From 514bb81f6f674d2c0123eac111e029caff8dd54b Mon Sep 17 00:00:00 2001 From: debruine Date: Mon, 6 May 2019 20:20:58 +0100 Subject: [PATCH] plots and increased design spec consistency --- DESCRIPTION | 10 +- NAMESPACE | 1 + NEWS.md | 9 +- R/check_design.R | 193 ++------- R/get_design_long.R | 112 +++++ R/plot_design.R | 66 +++ R/rnorm_multi.R | 6 + R/sim_design.R | 48 ++- README.Rmd | 22 +- .../plot-iris-sim-1.png | Bin 64496 -> 0 bytes docs/LICENSE-text.html | 2 +- docs/articles/index.html | 2 +- docs/articles/rnorm_multi.html | 4 +- docs/articles/sim_design.html | 133 +++--- .../figure-html/unnamed-chunk-1-1.png | Bin 0 -> 47659 bytes .../figure-html/unnamed-chunk-17-1.png | Bin 205039 -> 204135 bytes .../figure-html/unnamed-chunk-3-1.png | Bin 103547 -> 102241 bytes docs/articles/sim_df.html | 4 +- docs/authors.html | 2 +- docs/index.html | 45 +- .../figure-html/plot-iris-sim-1.png | Bin 147098 -> 150498 bytes .../figure-html/unnamed-chunk-1-1.png | Bin 0 -> 47659 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 0 -> 56902 bytes docs/news/index.html | 18 +- docs/reference/cell_combos.html | 172 ++++++++ docs/reference/check_design-1.png | Bin 0 -> 51983 bytes docs/reference/check_design.html | 92 +--- docs/reference/check_sim_stats.html | 32 +- docs/reference/convert_param.html | 2 +- docs/reference/cormat.html | 2 +- docs/reference/cormat_from_triangle.html | 2 +- docs/reference/faceratings.html | 2 +- docs/reference/fix_name_labels.html | 2 +- docs/reference/get_design_long.html | 2 +- docs/reference/index.html | 8 +- docs/reference/is_pos_def.html | 2 +- docs/reference/long2wide-1.png | Bin 0 -> 48455 bytes docs/reference/long2wide.html | 30 +- docs/reference/make_id.html | 2 +- docs/reference/pipe.html | 2 +- docs/reference/plot_design-1.png | Bin 0 -> 51983 bytes docs/reference/plot_design-2.png | Bin 0 -> 51983 bytes docs/reference/plot_design.html | 188 ++++++++ docs/reference/pos_def_limits.html | 2 +- docs/reference/rnorm_multi.html | 404 +++++++++--------- docs/reference/rnorm_pre.html | 2 +- docs/reference/select_num_grp.html | 30 +- docs/reference/sim_design.html | 8 +- docs/reference/sim_design_.html | 2 +- docs/reference/sim_df.html | 2 +- docs/reference/sim_mixed_df.html | 204 ++++----- docs/reference/wide2long.html | 353 ++++++++++++++- man/cell_combos.Rd | 18 + man/check_design.Rd | 4 +- man/get_design_long.Rd | 2 +- man/plot_design.Rd | 28 ++ man/sim_design.Rd | 4 +- tests/testthat/test-check_design.R | 117 +++-- tests/testthat/test-check_sim_stats.R | 32 -- tests/testthat/test-convert_param.R | 84 ++-- tests/testthat/test-get_design_long.R | 72 ++++ tests/testthat/test-plot_design.R | 47 ++ tests/testthat/test-sim_design.R | 37 +- vignettes/sim_design.Rmd | 11 +- 64 files changed, 1842 insertions(+), 838 deletions(-) create mode 100644 R/get_design_long.R create mode 100644 R/plot_design.R delete mode 100644 README_files/figure-markdown_github/plot-iris-sim-1.png create mode 100644 docs/articles/sim_design_files/figure-html/unnamed-chunk-1-1.png create mode 100644 docs/index_files/figure-html/unnamed-chunk-1-1.png create mode 100644 docs/index_files/figure-html/unnamed-chunk-7-1.png create mode 100644 docs/reference/cell_combos.html create mode 100644 docs/reference/check_design-1.png create mode 100644 docs/reference/long2wide-1.png create mode 100644 docs/reference/plot_design-1.png create mode 100644 docs/reference/plot_design-2.png create mode 100644 docs/reference/plot_design.html create mode 100644 man/cell_combos.Rd create mode 100644 man/plot_design.Rd create mode 100644 tests/testthat/test-get_design_long.R create mode 100644 tests/testthat/test-plot_design.R 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 819df6bef26074a8031dfbe38ba3428f7e0859b0..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 64496 zcmd43bySpH8$LP=LrW;#LpLZQA>AmcbO|USozgHgNFyaBASo@~DKIqB-QC@B9`yZw z=Y7xl`>b`=nuRgX?AiN{>$>iH@6Bf=1!+ul5_Avcac0Tmhe?3W|F4+f01PQW4`UR#DBB!grA!pJ3AqVDG#Bi$6o)>R*)0-Tfy-O{uk|M{4+ETtR zTg$}=ia8Wb?kHO5LC?nOk~Pk}XWfB}F|JMvk1BODxWQyf2|sFpZMen3`Ew5lrQ|?#F%1 zy2KVVz_Z@1+%l1$7u}xR;`poTh1Ss#Kcx9{t#;yJr`@%s^0%Y))vXtaJ#H=%H&^v8 zGG`ukseHSe?i@=EN(+BM#O}Hzp)qZx%RuS)S0>(%2ev2Z!tV zURL*)GaCj9`4E(n+=Lep3>zKh`|Etb74~H10SnU;L!V6g4!q-hbrxmIi9qcVEFgat zO;(C)xPLYA+CuW30MFQMedzPI*-4VKeC2g%`uES7<#d$C3s|u#1891fu90*bZaVaS 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z3Oj<+!MgTc)Y(||z=mqgf9**VPs=4}ZEZc;UXnVRfBHhO{XKkLXo1|~#U1n^V3NfgMRfr53BQEH(9p2$-bLhzmO+J_d~oxy+JJvoflJYg%GWLJJSG7q7Pe_{PcqFNx582S@~3m2Sen#_``3764R+ zd*7q49RHr+PacdNJckJ6_M2py|D#gSa^;xx 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
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zZTGZ-GWRSGnEkG0ESAvcQI<>pZ`*IMB31l|KkL`Xp3!w*IYylYwi-DPM z!^Qha;z%V7A}2C3UDZh7X~^31dfiO8=z)5X4B!yMt4m8y*V}a6MKV}pxx?bn)xnm= z1v!XxgorUvfw|xl(7~biV){^p5yAZi*r+?@r^AfHfPhM19-L}ET^XLqQ5hP$feo8Y z!l&T|qA^0^fNjF3XP@HmF3TxBV3Y365hFx7hZLH?AeagauYk&8OYFhT(6|ZMqSLV6 q$b+aHkb@i84q`Q72pP)W{~>=hnXxxWRw$PN2s~Z=T-G@yGywo2RF{AN 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") 
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@@ -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 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    +
    + + + +
    + +
    +
    + + +
    + +

    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

    + + +
    + +
    + +
    + + +
    +

    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 0000000000000000000000000000000000000000..6ae7c014d6fbb33853e7bd438359be4d99f3504e GIT binary patch literal 51983 zcmeFZbzIcl_BM>Pv`V)kAX3suD*{TYNViDG&<(>#sg#Ozi-3~S9RiZl$k5$2bi+J* z&bjY%&iy;{`}=*~=X3kleEc%s*?a9(`&!ppZ=b3v5EIZ6U|?VnKY9G; zB6&+gg+KG11JZG+Qr>6+*0CYxJ5r^`0Z&&#v)1Fq1>&u$i>A!BeD;zNDc)OE76_b` zsvPZ@59C)U6AHMg_~)EMBHu`Iz7GsZqry$sdd9$M@J$(0SdGxVOmAAY$=hi3_vB@2 zeuX>(1+yd5%aWz9gl{6RY-ek9xn1!({lFx>rptKva;1V#riIt5a@G{y>@4LMZe_l} zH8=PMM?>BEO)OdDub;>AL}z^8hmF!ytk-i^7C*mCbe&8qazL0|rrQ0m4BE46&hCv* zl?p_d=$~;4u`bU(Yf;Mim@0pcKWAg-TTVYS9+UXtnl$eFiK&FbtZRrz`g!AcIrXVu z^2^@c^ji)T-s76%e$y+m+^PO!BsVAOXVMS1mI@)=-Cc3)#<$ga2|I00XLjl-yUUAf zLJ3{oo{!E?Yds$y`Z}fF-(2-s*$a%}#yii9{S-ja67U9^F8|Vjm00}NXc|JMKJEL@SD!S!HiCwYM z`0gL|O`k5?+l>NFDtk)rz6z0gO<7EP9+5{mXtt9Zq;Gmd+K)>FQ<{y2`TA%;uOa`F z!w-)9S8jQ}lTbN~qA$Mj3UNB3Xe(zZAu@bX8z%5PD^YIperZjbF;d{Jvc5VzkBg){ 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    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()

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    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
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zf8n`X)U+snj3z`$PglBseoL&zs);AJ1K;)Bpeg literal 0 HcmV?d00001 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() 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()
    +
    +
    + +
    + +
    + + +
    +

    Site built with pkgdown 1.3.0.

    +
    +
    +
    + + + + + + 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