.0527697 -0.6240398122  2.7039147
+#> 8        8       1  1.4916886 -0.6240398122  5.0352024
+#> 9        9       1  1.1627588 -0.6240398122  4.0272131
+#> 10      10       1  1.3705433 -0.6240398122  1.8661317
+#> 11       1       2 -0.2396229  0.0737720044  5.7190067
+#> 12       2       2 -0.5442409  0.0737720044  2.5149279
+#> 13       3       2 -1.0690789  0.0737720044 -0.4307355
+#> 14       4       2 -0.1472031  0.0737720044  4.9436124
+#> 15       5       2 -1.8498327  0.0737720044  1.7851634
+#> 16       6       2  0.4872866  0.0737720044  3.7499332
+#> 17       7       2  0.0527697  0.0737720044  3.7508227
+#> 18       8       2  1.4916886  0.0737720044  5.3318171
+#> 19       9       2  1.1627588  0.0737720044  0.6610708
+#> 20      10       2  1.3705433  0.0737720044  5.4186885
+#> 21       1       3 -0.2396229  0.7077325203  5.0765159
+#> 22       2       3 -0.5442409  0.7077325203  3.0633821
+#> 23       3       3 -1.0690789  0.7077325203  4.0995844
+#> 24       4       3 -0.1472031  0.7077325203  4.8202937
+#> 25       5       3 -1.8498327  0.7077325203  0.8647338
+#> 26       6       3  0.4872866  0.7077325203  4.0022090
+#> 27       7       3  0.0527697  0.7077325203  4.7845643
+#> 28       8       3  1.4916886  0.7077325203  4.9096337
+#> 29       9       3  1.1627588  0.7077325203  5.5897579
+#> 30      10       3  1.3705433  0.7077325203  6.4079320
+#> 31       1       4 -0.2396229  0.3657847830  2.0720024
+#> 32       2       4 -0.5442409  0.3657847830  4.3231517
+#> 33       3       4 -1.0690789  0.3657847830  1.6225195
+#> 34       4       4 -0.1472031  0.3657847830  1.8747657
+#> 35       5       4 -1.8498327  0.3657847830  0.9092096
+#> 36       6       4  0.4872866  0.3657847830  2.0484583
+#> 37       7       4  0.0527697  0.3657847830  3.7233531
+#> 38       8       4  1.4916886  0.3657847830  3.3307094
+#> 39       9       4  1.1627588  0.3657847830  5.3892575
+#> 40      10       4  1.3705433  0.3657847830  4.0696969
+#> 41       1       5 -0.2396229 -0.3051109814 -0.2706484
+#> 42       2       5 -0.5442409 -0.3051109814  3.3070643
+#> 43       3       5 -1.0690789 -0.3051109814  2.4620409
+#> 44       4       5 -0.1472031 -0.3051109814  3.4348493
+#> 45       5       5 -1.8498327 -0.3051109814 -0.8413550
+#> 46       6       5  0.4872866 -0.3051109814  1.9528294
+#> 47       7       5  0.0527697 -0.3051109814  3.2404056
+#> 48       8       5  1.4916886 -0.3051109814  5.3511790
+#> 49       9       5  1.1627588 -0.3051109814  4.9246782
+#> 50      10       5  1.3705433 -0.3051109814  4.8392120
+#> 51       1       6 -0.2396229  0.3314753477  2.8604816
+#> 52       2       6 -0.5442409  0.3314753477  1.7884178
+#> 53       3       6 -1.0690789  0.3314753477  1.2994108
+#> 54       4       6 -0.1472031  0.3314753477  3.1696075
+#> 55       5       6 -1.8498327  0.3314753477  0.5555800
+#> 56       6       6  0.4872866  0.3314753477  6.2175933
+#> 57       7       6  0.0527697  0.3314753477  5.0767993
+#> 58       8       6  1.4916886  0.3314753477  2.7947075
+#> 59       9       6  1.1627588  0.3314753477  5.5094497
+#> 60      10       6  1.3705433  0.3314753477  4.2839022
+#> 61       1       7 -0.2396229  0.6568782867  2.0689045
+#> 62       2       7 -0.5442409  0.6568782867  4.4199572
+#> 63       3       7 -1.0690789  0.6568782867  3.7038534
+#> 64       4       7 -0.1472031  0.6568782867  2.3687959
+#> 65       5       7 -1.8498327  0.6568782867  0.6508695
+#> 66       6       7  0.4872866  0.6568782867  2.5024103
+#> 67       7       7  0.0527697  0.6568782867  2.2378004
+#> 68       8       7  1.4916886  0.6568782867  3.7602979
+#> 69       9       7  1.1627588  0.6568782867  1.6174663
+#> 70      10       7  1.3705433  0.6568782867  6.2707761
+#> 71       1       8 -0.2396229 -0.1315365784  2.6521274
+#> 72       2       8 -0.5442409 -0.1315365784  2.5200856
+#> 73       3       8 -1.0690789 -0.1315365784  0.2040451
+#> 74       4       8 -0.1472031 -0.1315365784  4.0073279
+#> 75       5       8 -1.8498327 -0.1315365784 -0.3466308
+#> 76       6       8  0.4872866 -0.1315365784  2.1630615
+#> 77       7       8  0.0527697 -0.1315365784  1.5184225
+#> 78       8       8  1.4916886 -0.1315365784  4.1895499
+#> 79       9       8  1.1627588 -0.1315365784  3.3068711
+#> 80      10       8  1.3705433 -0.1315365784  3.5817560
+#> 81       1       9 -0.2396229 -0.0006109263  2.6098816
+#> 82       2       9 -0.5442409 -0.0006109263  1.2921410
+#> 83       3       9 -1.0690789 -0.0006109263  2.5477599
+#> 84       4       9 -0.1472031 -0.0006109263  4.3883662
+#> 85       5       9 -1.8498327 -0.0006109263 -0.5593484
+#> 86       6       9  0.4872866 -0.0006109263  5.7547464
+#> 87       7       9  0.0527697 -0.0006109263  1.2249057
+#> 88       8       9  1.4916886 -0.0006109263  5.4460634
+#> 89       9       9  1.1627588 -0.0006109263  3.7680685
+#> 90      10       9  1.3705433 -0.0006109263  6.3987326
+#> 91       1      10 -0.2396229  0.6643744909  4.5724435
+#> 92       2      10 -0.5442409  0.6643744909  1.5404019
+#> 93       3      10 -1.0690789  0.6643744909 -0.4066272
+#> 94       4      10 -0.1472031  0.6643744909  3.4523366
+#> 95       5      10 -1.8498327  0.6643744909  0.7951289
+#> 96       6      10  0.4872866  0.6643744909  4.9414845
+#> 97       7      10  0.0527697  0.6643744909  5.6692058
+#> 98       8      10  1.4916886  0.6643744909  6.1173654
+#> 99       9      10  1.1627588  0.6643744909  5.0124822
+#> 100     10      10  1.3705433  0.6643744909  5.0033603
    @@ -407,7 +407,356 @@

    Examp #> 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 ]
    +#> 251 virginica Sepal Width 3.3 +#> 252 virginica Sepal Width 2.7 +#> 253 virginica Sepal Width 3.0 +#> 254 virginica Sepal Width 2.9 +#> 255 virginica Sepal Width 3.0 +#> 256 virginica Sepal Width 3.0 +#> 257 virginica Sepal Width 2.5 +#> 258 virginica Sepal Width 2.9 +#> 259 virginica Sepal Width 2.5 +#> 260 virginica Sepal Width 3.6 +#> 261 virginica Sepal Width 3.2 +#> 262 virginica Sepal Width 2.7 +#> 263 virginica Sepal Width 3.0 +#> 264 virginica Sepal Width 2.5 +#> 265 virginica Sepal Width 2.8 +#> 266 virginica Sepal Width 3.2 +#> 267 virginica Sepal Width 3.0 +#> 268 virginica Sepal Width 3.8 +#> 269 virginica Sepal Width 2.6 +#> 270 virginica Sepal Width 2.2 +#> 271 virginica Sepal Width 3.2 +#> 272 virginica Sepal Width 2.8 +#> 273 virginica Sepal Width 2.8 +#> 274 virginica Sepal Width 2.7 +#> 275 virginica Sepal Width 3.3 +#> 276 virginica Sepal Width 3.2 +#> 277 virginica Sepal Width 2.8 +#> 278 virginica Sepal Width 3.0 +#> 279 virginica Sepal Width 2.8 +#> 280 virginica Sepal Width 3.0 +#> 281 virginica Sepal Width 2.8 +#> 282 virginica Sepal Width 3.8 +#> 283 virginica Sepal Width 2.8 +#> 284 virginica Sepal Width 2.8 +#> 285 virginica Sepal Width 2.6 +#> 286 virginica Sepal Width 3.0 +#> 287 virginica Sepal Width 3.4 +#> 288 virginica Sepal Width 3.1 +#> 289 virginica Sepal Width 3.0 +#> 290 virginica Sepal Width 3.1 +#> 291 virginica Sepal Width 3.1 +#> 292 virginica Sepal Width 3.1 +#> 293 virginica Sepal Width 2.7 +#> 294 virginica Sepal Width 3.2 +#> 295 virginica Sepal Width 3.3 +#> 296 virginica Sepal Width 3.0 +#> 297 virginica Sepal Width 2.5 +#> 298 virginica Sepal Width 3.0 +#> 299 virginica Sepal Width 3.4 +#> 300 virginica Sepal Width 3.0 +#> 301 setosa Petal Length 1.4 +#> 302 setosa Petal Length 1.4 +#> 303 setosa Petal Length 1.3 +#> 304 setosa Petal Length 1.5 +#> 305 setosa Petal Length 1.4 +#> 306 setosa Petal Length 1.7 +#> 307 setosa Petal Length 1.4 +#> 308 setosa Petal Length 1.5 +#> 309 setosa Petal Length 1.4 +#> 310 setosa Petal Length 1.5 +#> 311 setosa Petal Length 1.5 +#> 312 setosa Petal Length 1.6 +#> 313 setosa Petal Length 1.4 +#> 314 setosa Petal Length 1.1 +#> 315 setosa Petal Length 1.2 +#> 316 setosa Petal Length 1.5 +#> 317 setosa Petal Length 1.3 +#> 318 setosa Petal Length 1.4 +#> 319 setosa Petal Length 1.7 +#> 320 setosa Petal Length 1.5 +#> 321 setosa Petal Length 1.7 +#> 322 setosa Petal Length 1.5 +#> 323 setosa Petal Length 1.0 +#> 324 setosa Petal Length 1.7 +#> 325 setosa Petal Length 1.9 +#> 326 setosa Petal Length 1.6 +#> 327 setosa Petal Length 1.6 +#> 328 setosa Petal Length 1.5 +#> 329 setosa Petal Length 1.4 +#> 330 setosa Petal Length 1.6 +#> 331 setosa Petal Length 1.6 +#> 332 setosa Petal Length 1.5 +#> 333 setosa Petal Length 1.5 +#> 334 setosa Petal Length 1.4 +#> 335 setosa Petal Length 1.5 +#> 336 setosa Petal Length 1.2 +#> 337 setosa Petal Length 1.3 +#> 338 setosa Petal Length 1.4 +#> 339 setosa Petal Length 1.3 +#> 340 setosa Petal Length 1.5 +#> 341 setosa Petal Length 1.3 +#> 342 setosa Petal Length 1.3 +#> 343 setosa Petal Length 1.3 +#> 344 setosa Petal Length 1.6 +#> 345 setosa Petal Length 1.9 +#> 346 setosa Petal Length 1.4 +#> 347 setosa Petal Length 1.6 +#> 348 setosa Petal Length 1.4 +#> 349 setosa Petal Length 1.5 +#> 350 setosa Petal Length 1.4 +#> 351 versicolor Petal Length 4.7 +#> 352 versicolor Petal Length 4.5 +#> 353 versicolor Petal Length 4.9 +#> 354 versicolor Petal Length 4.0 +#> 355 versicolor Petal Length 4.6 +#> 356 versicolor Petal Length 4.5 +#> 357 versicolor Petal Length 4.7 +#> 358 versicolor Petal Length 3.3 +#> 359 versicolor Petal Length 4.6 +#> 360 versicolor Petal Length 3.9 +#> 361 versicolor Petal Length 3.5 +#> 362 versicolor Petal Length 4.2 +#> 363 versicolor Petal Length 4.0 +#> 364 versicolor Petal Length 4.7 +#> 365 versicolor Petal Length 3.6 +#> 366 versicolor Petal Length 4.4 +#> 367 versicolor Petal Length 4.5 +#> 368 versicolor Petal Length 4.1 +#> 369 versicolor Petal Length 4.5 +#> 370 versicolor Petal Length 3.9 +#> 371 versicolor Petal Length 4.8 +#> 372 versicolor Petal Length 4.0 +#> 373 versicolor Petal Length 4.9 +#> 374 versicolor Petal Length 4.7 +#> 375 versicolor Petal Length 4.3 +#> 376 versicolor Petal Length 4.4 +#> 377 versicolor Petal Length 4.8 +#> 378 versicolor Petal Length 5.0 +#> 379 versicolor Petal Length 4.5 +#> 380 versicolor Petal Length 3.5 +#> 381 versicolor Petal Length 3.8 +#> 382 versicolor Petal Length 3.7 +#> 383 versicolor Petal Length 3.9 +#> 384 versicolor Petal Length 5.1 +#> 385 versicolor Petal Length 4.5 +#> 386 versicolor Petal Length 4.5 +#> 387 versicolor Petal Length 4.7 +#> 388 versicolor Petal Length 4.4 +#> 389 versicolor Petal Length 4.1 +#> 390 versicolor Petal Length 4.0 +#> 391 versicolor Petal Length 4.4 +#> 392 versicolor Petal Length 4.6 +#> 393 versicolor Petal Length 4.0 +#> 394 versicolor Petal Length 3.3 +#> 395 versicolor Petal Length 4.2 +#> 396 versicolor Petal Length 4.2 +#> 397 versicolor Petal Length 4.2 +#> 398 versicolor Petal Length 4.3 +#> 399 versicolor Petal Length 3.0 +#> 400 versicolor Petal Length 4.1 +#> 401 virginica Petal Length 6.0 +#> 402 virginica Petal Length 5.1 +#> 403 virginica Petal Length 5.9 +#> 404 virginica Petal Length 5.6 +#> 405 virginica Petal Length 5.8 +#> 406 virginica Petal Length 6.6 +#> 407 virginica Petal Length 4.5 +#> 408 virginica Petal Length 6.3 +#> 409 virginica Petal Length 5.8 +#> 410 virginica Petal Length 6.1 +#> 411 virginica Petal Length 5.1 +#> 412 virginica Petal Length 5.3 +#> 413 virginica Petal Length 5.5 +#> 414 virginica Petal Length 5.0 +#> 415 virginica Petal Length 5.1 +#> 416 virginica Petal Length 5.3 +#> 417 virginica Petal Length 5.5 +#> 418 virginica Petal Length 6.7 +#> 419 virginica Petal Length 6.9 +#> 420 virginica Petal Length 5.0 +#> 421 virginica Petal Length 5.7 +#> 422 virginica Petal Length 4.9 +#> 423 virginica Petal Length 6.7 +#> 424 virginica Petal Length 4.9 +#> 425 virginica Petal Length 5.7 +#> 426 virginica Petal Length 6.0 +#> 427 virginica Petal Length 4.8 +#> 428 virginica Petal Length 4.9 +#> 429 virginica Petal Length 5.6 +#> 430 virginica Petal Length 5.8 +#> 431 virginica Petal Length 6.1 +#> 432 virginica Petal Length 6.4 +#> 433 virginica Petal Length 5.6 +#> 434 virginica Petal Length 5.1 +#> 435 virginica Petal Length 5.6 +#> 436 virginica Petal Length 6.1 +#> 437 virginica Petal Length 5.6 +#> 438 virginica Petal Length 5.5 +#> 439 virginica Petal Length 4.8 +#> 440 virginica Petal Length 5.4 +#> 441 virginica Petal Length 5.6 +#> 442 virginica Petal Length 5.1 +#> 443 virginica Petal Length 5.1 +#> 444 virginica Petal Length 5.9 +#> 445 virginica Petal Length 5.7 +#> 446 virginica Petal Length 5.2 +#> 447 virginica Petal Length 5.0 +#> 448 virginica Petal Length 5.2 +#> 449 virginica Petal Length 5.4 +#> 450 virginica Petal Length 5.1 +#> 451 setosa Petal Width 0.2 +#> 452 setosa Petal Width 0.2 +#> 453 setosa Petal Width 0.2 +#> 454 setosa Petal Width 0.2 +#> 455 setosa Petal Width 0.2 +#> 456 setosa Petal Width 0.4 +#> 457 setosa Petal Width 0.3 +#> 458 setosa Petal Width 0.2 +#> 459 setosa Petal Width 0.2 +#> 460 setosa Petal Width 0.1 +#> 461 setosa Petal Width 0.2 +#> 462 setosa Petal Width 0.2 +#> 463 setosa Petal Width 0.1 +#> 464 setosa Petal Width 0.1 +#> 465 setosa Petal Width 0.2 +#> 466 setosa Petal Width 0.4 +#> 467 setosa Petal Width 0.4 +#> 468 setosa Petal Width 0.3 +#> 469 setosa Petal Width 0.3 +#> 470 setosa Petal Width 0.3 +#> 471 setosa Petal Width 0.2 +#> 472 setosa Petal Width 0.4 +#> 473 setosa Petal Width 0.2 +#> 474 setosa Petal Width 0.5 +#> 475 setosa Petal Width 0.2 +#> 476 setosa Petal Width 0.2 +#> 477 setosa Petal Width 0.4 +#> 478 setosa Petal Width 0.2 +#> 479 setosa Petal Width 0.2 +#> 480 setosa Petal Width 0.2 +#> 481 setosa Petal Width 0.2 +#> 482 setosa Petal Width 0.4 +#> 483 setosa Petal Width 0.1 +#> 484 setosa Petal Width 0.2 +#> 485 setosa Petal Width 0.2 +#> 486 setosa Petal Width 0.2 +#> 487 setosa Petal Width 0.2 +#> 488 setosa Petal Width 0.1 +#> 489 setosa Petal Width 0.2 +#> 490 setosa Petal Width 0.2 +#> 491 setosa Petal Width 0.3 +#> 492 setosa Petal Width 0.3 +#> 493 setosa Petal Width 0.2 +#> 494 setosa Petal Width 0.6 +#> 495 setosa Petal Width 0.4 +#> 496 setosa Petal Width 0.3 +#> 497 setosa Petal Width 0.2 +#> 498 setosa Petal Width 0.2 +#> 499 setosa Petal Width 0.2 +#> 500 setosa Petal Width 0.2 +#> 501 versicolor Petal Width 1.4 +#> 502 versicolor Petal Width 1.5 +#> 503 versicolor Petal Width 1.5 +#> 504 versicolor Petal Width 1.3 +#> 505 versicolor Petal Width 1.5 +#> 506 versicolor Petal Width 1.3 +#> 507 versicolor Petal Width 1.6 +#> 508 versicolor Petal Width 1.0 +#> 509 versicolor Petal Width 1.3 +#> 510 versicolor Petal Width 1.4 +#> 511 versicolor Petal Width 1.0 +#> 512 versicolor Petal Width 1.5 +#> 513 versicolor Petal Width 1.0 +#> 514 versicolor Petal Width 1.4 +#> 515 versicolor Petal Width 1.3 +#> 516 versicolor Petal Width 1.4 +#> 517 versicolor Petal Width 1.5 +#> 518 versicolor Petal Width 1.0 +#> 519 versicolor Petal Width 1.5 +#> 520 versicolor Petal Width 1.1 +#> 521 versicolor Petal Width 1.8 +#> 522 versicolor Petal Width 1.3 +#> 523 versicolor Petal Width 1.5 +#> 524 versicolor Petal Width 1.2 +#> 525 versicolor Petal Width 1.3 +#> 526 versicolor Petal Width 1.4 +#> 527 versicolor Petal Width 1.4 +#> 528 versicolor Petal Width 1.7 +#> 529 versicolor Petal Width 1.5 +#> 530 versicolor Petal Width 1.0 +#> 531 versicolor Petal Width 1.1 +#> 532 versicolor Petal Width 1.0 +#> 533 versicolor Petal Width 1.2 +#> 534 versicolor Petal Width 1.6 +#> 535 versicolor Petal Width 1.5 +#> 536 versicolor Petal Width 1.6 +#> 537 versicolor Petal Width 1.5 +#> 538 versicolor Petal Width 1.3 +#> 539 versicolor Petal Width 1.3 +#> 540 versicolor Petal Width 1.3 +#> 541 versicolor Petal Width 1.2 +#> 542 versicolor Petal Width 1.4 +#> 543 versicolor Petal Width 1.2 +#> 544 versicolor Petal Width 1.0 +#> 545 versicolor Petal Width 1.3 +#> 546 versicolor Petal Width 1.2 +#> 547 versicolor Petal Width 1.3 +#> 548 versicolor Petal Width 1.3 +#> 549 versicolor Petal Width 1.1 +#> 550 versicolor Petal Width 1.3 +#> 551 virginica Petal Width 2.5 +#> 552 virginica Petal Width 1.9 +#> 553 virginica Petal Width 2.1 +#> 554 virginica Petal Width 1.8 +#> 555 virginica Petal Width 2.2 +#> 556 virginica Petal Width 2.1 +#> 557 virginica Petal Width 1.7 +#> 558 virginica Petal Width 1.8 +#> 559 virginica Petal Width 1.8 +#> 560 virginica Petal Width 2.5 +#> 561 virginica Petal Width 2.0 +#> 562 virginica Petal Width 1.9 +#> 563 virginica Petal Width 2.1 +#> 564 virginica Petal Width 2.0 +#> 565 virginica Petal Width 2.4 +#> 566 virginica Petal Width 2.3 +#> 567 virginica Petal Width 1.8 +#> 568 virginica Petal Width 2.2 +#> 569 virginica Petal Width 2.3 +#> 570 virginica Petal Width 1.5 +#> 571 virginica Petal Width 2.3 +#> 572 virginica Petal Width 2.0 +#> 573 virginica Petal Width 2.0 +#> 574 virginica Petal Width 1.8 +#> 575 virginica Petal Width 2.1 +#> 576 virginica Petal Width 1.8 +#> 577 virginica Petal Width 1.8 +#> 578 virginica Petal Width 1.8 +#> 579 virginica Petal Width 2.1 +#> 580 virginica Petal Width 1.6 +#> 581 virginica Petal Width 1.9 +#> 582 virginica Petal Width 2.0 +#> 583 virginica Petal Width 2.2 +#> 584 virginica Petal Width 1.5 +#> 585 virginica Petal Width 1.4 +#> 586 virginica Petal Width 2.3 +#> 587 virginica Petal Width 2.4 +#> 588 virginica Petal Width 1.8 +#> 589 virginica Petal Width 1.8 +#> 590 virginica Petal Width 2.1 +#> 591 virginica Petal Width 2.4 +#> 592 virginica Petal Width 2.3 +#> 593 virginica Petal Width 1.9 +#> 594 virginica Petal Width 2.3 +#> 595 virginica Petal Width 2.5 +#> 596 virginica Petal Width 2.3 +#> 597 virginica Petal Width 1.9 +#> 598 virginica Petal Width 2.0 +#> 599 virginica Petal Width 2.3 +#> 600 virginica Petal Width 1.8