├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R ├── misc.R ├── sim_data.R └── stim_lists.R ├── README.Rmd ├── docs ├── LICENSE-text.html ├── authors.html ├── docsearch.css ├── docsearch.js ├── index.html ├── link.svg ├── pkgdown.css ├── pkgdown.js ├── pkgdown.yml └── reference │ ├── compose_data.html │ ├── contr.dev.html │ ├── design_formula.html │ ├── fac_counts.html │ ├── gen_pop.html │ ├── index.html │ ├── sim_norm.html │ ├── stim_lists.html │ ├── term_names.html │ ├── trial_lists.html │ └── with_dev_pred.html └── man ├── compose_data.Rd ├── contr.dev.Rd ├── design_formula.Rd ├── fac_counts.Rd ├── gen_pop.Rd ├── sim_norm.Rd ├── stim_lists.Rd ├── term_names.Rd ├── trial_lists.Rd └── with_dev_pred.Rd /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: funfact 2 | Title: Functions for factorial experiments 3 | Description: Provides functionality for planning and analyzing 4 | factorially-designed experiments involving sampling of subjects 5 | and stimulus materials (items). 6 | Version: 0.300 7 | License: GPL-2 8 | Author: Dale J. Barr 9 | Maintainer: Dale J. Barr 10 | Imports: clusterGeneration, MASS 11 | RoxygenNote: 6.1.1 12 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 2, June 1991 3 | 4 | Copyright (C) 1989, 1991 Free Software Foundation, Inc., 5 | 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA 6 | Everyone is permitted to copy and distribute verbatim copies 7 | of this license document, but changing it is not allowed. 8 | 9 | Preamble 10 | 11 | The licenses for most software are designed to take away your 12 | freedom to share and change it. By contrast, the GNU General Public 13 | License is intended to guarantee your freedom to share and change free 14 | software--to make sure the software is free for all its users. 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If this is what you want to do, use the GNU Lesser General 339 | Public License instead of this License. 340 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export(compose_data) 4 | export(contr.dev) 5 | export(design_formula) 6 | export(fac_counts) 7 | export(gen_pop) 8 | export(sim_norm) 9 | export(stim_lists) 10 | export(term_names) 11 | export(trial_lists) 12 | export(with_dev_pred) 13 | importFrom(MASS,mvrnorm) 14 | importFrom(clusterGeneration,genPositiveDefMat) 15 | -------------------------------------------------------------------------------- /R/misc.R: -------------------------------------------------------------------------------- 1 | #' Deviation-Coded Contrast Matrices 2 | #' 3 | #' Return a matrix of deviation-coded contrasts. 4 | #' 5 | #' @param n a vector of levels for a factor, or the number of levels. 6 | #' @param base an integer specifying which group is considered the 7 | #' baseline group. Ignored if 'contrasts' is \code{FALSE}. 8 | #' @param contrasts a logical indicating whether contrasts should be computed. 9 | #' 10 | #' @export 11 | contr.dev <- function(n, base = 1, contrasts = TRUE) { 12 | if (length(n) <= 1L) { 13 | if (is.numeric(n) && (length(n) == 1L) && (n > 1L)) 14 | levels <- seq_len(n) 15 | else stop("not enough degrees of freedom to define contrasts") 16 | } else { 17 | levels <- n 18 | } 19 | ctreat <- contr.treatment(levels, base, contrasts) 20 | mx <- apply(ctreat, 2, scale, scale = FALSE) 21 | dimnames(mx) <- dimnames(ctreat) 22 | mx 23 | } 24 | 25 | #' Calculate marginal and cell counts for factorially-designed data 26 | #' 27 | #' @param iv_names Names of independent variables in data.frame given by \code{dat}. 28 | #' @param dat A data frame 29 | #' @param unit_names Names of the fields containing sampling units (subjects, items) 30 | #' @return a list, the elements of which have the marginal/cell counts for each factor in the design 31 | #' @export 32 | fac_counts <- function(iv_names, dat, unit_names = c("subj_id", "item_id")) { 33 | fac_info <- attr(terms(as.formula(paste0("~", paste(iv_names, collapse = "*")))), "factors") 34 | rfx <- sapply(unit_names, function(this_unit) { 35 | ## figure out how many things are replicated by unit, how many times 36 | rep_mx <- xtabs(paste0("~", paste(iv_names, collapse = "+"), "+", this_unit), dat) 37 | lvec <- lapply(colnames(fac_info), function(cx) { 38 | x <- fac_info[, cx, drop = FALSE] 39 | ix <- seq_along(x)[as.logical(x)] 40 | ## create margin table 41 | marg_mx <- apply(rep_mx, c(ix, length(dim(rep_mx))), sum) 42 | mmx <- apply(marg_mx, length(dim(marg_mx)), c) 43 | }) 44 | names(lvec) <- colnames(fac_info) 45 | return(lvec) 46 | ## lvec <- apply(fac_info, 2, function(x) { 47 | ## ix <- seq_along(x)[as.logical(x)] 48 | ## ## create margin table 49 | ## marg_mx <- apply(rep_mx, c(ix, length(dim(rep_mx))), sum) 50 | ## mmx <- apply(marg_mx, length(dim(marg_mx)), c) 51 | ## }) 52 | }, simplify = FALSE) 53 | return(rfx) 54 | } 55 | 56 | #' Generate numerical deviation-coded predictors 57 | #' 58 | #' Add deviation-coded predictors to data frame. 59 | #' 60 | #' @param dat A data frame with columns containing factors to be converted. 61 | #' @param iv_names Names of the variables to be converted. 62 | #' 63 | #' @return A data frame including additional deviation coded predictors. 64 | #' 65 | #' @examples 66 | #' with_dev_pred(stim_lists(list(ivs = c(A = 3))), "A") 67 | #' @export 68 | with_dev_pred <- function(dat, iv_names = NULL) { 69 | if (is.null(iv_names)) { 70 | iv_names <- names(dat) 71 | } else {} 72 | mform <- as.formula(paste0("~", paste(iv_names, collapse = "+"))) 73 | 74 | cont <- lapply(iv_names, function(.x) { 75 | if (is.factor(dat[[.x]])) { 76 | contr.dev(levels(dat[[.x]])) 77 | } else { 78 | contr.dev(sort(unique(dat[[.x]]))) 79 | } 80 | }) 81 | names(cont) <- iv_names 82 | 83 | mmx <- model.matrix(mform, dat, contrasts.arg = cont)[, -1, drop = FALSE] 84 | cbind(dat, mmx) 85 | } 86 | 87 | check_design_args <- function(design_args) { 88 | ## TODO check integrity of design args 89 | required_elements <- c("ivs") 90 | missing_elements <- setdiff(required_elements, names(design_args)) 91 | if (length(missing_elements) > 0) { 92 | stop("'design_args' missing element(s): ", 93 | paste(missing_elements, collapse = ", ")) 94 | } else {} 95 | return(TRUE) 96 | } 97 | 98 | #' Get names for predictors in factorial design 99 | #' 100 | #' Get the names for the numerical predictors corresponding to all 101 | #' main effects and interactions of categorical IVs in a factorial 102 | #' design. 103 | #' 104 | #' @param design_args A list with experimental design information (see \code{link{stim_lists}}) 105 | #' @param design_formula A formula (default NULL, constructs from \code{design_args}) 106 | #' @param contr_type Name of formula for generating contrasts (default "contr.dev") 107 | #' @return A character vector with names of all the terms 108 | #' @export 109 | term_names <- function(design_args, 110 | design_formula = NULL, 111 | contr_type = "contr.dev") { 112 | check_design_args(design_args) 113 | plists <- stim_lists(design_args) 114 | 115 | cont <- lapply(design_args[["ivs"]], function(.x) { 116 | do.call(contr_type, list(.x)) 117 | }) 118 | 119 | names(cont) <- names(design_args[["ivs"]]) 120 | if (is.null(design_formula)) 121 | design_formula <- as.formula(paste0("~", 122 | paste(names(design_args[["ivs"]]), 123 | collapse = " * "))) 124 | suppressWarnings(mmx <- model.matrix(design_formula, plists, 125 | contrasts.arg = cont)) 126 | return(colnames(mmx)) 127 | } 128 | 129 | #' Get the GLM formula for a factorially-designed experiment 130 | #' 131 | #' Get the formula corresponding to the general linear model for a 132 | #' factorially designed experiment, with maximal random effects. 133 | #' 134 | #' @param design_args A list with experimental design information (see \code{link{stim_lists}}) 135 | #' @param n_subj Number of subjects 136 | #' @param dv_name Name of dependent variable; \code{NULL} (default) for one-sided formula 137 | #' @param lme4_format Do you want the results combined as the model formula for a \code{lme4} model? (default \code{TRUE}) 138 | #' @return A formula, character string, or list, depending 139 | #' @export 140 | design_formula <- function(design_args, 141 | n_subj = NULL, 142 | dv_name = NULL, 143 | lme4_format = TRUE) { 144 | iv_names <- names(design_args[["ivs"]]) 145 | 146 | fixed <- paste(iv_names, collapse = " * ") 147 | 148 | fac_cnts <- fac_counts(iv_names, trial_lists(design_args, subjects = n_subj)) 149 | fac_info <- attr(terms(as.formula(paste0("~", paste(iv_names, collapse = "*")))), "factors") 150 | 151 | ## figure out whether data are multilevel 152 | is_unit_ml <- sapply(fac_cnts, function(lx) { 153 | is_multilevel <- FALSE 154 | ## first condition: more than one subject? (item?) 155 | ranfac_has_multiple_levels <- any(sapply(lx, ncol) > 1) 156 | if (ranfac_has_multiple_levels) { 157 | is_fac_multilevel <- sapply(lx, function(fx) { 158 | any(apply(fx, 2, function(.x) any(.x > 1L))) 159 | }) 160 | is_multilevel <- any(is_fac_multilevel) 161 | } 162 | is_multilevel 163 | }) 164 | 165 | if (!any(is_unit_ml)) { 166 | ## data are not multilevel 167 | rfx <- NULL 168 | } else { 169 | ## data are multilevel 170 | rfx <- lapply(fac_cnts[is_unit_ml], function(lx) { 171 | lvec <- sapply(lx, function(mx) { 172 | as.logical(prod(apply(mx, 2, function(xx) all(xx > 1)))) 173 | }) 174 | res <- fac_info[, lvec, drop = FALSE] 175 | keep_term <- rep(TRUE, ncol(res)) 176 | ## try to simplify the formula 177 | for (cx in rev(seq_len(ncol(res))[-1])) { 178 | drop_term <- sapply(seq_len(cx - 1), function(ccx) { 179 | identical(as.logical(res[, ccx, drop = FALSE]) | as.logical(res[, cx, drop = FALSE]), 180 | as.logical(res[, cx, drop = FALSE])) 181 | }) 182 | keep_term[seq_len(cx - 1)] <- keep_term[seq_len(cx - 1)] & (!drop_term) 183 | } 184 | fterms <- apply(res[, keep_term, drop = FALSE], 2, function(llx) { 185 | paste(names(llx)[as.logical(llx)], collapse = " * ") 186 | }) 187 | need_int <- any(apply(lx[[1]], 2, sum) > 1) 188 | fterms2 <- if (need_int) c("1", fterms) else fterms 189 | paste(fterms2, collapse = " + ") 190 | }) 191 | } 192 | 193 | form_list <- c(list(fixed = fixed), rfx) 194 | 195 | if (lme4_format) { 196 | form_str <- paste0(dv_name, " ~ ", form_list[["fixed"]]) 197 | if (length(form_list) > 1L) { 198 | form_str <- paste0(form_str, " + ", 199 | paste(sapply(names(form_list[-1]), 200 | function(nx) paste0("(", rfx[[nx]], " | ", nx, ")")), 201 | collapse = " + ")) 202 | } 203 | result <- as.formula(form_str) 204 | } else { 205 | result <- lapply(form_list, function(x) formula(paste0("~", x))) 206 | } 207 | 208 | return(result) 209 | } 210 | -------------------------------------------------------------------------------- /R/sim_data.R: -------------------------------------------------------------------------------- 1 | #' Generate population for data simulation 2 | #' 3 | #' @param design_args list specifying experimental design (see 4 | #' \code{\link{stim_lists}}); for best results, n_item should be 5 | #' 2*minimum number of items needed for the design 6 | #' @param n_subj number of subjects (for defining random effects 7 | #' structure; for best results, should be 2*number of stimulus lists 8 | #' @param fixed_ranges list of 2-element vectors (min, max) defining 9 | #' ranges of fixed effect parameters 10 | #' @param var_range 2-element vector defining range (min, max) of 11 | #' random effect variances 12 | #' @param err_range 2-element vector defining range of error variance 13 | #' @return list with parameters for data generation 14 | #' @seealso \code{\link{sim_norm}} 15 | #' @examples 16 | #' 17 | #' dargs <- list(ivs = c(A = 2, B = 2), n_item = 8) 18 | #' gen_pop(dargs, 8) 19 | #' @importFrom clusterGeneration genPositiveDefMat 20 | #' @export 21 | gen_pop <- function(design_args, 22 | n_subj = NULL, 23 | fixed_ranges = NULL, 24 | var_range = c(0, 3), 25 | err_range = c(0, 6)) { 26 | tdat <- trial_lists(design_args, n_subj) 27 | if ("n_rep" %in% colnames(tdat)) { 28 | design_args[["ivs"]] <- c(as.list(design_args[["ivs"]]), list(n_rep = unique(tdat[["n_rep"]]))) 29 | } else {} 30 | forms <- design_formula(design_args, n_subj, lme4_format = FALSE) 31 | tnames <- lapply(forms, function(x) term_names(design_args, x)) 32 | err_var <- runif(1, err_range[1], err_range[2]) 33 | ## generate random effects 34 | rfx <- lapply(tnames[-1], function(x) { 35 | mx <- clusterGeneration::genPositiveDefMat(length(x), 36 | covMethod = "onion", 37 | rangeVar = var_range)$Sigma 38 | dimnames(mx) <- list(x, x) 39 | return(mx) 40 | }) 41 | ## generate fixed effects 42 | if (!is.null(fixed_ranges)) { 43 | if (length(fixed_ranges) != length(tnames[["fixed"]])) { 44 | stop("fixed_ranges must have ", length(tnames[["fixed"]]), 45 | " elements, corresponding to variables ", 46 | paste(tnames[["fixed"]], collapse = ", ")) 47 | } else {} 48 | } else { 49 | fixed_ranges <- lapply(seq_along(tnames[["fixed"]]), function(x) {c(0, 3)}) 50 | names(fixed_ranges) <- tnames[["fixed"]] 51 | } 52 | if (!is.null(names(fixed_ranges)) && 53 | !identical(names(fixed_ranges), tnames[["fixed"]])) { 54 | warning("names of 'fixed_ranges' elements do not match variable names: ", 55 | paste(tnames[["fixed"]], collapse = ", ")) 56 | } else {} 57 | return(list(fixed = sapply(fixed_ranges, function(x) runif(1, x[1], x[2])), 58 | subj_rfx = rfx[["subj_id"]], 59 | item_rfx = rfx[["item_id"]], 60 | err_var = err_var)) 61 | } 62 | 63 | #' Sample data from population with normal error variance 64 | #' 65 | #' @param design_args experiment design (see 66 | #' \code{\link{stim_lists}}); must contain sub-element \code{n_item} 67 | #' defining the number of items. 68 | #' @param n_subj number of subjects 69 | #' @param params list with parameters defining population 70 | #' (\code{fixed}, \code{subj_rfx}, \code{item_rfx}), and 71 | #' \code{err_var}; normally generated by a call to 72 | #' \code{\link{gen_pop}}. 73 | #' @param contr_type name of function defining IV contrast type; see 74 | #' \code{?contrasts} 75 | #' @param verbose whether to return explicit information about 76 | #' individual random effects and residual error 77 | #' 78 | #' @return A data frame with simulated data 79 | #' @seealso \code{\link{gen_pop}} 80 | #' @examples 81 | #' design_args <- list(ivs = c(A = 2, B = 3), n_item = 18) 82 | #' pop_params <- gen_pop(design_args, 12) 83 | #' 84 | #' dat <- sim_norm(design_args, 12, pop_params) 85 | #' @importFrom MASS mvrnorm 86 | #' @export 87 | sim_norm <- function(design_args, 88 | n_subj, 89 | params, 90 | contr_type = "contr.dev", 91 | verbose = FALSE) { 92 | required_elements <- c("fixed", "subj_rfx", "item_rfx", "err_var") 93 | missing_elements <- setdiff(required_elements, names(params)) 94 | if (length(missing_elements) > 0) { 95 | stop("mcr.data was missing element(s): ", 96 | paste(missing_elements, collapse = ", ")) 97 | } else {} 98 | if (is.null(design_args[["n_item"]])) { 99 | stop("'n_item' not specified in 'design_args'") 100 | } else {} 101 | rfx <- mapply(function(x, n) { 102 | if (!is.null(x)) { 103 | MASS::mvrnorm(n, mu = rep(0, ncol(x)), x) 104 | } else { 105 | n 106 | } 107 | }, params[c("subj_rfx", "item_rfx")], 108 | c(n_subj, design_args[["n_item"]]), SIMPLIFY = FALSE) 109 | dat <- compose_data(design_args, 110 | fixed = params[["fixed"]], 111 | subj_rmx = rfx[["subj_rfx"]], 112 | item_rmx = rfx[["item_rfx"]], 113 | contr_type = contr_type, verbose = verbose) 114 | dat[["err"]] <- rnorm(nrow(dat), sd = sqrt(params[["err_var"]])) 115 | dat[["Y"]] <- dat[["Y"]] + dat[["err"]] 116 | return(dat) 117 | } 118 | -------------------------------------------------------------------------------- /R/stim_lists.R: -------------------------------------------------------------------------------- 1 | #' Generate stimulus presentation lists 2 | #' 3 | #' Generates counterbalanced presentation lists for factorially 4 | #' designed experiments involving stimulus presentation 5 | #' 6 | #' @param design_args A list describing the experimental design, which 7 | #' must have an element \code{ivs}, giving a named list of independent 8 | #' variables, with each list element a vector giving the levels of 9 | #' that IV, or a single number stating the number of desired levels. 10 | #' If any IVs are administered between-subjects or between-items, 11 | #' these should be named in list elements \code{between_subj} and 12 | #' \code{between_item} respectively. The argument \code{design_args} 13 | #' also can optionally include the following two elements: 14 | #' \code{n_item}, the desired number of stimulus items, which if 15 | #' unspecified, will result in lists with the minimum possible number 16 | #' of items; and \code{n_rep}, the number of repetitions of each 17 | #' stimulus item for each participant (default 1). 18 | #' @param as_one boolean (default \code{TRUE}) specifying whether the 19 | #' presentation lists are to be returned as a single data frame or as 20 | #' elements in a list object 21 | #' 22 | #' @return a single \code{data.frame} (default) with each list 23 | #' identified by \code{list_id} or a \code{list} of dataframes, 24 | #' depending on the value of \code{as_one} 25 | #' 26 | #' @examples 27 | #' stim_lists(list(ivs = c(A = 2, B = 2))) # 2x2 within-subjects within-item 28 | #' 29 | #' stim_lists(list(ivs = c(A = 2, B = 2, n_item = 16))) # same but w/more items 30 | #' 31 | #' stim_lists(list(ivs = c(A = 2, B = 2, n_item = 16, n_rep = 3))) 32 | #' 33 | #' # mixed by subject, fully within by item 34 | #' stim_lists(list(ivs = list(group = c("adult", "child"), 35 | #' task = c("easy", "hard")), 36 | #' between_subj = "group", 37 | #' n_item = 12)) 38 | #' 39 | #' # mixed by subject, mixed by item 40 | #' stim_lists(list(ivs = c(A = 2, B = 2), 41 | #' between_subj = "A", 42 | #' between_item = "B")) 43 | #' @export 44 | stim_lists <- function(design_args, 45 | as_one = TRUE) { 46 | fac_combine_levels <- function(vars, iv_list, dframe = TRUE) { 47 | row_indices <- rev(do.call("expand.grid", 48 | lapply(rev(vars), 49 | function(x) seq_along(iv_list[[x]])))) 50 | res <- mapply(function(x, y) x[y], iv_list[vars], row_indices) 51 | if (dframe) { 52 | as.data.frame(res, stringsAsFactors = FALSE) 53 | } else { 54 | res 55 | } 56 | } 57 | 58 | rotate_cells <- function(x, combine = FALSE) { 59 | res <- lapply(seq_len(nrow(x)), 60 | function(ix) x[c(ix:nrow(x), seq_len(ix - 1)), , drop = FALSE]) 61 | if (combine) { 62 | do.call("rbind", res) 63 | } else { 64 | res 65 | } 66 | } 67 | 68 | bs_combine <- function(dat, plists) { 69 | ## factorially combine across lists for between subject variables 70 | if (nrow(dat) == 0) { 71 | return(plists) 72 | } else {} 73 | res <- c(lapply(seq_len(nrow(dat)), function(rx) { 74 | if (length(plists) > 0) { 75 | lapply(plists, function(lx) { 76 | cbind(dat[rep(rx, nrow(lx)), , drop = FALSE], lx) 77 | }) 78 | } else { 79 | dat[rx, , drop = FALSE] 80 | } 81 | })) 82 | if (length(plists) > 0) { 83 | do.call("c", res) 84 | } else { 85 | res 86 | } 87 | } 88 | 89 | check_design_args(design_args) 90 | iv_names <- names(design_args[["ivs"]]) 91 | ## check whether elements of 'ivs' are numbers and convert to char vector 92 | ivs2 <- lapply(iv_names, function(nx) { 93 | x <- design_args[["ivs"]][[nx]] 94 | if ((length(x) == 1) && is.numeric(x)) { 95 | paste0(nx, seq_len(x)) 96 | } else {x} 97 | }) 98 | names(ivs2) <- iv_names 99 | 100 | item_within <- setdiff(names(ivs2), design_args[["between_item"]]) 101 | subj_within <- setdiff(names(ivs2), design_args[["between_subj"]]) 102 | ## iv_levels <- sapply(ivs2, length) # IS THIS NEEDED? 103 | 104 | ww_fac <- intersect(item_within, subj_within) 105 | 106 | ww_chunks <- rotate_cells(fac_combine_levels(intersect(item_within, subj_within), 107 | ivs2)) 108 | 109 | wb_chunks <- fac_combine_levels(intersect(subj_within, design_args[["between_item"]]), ivs2) 110 | 111 | ## combine the WSWI and WSBI chunks to create the base presentation lists 112 | if (nrow(wb_chunks) > 0) { 113 | if (length(ww_chunks) > 0) { 114 | base_plists <- lapply(ww_chunks, function(ww) { 115 | cbind(wb_chunks[rep(seq_len(nrow(wb_chunks)), each = nrow(ww)), , drop = FALSE], ww) 116 | }) 117 | } else { 118 | base_plists <- list(wb_chunks) 119 | } 120 | } else { 121 | base_plists <- ww_chunks 122 | } 123 | 124 | ## handle BSWI 125 | bswi <- fac_combine_levels(intersect(design_args[["between_subj"]], 126 | item_within), ivs2) 127 | bswi_lists <- bs_combine(bswi, base_plists) 128 | 129 | ## handle bsbi factors (if they exist) 130 | bsbi <- fac_combine_levels(intersect(design_args[["between_subj"]], 131 | design_args[["between_item"]]), ivs2) 132 | bsbi_lists <- bs_combine(bsbi, bswi_lists) 133 | div_fac <- if (nrow(bsbi)) nrow(bsbi) else 1 134 | n_item <- design_args[["n_item"]] 135 | if (is.null(design_args[["n_item"]])) { # dynamically choose minimum n_item 136 | if (length(bswi_lists) > 0) { 137 | n_item <- nrow(bswi_lists[[1]]) * div_fac 138 | } else { 139 | n_item <- div_fac 140 | } 141 | } else {} 142 | if (length(bswi_lists) > 0) { 143 | item_fac <- div_fac * nrow(bswi_lists[[1]]) 144 | if ((n_item %% item_fac) != 0) { 145 | stop("n_item must be a factor of ", item_fac) 146 | } else {} 147 | } else {} 148 | 149 | if (length(bsbi_lists) == 0) { 150 | bsbi_lists <- bswi_lists 151 | } else {} 152 | 153 | if ((n_item %% div_fac) != 0) stop("n_item must be a multiple of ", div_fac) 154 | 155 | rep_times <- if (length(bswi_lists) > 0) length(bswi_lists) else 1 156 | it_chunks <- rep(seq_len(div_fac), each = rep_times) 157 | it_lists <- split(seq_len(n_item), 158 | rep(seq_len(div_fac), each = n_item / div_fac))[it_chunks] 159 | 160 | plists <- mapply(function(x, y) { 161 | ix <- rep(seq_len(nrow(y)), each = length(x) / nrow(y)) 162 | cbind(item_id = x, y[ix, , drop = FALSE]) 163 | }, 164 | it_lists, bsbi_lists, SIMPLIFY = FALSE) 165 | 166 | n_rep <- design_args[["n_rep"]] 167 | if (is.null(design_args[["n_rep"]])) n_rep <- 1 168 | 169 | if (n_rep > 1) { 170 | plists <- lapply(plists, function(x) { 171 | data.frame(n_rep = as.character(paste0("r", rep(seq_len(n_rep), each = nrow(x)))), 172 | x[rep(seq_len(nrow(x)), n_rep), , drop = FALSE], 173 | check.names = FALSE, stringsAsFactors = FALSE) 174 | }) 175 | } else {} 176 | 177 | if (as_one) { 178 | res <- mapply(function(x, y) { 179 | cbind(list_id = x, y) 180 | }, 181 | seq_along(plists), plists, SIMPLIFY = FALSE) 182 | final_lists <- do.call("rbind", res) 183 | } else { 184 | final_lists <- plists 185 | } 186 | rownames(final_lists) <- NULL 187 | return(final_lists) 188 | } 189 | 190 | #' Generate trial lists from a stimulus presentation lists 191 | #' 192 | #' Merge stimulus presentation lists with subject data to create a 193 | #' trial list. 194 | #' 195 | #' @param design_args Stimulus presentation lists (see \code{\link{stim_lists}}). 196 | #' @param subjects One of the following three: (1) an integer 197 | #' specifying the desired number of subjects (must be a multiple of 198 | #' number of stimulus lists); (2) a data frame with assignment 199 | #' information (must include a column \code{subj_id}); or (3) 200 | #' \code{NULL}, in which case there will be one subject per list. 201 | #' @param seq_assign If TRUE, assignment of subjects to lists will 202 | #' be sequential rather than random (default is FALSE) 203 | #' 204 | #' @return A data frame containing all trial information. 205 | #' @export 206 | trial_lists <- function(design_args, 207 | subjects = NULL, seq_assign = FALSE) { 208 | sp_lists <- stim_lists(design_args) 209 | sp2 <- split(sp_lists, sp_lists[["list_id"]]) 210 | if (is.null(subjects)) { 211 | subjects <- length(sp2) 212 | } else {} 213 | if (is.numeric(subjects)) { 214 | if ((subjects %% length(sp2)) != 0) { 215 | stop("'subjects' must be a multiple of number of lists (", 216 | length(sp2), ")") 217 | } else {} 218 | list_ord = rep(seq_along(sp2), subjects / length(sp2)) 219 | if (!seq_assign) { 220 | list_ord = sample(list_ord) # randomize assignment to lists 221 | } else {} 222 | subj_dat <- data.frame(subj_id = seq_len(subjects), 223 | list_id = list_ord) 224 | } else { 225 | if (!is.data.frame(subjects)) { 226 | stop("'subjects' must be an integer, data.frame, or NULL") 227 | } else {} 228 | subj_dat <- subjects 229 | if (any(!("subj_id" %in% names(subj_dat)), 230 | !("list_id" %in% names(subj_dat)))) { 231 | stop("'subjects' must contain fields 'subj_id', 'list_id'") 232 | } else {} 233 | } 234 | res <- lapply(seq_len(nrow(subj_dat)), function(rx) { 235 | cbind(subj_id = subj_dat[rx, "subj_id"], 236 | sp2[[subj_dat[rx, "list_id"]]]) 237 | }) 238 | res2 <- do.call("rbind", res) 239 | rownames(res2) <- NULL 240 | return(res2) 241 | } 242 | 243 | #' Compose response data from fixed and random effects 244 | #' 245 | #' @param design_args List containing information about the experiment 246 | #' design; see \code{\link{stim_lists}} 247 | #' @param fixed vector of fixed effects 248 | #' @param subj_rmx matrix of by-subject random effects 249 | #' @param item_rmx matrix of by-item random effects 250 | #' @param verbose give debugging info (default = \code{FALSE}) 251 | #' @param contr_type contrast type (default "contr.dev"); see ?contrasts 252 | #' 253 | #' @details This will add together all of the fixed and random effects 254 | #' according to the linear model specified by the design. Note, 255 | #' however, that it does not add in any residual noise; for that, use 256 | #' the function \code{\link{sim_norm}}. 257 | #' 258 | #' @return a data frame containing response variable \code{Y}, the 259 | #' linear sum of all fixed and random effects. 260 | #' @export 261 | compose_data <- function(design_args, 262 | fixed = NULL, 263 | subj_rmx = NULL, 264 | item_rmx = NULL, 265 | verbose = FALSE, 266 | contr_type = "contr.dev") { 267 | 268 | ## utility function for doing matrix multiplication 269 | multiply_mx <- function(des_mx, rfx, row_ix, design_args) { 270 | ## make sure all cols in rfx are represented in des_mx 271 | diff_cols <- setdiff(colnames(rfx), colnames(des_mx)) 272 | if (length(diff_cols) != 0) { 273 | stop("column(s) '", paste(diff_cols, collapse = ", "), 274 | "' not represented in terms '", 275 | paste(term_names(design_args[["ivs"]], 276 | design_args[["between_subj"]], 277 | design_args[["between_item"]]), 278 | collapse = ", "), "'") 279 | } else {} 280 | 281 | reduced_des <- des_mx[, colnames(rfx), drop = FALSE] 282 | 283 | t_rfx <- t(rfx) 284 | res_vec <- vector("numeric", length(row_ix)) 285 | for (ix in unique(row_ix)) { 286 | lvec <- row_ix == ix 287 | res_vec[lvec] <- c(reduced_des[lvec, , drop = FALSE] %*% 288 | t_rfx[, ix, drop = FALSE]) 289 | } 290 | res_vec 291 | } 292 | 293 | ivs_nrep <- design_args[["ivs"]] 294 | if (!is.null(design_args[["n_rep"]])) { 295 | if (design_args[["n_rep"]] > 1) { 296 | ivs_nrep <- c(as.list(design_args[["ivs"]]), 297 | list(n_rep = paste0("r", seq_len(design_args[["n_rep"]])))) 298 | } else {} 299 | } else {} 300 | iv_names <- names(ivs_nrep) 301 | if (is.matrix(subj_rmx)) { 302 | tlists <- trial_lists(design_args, subjects = nrow(subj_rmx)) 303 | } else { 304 | tlists <- trial_lists(design_args, subjects = subj_rmx) 305 | } 306 | 307 | ## 308 | cont <- lapply(design_args[["ivs"]], function(.x) { 309 | do.call(contr_type, list(.x)) 310 | }) 311 | ## cont <- as.list(rep(contr_type, length(ivs_nrep))) 312 | names(cont) <- iv_names 313 | 314 | mmx <- model.matrix(as.formula(paste0("~", paste(iv_names, collapse = "*"))), 315 | tlists, 316 | contrasts.arg = cont) 317 | 318 | if (is.null(fixed)) { 319 | fixed <- runif(ncol(mmx), -3, 3) 320 | names(fixed) <- colnames(mmx) 321 | } else {} 322 | 323 | ## fixed component of Y 324 | fix_y <- c(mmx %*% fixed) # fixed component of Y 325 | 326 | if (is.matrix(subj_rmx)) { 327 | ## stop("Autogeneration of subj_rmx not implemented yet; please define 'subj_rmx'") 328 | if (nrow(subj_rmx) != length(unique(tlists[["subj_id"]]))) { 329 | stop("Argument 'subj_rmx' has ", nrow(subj_rmx), " rows; needs ", 330 | length(unique(tlists[["subj_id"]]))) 331 | } else {} 332 | sre <- multiply_mx(mmx, subj_rmx, tlists[["subj_id"]], design_args) 333 | } else { 334 | sre <- NULL 335 | } 336 | 337 | if (is.matrix(item_rmx)) { 338 | ## stop("Autogeneration of item_rmx not implemented yet; please define 'item_rmx'") 339 | if (nrow(item_rmx) != length(unique(tlists[["item_id"]]))) { 340 | stop("Argument 'item_rmx' has ", nrow(item_rmx), " rows; needs ", 341 | length(unique(tlists[["item_id"]]))) 342 | } else {} 343 | ire <- multiply_mx(mmx, item_rmx, tlists[["item_id"]], design_args) 344 | } else { 345 | ire <- NULL 346 | } 347 | 348 | comb_mx <- matrix(nrow = nrow(tlists), ncol = 0) 349 | if (verbose) { 350 | ## comb_mx <- cbind(fix_y = fix_y, sre = sre, ire = ire, err = err) 351 | comb_mx <- cbind(fix_y = fix_y, sre = sre, ire = ire) 352 | } else {} 353 | ## cbind(tlists, Y = fix_y + sre + ire + err, comb_mx) 354 | 355 | yvals <- fix_y 356 | if (!is.null(sre)) { 357 | yvals <- yvals + sre 358 | } 359 | if (!is.null(ire)) { 360 | yvals <- yvals + ire 361 | } 362 | cbind(tlists, Y = yvals, comb_mx) 363 | } 364 | -------------------------------------------------------------------------------- /README.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: funfact 3 | --- 4 | 5 | funfact is an R package that makes it easier to plan, simulate, and analyze studies with factorial designs. It is intended for use with experiments in which multiple stimulus items are presented to multiple subjects. 6 | 7 | funfact can be used for the following purposes: 8 | 9 | - generating counterbalanced stimulus lists for any arbitrary factorial design; 10 | - generating a full list of trials corresponding to the realization of an experiment; 11 | - simulating data for any type of full factorial design; 12 | - generating a design formula for use in mixed model analysis in `lme4`. 13 | 14 | Each of these purposes is illustrated in one of the sections below. 15 | 16 | In addition, funfact provides convenience functions `contr.dev()` and `with_dev_pred()` for creating deviation-coded predictors for use in regression-based analyses of factorial designs. See http://talklab.psy.gla.ac.uk/tvw/catpred for discussion on coding schemes. 17 | 18 | ## funfact basics 19 | 20 | funfact conceptually separates the *design* of an experiment from the *realization* of that experiment through the sampling of subjects. 21 | 22 | The design of an experiment is represented using a list object with the following named elements: 23 | 24 | | Element Name | Description | 25 | |----------------+----------------------------------------------------------------------------------------------| 26 | | `ivs` | Names and levels of the independent variables (IVs) that form the main factors in the design | 27 | | `between_subj` | Names of any IVs administered between subjects | 28 | | `between_item` | Names of any IVs administered between items | 29 | | `n_item` | Number of stimulus items | 30 | | `n_rep` | Number of times each item should be presented to each participant (default 1) | 31 | 32 | For information on how these elements should be defined, see the examples in the next section. 33 | 34 | ## Generating counterbalanced stimulus presentation lists 35 | 36 | ```{r, echo = FALSE} 37 | library("funfact") 38 | ``` 39 | 40 | The function `stim_lists()` generates a set of counterbalanced stimulus presentation lists for any given (full) factorial design. A "stimulus presentation list" determines which items will be presented to a given subject, and in which condition. The generation process ensures that each subject sees the same number of items as well as the same number of items in each condition. It also ensures that each item is presented the same number of times, in each condition, across subjects. 41 | 42 | You determine the experiment design by specifying arguments within a list object. 43 | 44 | ### 2x2 design with both factors within-subjects and within-items 45 | 46 | ```{r} 47 | ## 2x2 within-subject within-item factorial design 48 | ## NB: here, the two IVs are A and B, each having 2 levels 49 | wswi_design <- list(ivs = c(A = 2, B = 2), 50 | n_item = 8) 51 | 52 | stim_lists(wswi_design) 53 | ``` 54 | 55 | ### A more complicated, two-way mixed design 56 | 57 | ```{r} 58 | mixed_design <- list(ivs = c(A = 2, B = 2), 59 | between_subj = c("A"), 60 | between_item = c("B"), 61 | n_item = 8) 62 | 63 | stim_lists(mixed_design) 64 | ``` 65 | 66 | ### 2x3 design, one factor between 67 | 68 | ```{r} 69 | ## note that IVs can also be specified as elements of a list, each 70 | ## key/value pair being the variable name and a vector with the levels 71 | ## of that variable 72 | bswi_2by3 <- list(ivs = list(Group = c("Child", "Adult"), 73 | Condition = c("Control", "High", "Low")), 74 | between_subj = c("Group"), 75 | n_item = 6) 76 | 77 | stim_lists(bswi_2by3) 78 | ``` 79 | 80 | ### 3-way mixed design 81 | 82 | The design below is a 3-way design based on Gann & Barr (2014). 83 | 84 | ```{r} 85 | threeway <- list(ivs = list(Novelty = c("New", "Old"), 86 | Addressee = c("Same", "Diff"), 87 | Feedback = c("Yes", "No")), 88 | between_subj = c("Addressee"), 89 | between_item = c("Feedback"), 90 | n_item = 16) 91 | 92 | stim_lists(threeway) 93 | ``` 94 | 95 | ## Generating trial lists 96 | 97 | While stimulus presentation lists determine which items a subject sees in which condition, trial lists randomly assign an equal number of subjects to each list. 98 | 99 | ```{r} 100 | wswi_design <- list(ivs = c(A = 2, B = 2), 101 | n_item = 8) 102 | 103 | trial_lists(wswi_design, subjects = 8) 104 | ``` 105 | 106 | ## Simulating data from a factorial design 107 | 108 | The functions `gen_pop()` and `sim_norm()` can be used to simulate data from any type of factorial design. 109 | 110 | `gen_pop()` is used to generate population parameters to be used in the simulation. `sim_norm()` takes a sample from the population defined by `gen_pop()`, as the example below illustrates. 111 | 112 | ```{r} 113 | my_design <- list(ivs = c(A = 2, B = 2), 114 | n_item = 8) 115 | popdata <- gen_pop(my_design, 8) 116 | 117 | my_data <- sim_norm(my_design, 8, popdata) 118 | my_data 119 | ``` 120 | 121 | ## Identifying the model formula to be used in mixed-effects modeling 122 | 123 | Simultaneous sampling of subjects and items can lead to complicated dependencies (see [Barr, Levy, Scheepers, and Tily, 2013](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3881361)). One appealing way to account for these dependencies is through linear mixed-effects modeling, which can be accomplished using the lme4 package in R. The function =design_formula()= in funfact gives you the model formula that is appropriate for analyzing the data in your sample. 124 | 125 | ```{r} 126 | design_formula(threeway, n_subj = 16) 127 | ``` 128 | -------------------------------------------------------------------------------- /docs/LICENSE-text.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | License • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 45 | 46 | 47 | 48 | 49 | 50 |
51 |
52 | 87 | 88 | 89 |
90 | 91 |
92 |
93 |
94 |

License

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389 |     {description}
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391 | 
392 |     This program is free software; you can redistribute it and/or modify
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406 | Also add information on how to contact you by electronic and paper mail.
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408 | If the program is interactive, make it output a short notice like this
409 | when it starts in an interactive mode:
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411 |     Gnomovision version 69, Copyright (C) year name of author
412 |     Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
413 |     This is free software, and you are welcome to redistribute it
414 |     under certain conditions; type `show c' for details.
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425 |   Yoyodyne, Inc., hereby disclaims all copyright interest in the program
426 |   `Gnomovision' (which makes passes at compilers) written by James Hacker.
427 | 
428 |   {signature of Ty Coon}, 1 April 1989
429 |   Ty Coon, President of Vice
430 | 
431 | This General Public License does not permit incorporating your program into
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434 | library.  If this is what you want to do, use the GNU Lesser General
435 | Public License instead of this License.
436 |
437 | 438 |
439 | 440 |
441 | 442 | 443 |
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445 |

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52 | 87 | 88 | 89 |
90 | 91 |
92 |
93 |
94 |

Authors

95 |
96 | 97 |
    98 |
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    Dale J. Barr. Maintainer. 100 |

    101 |
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105 | 106 |
107 | 108 | 109 |
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37 | background-color: rgb(255,255,255); 38 | border: 0; 39 | max-height: 80vh; 40 | } 41 | 42 | .algolia-autocomplete .ds-dropdown-menu .ds-suggestions { 43 | margin-top: 0 44 | } 45 | 46 | .algolia-autocomplete .algolia-docsearch-suggestion { 47 | padding: 0; 48 | overflow: visible 49 | } 50 | 51 | .algolia-autocomplete .algolia-docsearch-suggestion--category-header { 52 | padding: .125rem 1rem; 53 | margin-top: 0; 54 | font-size: 1.3em; 55 | font-weight: 500; 56 | color: #00008B; 57 | border-bottom: 0 58 | } 59 | 60 | .algolia-autocomplete .algolia-docsearch-suggestion--wrapper { 61 | float: none; 62 | padding-top: 0 63 | } 64 | 65 | .algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column { 66 | float: none; 67 | width: auto; 68 | padding: 0; 69 | text-align: left 70 | } 71 | 72 | .algolia-autocomplete .algolia-docsearch-suggestion--content { 73 | float: none; 74 | width: auto; 75 | padding: 0 76 | } 77 | 78 | .algolia-autocomplete .algolia-docsearch-suggestion--content::before { 79 | display: none 80 | } 81 | 82 | .algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header { 83 | padding-top: .75rem; 84 | margin-top: .75rem; 85 | border-top: 1px solid rgba(0, 0, 0, .1) 86 | } 87 | 88 | .algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column { 89 | display: block; 90 | padding: .1rem 1rem; 91 | margin-bottom: 0.1; 92 | font-size: 1.0em; 93 | font-weight: 400 94 | /* display: none */ 95 | } 96 | 97 | .algolia-autocomplete .algolia-docsearch-suggestion--title { 98 | display: block; 99 | padding: .25rem 1rem; 100 | margin-bottom: 0; 101 | font-size: 0.9em; 102 | font-weight: 400 103 | } 104 | 105 | .algolia-autocomplete .algolia-docsearch-suggestion--text { 106 | padding: 0 1rem .5rem; 107 | margin-top: -.25rem; 108 | font-size: 0.8em; 109 | font-weight: 400; 110 | line-height: 1.25 111 | } 112 | 113 | .algolia-autocomplete .algolia-docsearch-footer { 114 | width: 110px; 115 | height: 20px; 116 | z-index: 3; 117 | margin-top: 10.66667px; 118 | float: right; 119 | font-size: 0; 120 | line-height: 0; 121 | } 122 | 123 | .algolia-autocomplete .algolia-docsearch-footer--logo { 124 | background-image: url("data:image/svg+xml;utf8,"); 125 | background-repeat: no-repeat; 126 | background-position: 50%; 127 | background-size: 100%; 128 | overflow: hidden; 129 | text-indent: -9000px; 130 | width: 100%; 131 | height: 100%; 132 | display: block; 133 | transform: translate(-8px); 134 | } 135 | 136 | .algolia-autocomplete .algolia-docsearch-suggestion--highlight { 137 | color: #FF8C00; 138 | background: rgba(232, 189, 54, 0.1) 139 | } 140 | 141 | 142 | .algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight { 143 | box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5) 144 | } 145 | 146 | .algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content { 147 | background-color: rgba(192, 192, 192, .15) 148 | } 149 | -------------------------------------------------------------------------------- /docs/docsearch.js: -------------------------------------------------------------------------------- 1 | $(function() { 2 | 3 | // register a handler to move the focus to the search bar 4 | // upon pressing shift + "/" (i.e. "?") 5 | $(document).on('keydown', function(e) { 6 | if (e.shiftKey && e.keyCode == 191) { 7 | e.preventDefault(); 8 | $("#search-input").focus(); 9 | } 10 | }); 11 | 12 | $(document).ready(function() { 13 | // do keyword highlighting 14 | /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ 15 | var mark = function() { 16 | 17 | var referrer = document.URL ; 18 | var paramKey = "q" ; 19 | 20 | if (referrer.indexOf("?") !== -1) { 21 | var qs = referrer.substr(referrer.indexOf('?') + 1); 22 | var qs_noanchor = qs.split('#')[0]; 23 | var qsa = qs_noanchor.split('&'); 24 | var keyword = ""; 25 | 26 | for (var i = 0; i < qsa.length; i++) { 27 | var currentParam = qsa[i].split('='); 28 | 29 | if (currentParam.length !== 2) { 30 | continue; 31 | } 32 | 33 | if (currentParam[0] == paramKey) { 34 | keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); 35 | } 36 | } 37 | 38 | if (keyword !== "") { 39 | $(".contents").unmark({ 40 | done: function() { 41 | $(".contents").mark(keyword); 42 | } 43 | }); 44 | } 45 | } 46 | }; 47 | 48 | mark(); 49 | }); 50 | }); 51 | 52 | /* Search term highlighting ------------------------------*/ 53 | 54 | function matchedWords(hit) { 55 | var words = []; 56 | 57 | var hierarchy = hit._highlightResult.hierarchy; 58 | // loop to fetch from lvl0, lvl1, etc. 59 | for (var idx in hierarchy) { 60 | words = words.concat(hierarchy[idx].matchedWords); 61 | } 62 | 63 | var content = hit._highlightResult.content; 64 | if (content) { 65 | words = words.concat(content.matchedWords); 66 | } 67 | 68 | // return unique words 69 | var words_uniq = [...new Set(words)]; 70 | return words_uniq; 71 | } 72 | 73 | function updateHitURL(hit) { 74 | 75 | var words = matchedWords(hit); 76 | var url = ""; 77 | 78 | if (hit.anchor) { 79 | url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; 80 | } else { 81 | url = hit.url + '?q=' + escape(words.join(" ")); 82 | } 83 | 84 | return url; 85 | } 86 | -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | Functions for factorial experiments • funfact 9 | 10 | 11 | 12 | 13 | 16 | 17 | 21 | 22 | 23 |
24 |
57 | 58 | 59 | 60 |
61 |
62 | 63 | 64 | 65 | 66 |

funfact is an R package that makes it easier to plan, simulate, and analyze studies with factorial designs. It is intended for use with experiments in which multiple stimulus items are presented to multiple subjects.

67 |

funfact can be used for the following purposes:

68 |
    69 |
  • generating counterbalanced stimulus lists for any arbitrary factorial design;
    • 70 |
  • generating a full list of trials corresponding to the realization of an experiment;
    • 71 |
  • simulating data for any type of full factorial design;
    • 72 |
  • generating a design formula for use in mixed model analysis in lme4.
    • 73 |
74 |

Each of these purposes is illustrated in one of the sections below.

75 |

In addition, funfact provides convenience functions contr.dev() and with_dev_pred() for creating deviation-coded predictors for use in regression-based analyses of factorial designs. See http://talklab.psy.gla.ac.uk/tvw/catpred for discussion on coding schemes.

76 |
77 |

78 | funfact basics

79 |

funfact conceptually separates the design of an experiment from the realization of that experiment through the sampling of subjects.

80 |

The design of an experiment is represented using a list object with the following named elements:

81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 108 | 109 | 110 | 111 | 112 |
Element NameDescription
ivsNames and levels of the independent variables (IVs) that form the main factors in the design
between_subjNames of any IVs administered between subjects
between_itemNames of any IVs administered between items
n_itemNumber of stimulus items
n_repNumber of times each item should be presented to each participant (default 1)
113 |

For information on how these elements should be defined, see the examples in the next section.

114 |
115 |
116 |

117 | Generating counterbalanced stimulus presentation lists

118 |

The function stim_lists() generates a set of counterbalanced stimulus presentation lists for any given (full) factorial design. A “stimulus presentation list” determines which items will be presented to a given subject, and in which condition. The generation process ensures that each subject sees the same number of items as well as the same number of items in each condition. It also ensures that each item is presented the same number of times, in each condition, across subjects.

119 |

You determine the experiment design by specifying arguments within a list object.

120 |
121 |

122 | 2x2 design with both factors within-subjects and within-items

123 | 
  ## 2x2 within-subject within-item factorial design
124 |   ## NB: here, the two IVs are A and B, each having 2 levels
125 |   wswi_design <- list(ivs = c(A = 2, B = 2),
126 |                       n_item = 8)
127 | 
128 |   stim_lists(wswi_design)
129 | 
##    list_id item_id  A  B
130 | ## 1        1       1 A1 B1
131 | ## 2        1       2 A1 B1
132 | ## 3        1       3 A1 B2
133 | ## 4        1       4 A1 B2
134 | ## 5        1       5 A2 B1
135 | ## 6        1       6 A2 B1
136 | ## 7        1       7 A2 B2
137 | ## 8        1       8 A2 B2
138 | ## 9        2       1 A1 B2
139 | ## 10       2       2 A1 B2
140 | ## 11       2       3 A2 B1
141 | ## 12       2       4 A2 B1
142 | ## 13       2       5 A2 B2
143 | ## 14       2       6 A2 B2
144 | ## 15       2       7 A1 B1
145 | ## 16       2       8 A1 B1
146 | ## 17       3       1 A2 B1
147 | ## 18       3       2 A2 B1
148 | ## 19       3       3 A2 B2
149 | ## 20       3       4 A2 B2
150 | ## 21       3       5 A1 B1
151 | ## 22       3       6 A1 B1
152 | ## 23       3       7 A1 B2
153 | ## 24       3       8 A1 B2
154 | ## 25       4       1 A2 B2
155 | ## 26       4       2 A2 B2
156 | ## 27       4       3 A1 B1
157 | ## 28       4       4 A1 B1
158 | ## 29       4       5 A1 B2
159 | ## 30       4       6 A1 B2
160 | ## 31       4       7 A2 B1
161 | ## 32       4       8 A2 B1
162 |
163 |
164 |

165 | A more complicated, two-way mixed design

166 | 
  mixed_design <- list(ivs = c(A = 2, B = 2),
167 |                        between_subj = c("A"),
168 |                        between_item = c("B"),
169 |                        n_item = 8)
170 | 
171 |   stim_lists(mixed_design)
172 | 
##    list_id item_id  A  B
173 | ## 1        1       1 A1 B1
174 | ## 2        1       2 A1 B1
175 | ## 3        1       3 A1 B1
176 | ## 4        1       4 A1 B1
177 | ## 5        1       5 A1 B2
178 | ## 6        1       6 A1 B2
179 | ## 7        1       7 A1 B2
180 | ## 8        1       8 A1 B2
181 | ## 9        2       1 A2 B1
182 | ## 10       2       2 A2 B1
183 | ## 11       2       3 A2 B1
184 | ## 12       2       4 A2 B1
185 | ## 13       2       5 A2 B2
186 | ## 14       2       6 A2 B2
187 | ## 15       2       7 A2 B2
188 | ## 16       2       8 A2 B2
189 |
190 |
191 |

192 | 2x3 design, one factor between

193 | 
  ## note that IVs can also be specified as elements of a list, each
194 |   ## key/value pair being the variable name and a vector with the levels
195 |   ## of that variable
196 |   bswi_2by3 <- list(ivs = list(Group = c("Child", "Adult"),
197 |                         Condition = c("Control", "High", "Low")),
198 |                     between_subj = c("Group"),
199 |                     n_item = 6)
200 | 
201 |   stim_lists(bswi_2by3)
202 | 
##    list_id item_id Group Condition
203 | ## 1        1       1 Child   Control
204 | ## 2        1       2 Child   Control
205 | ## 3        1       3 Child      High
206 | ## 4        1       4 Child      High
207 | ## 5        1       5 Child       Low
208 | ## 6        1       6 Child       Low
209 | ## 7        2       1 Child      High
210 | ## 8        2       2 Child      High
211 | ## 9        2       3 Child       Low
212 | ## 10       2       4 Child       Low
213 | ## 11       2       5 Child   Control
214 | ## 12       2       6 Child   Control
215 | ## 13       3       1 Child       Low
216 | ## 14       3       2 Child       Low
217 | ## 15       3       3 Child   Control
218 | ## 16       3       4 Child   Control
219 | ## 17       3       5 Child      High
220 | ## 18       3       6 Child      High
221 | ## 19       4       1 Adult   Control
222 | ## 20       4       2 Adult   Control
223 | ## 21       4       3 Adult      High
224 | ## 22       4       4 Adult      High
225 | ## 23       4       5 Adult       Low
226 | ## 24       4       6 Adult       Low
227 | ## 25       5       1 Adult      High
228 | ## 26       5       2 Adult      High
229 | ## 27       5       3 Adult       Low
230 | ## 28       5       4 Adult       Low
231 | ## 29       5       5 Adult   Control
232 | ## 30       5       6 Adult   Control
233 | ## 31       6       1 Adult       Low
234 | ## 32       6       2 Adult       Low
235 | ## 33       6       3 Adult   Control
236 | ## 34       6       4 Adult   Control
237 | ## 35       6       5 Adult      High
238 | ## 36       6       6 Adult      High
239 |
240 |
241 |

242 | 3-way mixed design

243 |

The design below is a 3-way design based on Gann & Barr (2014).

244 | 
  threeway <- list(ivs = list(Novelty = c("New", "Old"),
245 |                        Addressee = c("Same", "Diff"),
246 |                        Feedback = c("Yes", "No")),
247 |                    between_subj = c("Addressee"),
248 |                    between_item = c("Feedback"),
249 |                    n_item = 16)
250 | 
251 |   stim_lists(threeway)
252 | 
##    list_id item_id Addressee Feedback Novelty
253 | ## 1        1       1      Same      Yes     New
254 | ## 2        1       2      Same      Yes     New
255 | ## 3        1       3      Same      Yes     New
256 | ## 4        1       4      Same      Yes     New
257 | ## 5        1       5      Same      Yes     Old
258 | ## 6        1       6      Same      Yes     Old
259 | ## 7        1       7      Same      Yes     Old
260 | ## 8        1       8      Same      Yes     Old
261 | ## 9        1       9      Same       No     New
262 | ## 10       1      10      Same       No     New
263 | ## 11       1      11      Same       No     New
264 | ## 12       1      12      Same       No     New
265 | ## 13       1      13      Same       No     Old
266 | ## 14       1      14      Same       No     Old
267 | ## 15       1      15      Same       No     Old
268 | ## 16       1      16      Same       No     Old
269 | ## 17       2       1      Same      Yes     Old
270 | ## 18       2       2      Same      Yes     Old
271 | ## 19       2       3      Same      Yes     Old
272 | ## 20       2       4      Same      Yes     Old
273 | ## 21       2       5      Same      Yes     New
274 | ## 22       2       6      Same      Yes     New
275 | ## 23       2       7      Same      Yes     New
276 | ## 24       2       8      Same      Yes     New
277 | ## 25       2       9      Same       No     Old
278 | ## 26       2      10      Same       No     Old
279 | ## 27       2      11      Same       No     Old
280 | ## 28       2      12      Same       No     Old
281 | ## 29       2      13      Same       No     New
282 | ## 30       2      14      Same       No     New
283 | ## 31       2      15      Same       No     New
284 | ## 32       2      16      Same       No     New
285 | ## 33       3       1      Diff      Yes     New
286 | ## 34       3       2      Diff      Yes     New
287 | ## 35       3       3      Diff      Yes     New
288 | ## 36       3       4      Diff      Yes     New
289 | ## 37       3       5      Diff      Yes     Old
290 | ## 38       3       6      Diff      Yes     Old
291 | ## 39       3       7      Diff      Yes     Old
292 | ## 40       3       8      Diff      Yes     Old
293 | ## 41       3       9      Diff       No     New
294 | ## 42       3      10      Diff       No     New
295 | ## 43       3      11      Diff       No     New
296 | ## 44       3      12      Diff       No     New
297 | ## 45       3      13      Diff       No     Old
298 | ## 46       3      14      Diff       No     Old
299 | ## 47       3      15      Diff       No     Old
300 | ## 48       3      16      Diff       No     Old
301 | ## 49       4       1      Diff      Yes     Old
302 | ## 50       4       2      Diff      Yes     Old
303 | ## 51       4       3      Diff      Yes     Old
304 | ## 52       4       4      Diff      Yes     Old
305 | ## 53       4       5      Diff      Yes     New
306 | ## 54       4       6      Diff      Yes     New
307 | ## 55       4       7      Diff      Yes     New
308 | ## 56       4       8      Diff      Yes     New
309 | ## 57       4       9      Diff       No     Old
310 | ## 58       4      10      Diff       No     Old
311 | ## 59       4      11      Diff       No     Old
312 | ## 60       4      12      Diff       No     Old
313 | ## 61       4      13      Diff       No     New
314 | ## 62       4      14      Diff       No     New
315 | ## 63       4      15      Diff       No     New
316 | ## 64       4      16      Diff       No     New
317 |
318 |
319 |
320 |

321 | Generating trial lists

322 |

While stimulus presentation lists determine which items a subject sees in which condition, trial lists randomly assign an equal number of subjects to each list.

323 | 
  wswi_design <- list(ivs = c(A = 2, B = 2),
324 |                       n_item = 8)
325 | 
326 |   trial_lists(wswi_design, subjects = 8)
327 | 
##    subj_id list_id item_id  A  B
328 | ## 1        1       3       1 A2 B1
329 | ## 2        1       3       2 A2 B1
330 | ## 3        1       3       3 A2 B2
331 | ## 4        1       3       4 A2 B2
332 | ## 5        1       3       5 A1 B1
333 | ## 6        1       3       6 A1 B1
334 | ## 7        1       3       7 A1 B2
335 | ## 8        1       3       8 A1 B2
336 | ## 9        2       4       1 A2 B2
337 | ## 10       2       4       2 A2 B2
338 | ## 11       2       4       3 A1 B1
339 | ## 12       2       4       4 A1 B1
340 | ## 13       2       4       5 A1 B2
341 | ## 14       2       4       6 A1 B2
342 | ## 15       2       4       7 A2 B1
343 | ## 16       2       4       8 A2 B1
344 | ## 17       3       1       1 A1 B1
345 | ## 18       3       1       2 A1 B1
346 | ## 19       3       1       3 A1 B2
347 | ## 20       3       1       4 A1 B2
348 | ## 21       3       1       5 A2 B1
349 | ## 22       3       1       6 A2 B1
350 | ## 23       3       1       7 A2 B2
351 | ## 24       3       1       8 A2 B2
352 | ## 25       4       4       1 A2 B2
353 | ## 26       4       4       2 A2 B2
354 | ## 27       4       4       3 A1 B1
355 | ## 28       4       4       4 A1 B1
356 | ## 29       4       4       5 A1 B2
357 | ## 30       4       4       6 A1 B2
358 | ## 31       4       4       7 A2 B1
359 | ## 32       4       4       8 A2 B1
360 | ## 33       5       2       1 A1 B2
361 | ## 34       5       2       2 A1 B2
362 | ## 35       5       2       3 A2 B1
363 | ## 36       5       2       4 A2 B1
364 | ## 37       5       2       5 A2 B2
365 | ## 38       5       2       6 A2 B2
366 | ## 39       5       2       7 A1 B1
367 | ## 40       5       2       8 A1 B1
368 | ## 41       6       1       1 A1 B1
369 | ## 42       6       1       2 A1 B1
370 | ## 43       6       1       3 A1 B2
371 | ## 44       6       1       4 A1 B2
372 | ## 45       6       1       5 A2 B1
373 | ## 46       6       1       6 A2 B1
374 | ## 47       6       1       7 A2 B2
375 | ## 48       6       1       8 A2 B2
376 | ## 49       7       3       1 A2 B1
377 | ## 50       7       3       2 A2 B1
378 | ## 51       7       3       3 A2 B2
379 | ## 52       7       3       4 A2 B2
380 | ## 53       7       3       5 A1 B1
381 | ## 54       7       3       6 A1 B1
382 | ## 55       7       3       7 A1 B2
383 | ## 56       7       3       8 A1 B2
384 | ## 57       8       2       1 A1 B2
385 | ## 58       8       2       2 A1 B2
386 | ## 59       8       2       3 A2 B1
387 | ## 60       8       2       4 A2 B1
388 | ## 61       8       2       5 A2 B2
389 | ## 62       8       2       6 A2 B2
390 | ## 63       8       2       7 A1 B1
391 | ## 64       8       2       8 A1 B1
392 |
393 |
394 |

395 | Simulating data from a factorial design

396 |

The functions gen_pop() and sim_norm() can be used to simulate data from any type of factorial design.

397 |

gen_pop() is used to generate population parameters to be used in the simulation. sim_norm() takes a sample from the population defined by gen_pop(), as the example below illustrates.

398 | 
  my_design <- list(ivs = c(A = 2, B = 2),
399 |                     n_item = 8)
400 |   popdata <- gen_pop(my_design, 8)
401 | 
402 |   my_data <- sim_norm(my_design, 8, popdata)
403 |   my_data
404 | 
##    subj_id list_id item_id  A  B           Y         err
405 | ## 1        1       4       1 A2 B2  5.01689929  0.87842477
406 | ## 2        1       4       2 A2 B2  4.38878926  0.54911911
407 | ## 3        1       4       3 A1 B1  1.11699093  1.13036926
408 | ## 4        1       4       4 A1 B1 -1.37261803 -0.75655038
409 | ## 5        1       4       5 A1 B2  3.08824910 -0.54462380
410 | ## 6        1       4       6 A1 B2  3.16408362  0.52654105
411 | ## 7        1       4       7 A2 B1  1.43523189 -0.60861942
412 | ## 8        1       4       8 A2 B1 -2.96707125 -1.08123639
413 | ## 9        2       4       1 A2 B2  3.62682046  1.82008435
414 | ## 10       2       4       2 A2 B2  1.66457951  0.15664777
415 | ## 11       2       4       3 A1 B1 -4.12997003 -3.07323658
416 | ## 12       2       4       4 A1 B1 -0.06318779  1.59623499
417 | ## 13       2       4       5 A1 B2  5.54570583  1.55729875
418 | ## 14       2       4       6 A1 B2  6.23910322  3.24602648
419 | ## 15       2       4       7 A2 B1  1.96767277  0.35720436
420 | ## 16       2       4       8 A2 B1 -3.95741355 -1.63819578
421 | ## 17       3       2       1 A1 B2  0.62310726  0.18186913
422 | ## 18       3       2       2 A1 B2  2.84852317  2.42995361
423 | ## 19       3       2       3 A2 B1  2.93416550  2.84802105
424 | ## 20       3       2       4 A2 B1 -1.86441745 -2.21153506
425 | ## 21       3       2       5 A2 B2  5.54234548  1.62438411
426 | ## 22       3       2       6 A2 B2  4.34874446  1.19800313
427 | ## 23       3       2       7 A1 B1  0.99181306  1.07747370
428 | ## 24       3       2       8 A1 B1 -5.07793578 -1.44868782
429 | ## 25       4       2       1 A1 B2 -1.86092555 -3.77503400
430 | ## 26       4       2       2 A1 B2  1.05830819 -0.83313170
431 | ## 27       4       2       3 A2 B1 -1.72501882 -2.27181093
432 | ## 28       4       2       4 A2 B1  2.15651698  1.34875170
433 | ## 29       4       2       5 A2 B2  6.51251600  1.50067182
434 | ## 30       4       2       6 A2 B2  7.21721951  2.97259536
435 | ## 31       4       2       7 A1 B1 -0.66632710 -2.33294398
436 | ## 32       4       2       8 A1 B1 -4.88096622 -3.00399578
437 | ## 33       5       1       1 A1 B1 -1.78701801 -0.38607379
438 | ## 34       5       1       2 A1 B1 -2.34810557 -2.26404305
439 | ## 35       5       1       3 A1 B2 -1.01401176 -4.68876659
440 | ## 36       5       1       4 A1 B2  5.26411754  2.63121706
441 | ## 37       5       1       5 A2 B1 -0.95034358  0.17980574
442 | ## 38       5       1       6 A2 B1  1.44786141  1.30697374
443 | ## 39       5       1       7 A2 B2  5.13209663  0.84701676
444 | ## 40       5       1       8 A2 B2  1.61319128 -2.89352793
445 | ## 41       6       3       1 A2 B1  0.71193156  0.33470335
446 | ## 42       6       3       2 A2 B1 -0.98627644 -2.07839456
447 | ## 43       6       3       3 A2 B2  1.45981679 -1.15834951
448 | ## 44       6       3       4 A2 B2  4.47707391  0.47322822
449 | ## 45       6       3       5 A1 B1 -0.03030409  1.12360400
450 | ## 46       6       3       6 A1 B1  0.02438670  0.54991336
451 | ## 47       6       3       7 A1 B2  1.02196468 -0.09761682
452 | ## 48       6       3       8 A1 B2 -1.62145736 -3.56747166
453 | ## 49       7       1       1 A1 B1 -1.19913187  0.69500964
454 | ## 50       7       1       2 A1 B1 -0.14203225  0.43522756
455 | ## 51       7       1       3 A1 B2  2.78339614 -0.36544194
456 | ## 52       7       1       4 A1 B2 -0.46099038 -2.56797412
457 | ## 53       7       1       5 A2 B1  3.21020113  2.83079355
458 | ## 54       7       1       6 A2 B1  3.64246520  1.99202064
459 | ## 55       7       1       7 A2 B2  9.84260173  3.11607461
460 | ## 56       7       1       8 A2 B2  6.02346797 -0.92469846
461 | ## 57       8       3       1 A2 B1  1.50088558  0.30064124
462 | ## 58       8       3       2 A2 B1  4.98827729  3.07314303
463 | ## 59       8       3       3 A2 B2  4.57124615  0.74551922
464 | ## 60       8       3       4 A2 B2  2.90029999 -2.31110632
465 | ## 61       8       3       5 A1 B1 -4.80927467  0.13641578
466 | ## 62       8       3       6 A1 B1 -6.30242151 -1.98511248
467 | ## 63       8       3       7 A1 B2  2.00756103  4.04041129
468 | ## 64       8       3       8 A1 B2 -0.98576259  0.22065487
469 |
470 |
471 |

472 | Identifying the model formula to be used in mixed-effects modeling

473 |

Simultaneous sampling of subjects and items can lead to complicated dependencies (see Barr, Levy, Scheepers, and Tily, 2013). One appealing way to account for these dependencies is through linear mixed-effects modeling, which can be accomplished using the lme4 package in R. The function =design_formula()= in funfact gives you the model formula that is appropriate for analyzing the data in your sample.

474 | 
design_formula(threeway, n_subj = 16)
475 | 
## ~Novelty * Addressee * Feedback + (1 + Novelty * Feedback | subj_id) + 
476 | ##     (1 + Novelty * Addressee | item_id)
477 | ## <environment: 0x55b8eac92e60>
478 |
479 |
480 | 481 | 496 | 497 |
498 | 499 | 500 |
501 |

Developed by Dale J. Barr.

502 |
503 | 504 |
505 |

Site built with pkgdown 1.3.0.

506 |
507 |
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    54 |
    55 | 90 | 91 | 92 |
    93 | 94 |
    95 |
    96 |
    97 |

    Compose response data from fixed and random effects

    98 | 99 | 100 |
    101 | 102 |
    103 | 104 |

    Compose response data from fixed and random effects

    105 | 106 |
    107 | 108 | 
    compose_data(design_args, fixed = NULL, subj_rmx = NULL,
109 |   item_rmx = NULL, verbose = FALSE, contr_type = "contr.dev")
    110 | 111 |

    Arguments

    112 | 113 | 114 | 115 | 116 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 | 133 | 134 | 135 | 136 | 137 | 138 | 139 |
    design_args

    List containing information about the experiment 117 | design; see stim_lists

    fixed

    vector of fixed effects

    subj_rmx

    matrix of by-subject random effects

    item_rmx

    matrix of by-item random effects

    verbose

    give debugging info (default = FALSE)

    contr_type

    contrast type (default "contr.dev"); see ?contrasts

    140 | 141 |

    Value

    142 | 143 |

    a data frame containing response variable Y, the 144 | linear sum of all fixed and random effects.

    145 | 146 |

    Details

    147 | 148 |

    This will add together all of the fixed and random effects 149 | according to the linear model specified by the design. Note, 150 | however, that it does not add in any residual noise; for that, use 151 | the function sim_norm.

    152 | 153 | 154 |
    155 | 166 |
    167 | 168 |
    169 |
    170 |

    Developed by Dale J. Barr.

    171 |
    172 | 173 |
    174 |

    Site built with pkgdown 1.3.0.

    175 |
    176 |
    177 |
    178 | 179 | 180 | 181 | 182 | 183 | 184 | -------------------------------------------------------------------------------- /docs/reference/contr.dev.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Deviation-Coded Contrast Matrices — contr.dev • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 48 | 49 | 50 | 51 | 52 | 53 |
    54 |
    55 | 90 | 91 | 92 |
    93 | 94 |
    95 |
    96 |
    97 |

    Deviation-Coded Contrast Matrices

    98 | 99 | 100 |
    101 | 102 |
    103 | 104 |

    Return a matrix of deviation-coded contrasts.

    105 | 106 |
    107 | 108 | 
    contr.dev(n, base = 1, contrasts = TRUE)
    109 | 110 |

    Arguments

    111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 121 | 122 | 123 | 124 | 125 | 126 |
    n

    a vector of levels for a factor, or the number of levels.

    base

    an integer specifying which group is considered the 120 | baseline group. Ignored if 'contrasts' is FALSE.

    contrasts

    a logical indicating whether contrasts should be computed.

    127 | 128 | 129 |
    130 | 137 |
    138 | 139 |
    140 |
    141 |

    Developed by Dale J. Barr.

    142 |
    143 | 144 |
    145 |

    Site built with pkgdown 1.3.0.

    146 |
    147 |
    148 |
    149 | 150 | 151 | 152 | 153 | 154 | 155 | -------------------------------------------------------------------------------- /docs/reference/design_formula.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Get the GLM formula for a factorially-designed experiment — design_formula • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 49 | 50 | 51 | 52 | 53 | 54 |
    55 |
    56 | 91 | 92 | 93 |
    94 | 95 |
    96 |
    97 |
    98 |

    Get the GLM formula for a factorially-designed experiment

    99 | 100 | 101 |
    102 | 103 |
    104 | 105 |

    Get the formula corresponding to the general linear model for a 106 | factorially designed experiment, with maximal random effects.

    107 | 108 |
    109 | 110 | 
    design_formula(design_args, n_subj = NULL, dv_name = NULL,
111 |   lme4_format = TRUE)
    112 | 113 |

    Arguments

    114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 | 131 | 132 |
    design_args

    A list with experimental design information (see link{stim_lists})

    n_subj

    Number of subjects

    dv_name

    Name of dependent variable; NULL (default) for one-sided formula

    lme4_format

    Do you want the results combined as the model formula for a lme4 model? (default TRUE)

    133 | 134 |

    Value

    135 | 136 |

    A formula, character string, or list, depending

    137 | 138 | 139 |
    140 | 149 |
    150 | 151 |
    152 |
    153 |

    Developed by Dale J. Barr.

    154 |
    155 | 156 |
    157 |

    Site built with pkgdown 1.3.0.

    158 |
    159 |
    160 |
    161 | 162 | 163 | 164 | 165 | 166 | 167 | -------------------------------------------------------------------------------- /docs/reference/fac_counts.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Calculate marginal and cell counts for factorially-designed data — fac_counts • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 48 | 49 | 50 | 51 | 52 | 53 |
    54 |
    55 | 90 | 91 | 92 |
    93 | 94 |
    95 |
    96 |
    97 |

    Calculate marginal and cell counts for factorially-designed data

    98 | 99 | 100 |
    101 | 102 |
    103 | 104 |

    Calculate marginal and cell counts for factorially-designed data

    105 | 106 |
    107 | 108 | 
    fac_counts(iv_names, dat, unit_names = c("subj_id", "item_id"))
    109 | 110 |

    Arguments

    111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 |
    iv_names

    Names of independent variables in data.frame given by dat.

    dat

    A data frame

    unit_names

    Names of the fields containing sampling units (subjects, items)

    126 | 127 |

    Value

    128 | 129 |

    a list, the elements of which have the marginal/cell counts for each factor in the design

    130 | 131 | 132 |
    133 | 142 |
    143 | 144 |
    145 |
    146 |

    Developed by Dale J. Barr.

    147 |
    148 | 149 |
    150 |

    Site built with pkgdown 1.3.0.

    151 |
    152 |
    153 |
    154 | 155 | 156 | 157 | 158 | 159 | 160 | -------------------------------------------------------------------------------- /docs/reference/gen_pop.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Generate population for data simulation — gen_pop • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 48 | 49 | 50 | 51 | 52 | 53 |
    54 |
    55 | 90 | 91 | 92 |
    93 | 94 |
    95 |
    96 |
    97 |

    Generate population for data simulation

    98 | 99 | 100 |
    101 | 102 |
    103 | 104 |

    Generate population for data simulation

    105 | 106 |
    107 | 108 | 
    gen_pop(design_args, n_subj = NULL, fixed_ranges = NULL,
109 |   var_range = c(0, 3), err_range = c(0, 6))
    110 | 111 |

    Arguments

    112 | 113 | 114 | 115 | 116 | 119 | 120 | 121 | 122 | 124 | 125 | 126 | 127 | 129 | 130 | 131 | 132 | 134 | 135 | 136 | 137 | 138 | 139 |
    design_args

    list specifying experimental design (see 117 | stim_lists); for best results, n_item should be 118 | 2*minimum number of items needed for the design

    n_subj

    number of subjects (for defining random effects 123 | structure; for best results, should be 2*number of stimulus lists

    fixed_ranges

    list of 2-element vectors (min, max) defining 128 | ranges of fixed effect parameters

    var_range

    2-element vector defining range (min, max) of 133 | random effect variances

    err_range

    2-element vector defining range of error variance

    140 | 141 |

    Value

    142 | 143 |

    list with parameters for data generation

    144 | 145 |

    See also

    146 | 147 |

    sim_norm

    148 | 149 | 150 |

    Examples

    151 | 
    
152 | dargs <- list(ivs = c(A = 2, B = 2), n_item = 8)
153 | gen_pop(dargs, 8)
    #> $fixed
154 | #> (Intercept)          A2          B2       A2:B2 
155 | #>   2.1401918   0.1956483   1.0626204   2.4755983 
156 | #> 
157 | #> $subj_rfx
158 | #>             (Intercept)         A2          B2      A2:B2
159 | #> (Intercept)  1.90939684 -1.2258088  0.09597889  0.1521171
160 | #> A2          -1.22580877  1.4370736 -0.16513874  0.4781659
161 | #> B2           0.09597889 -0.1651387  1.29651377 -1.3198060
162 | #> A2:B2        0.15211706  0.4781659 -1.31980598  2.1193015
163 | #> 
164 | #> $item_rfx
165 | #>             (Intercept)        A2          B2      A2:B2
166 | #> (Intercept)  1.59472062 1.1896595 -0.01136419  0.8764575
167 | #> A2           1.18965948 1.4809110  0.98153066  0.5899103
168 | #> B2          -0.01136419 0.9815307  2.33792588 -0.2303857
169 | #> A2:B2        0.87645749 0.5899103 -0.23038566  0.6125350
170 | #> 
171 | #> $err_var
172 | #> [1] 2.421229
173 | #>
    174 |
    175 | 188 |
    189 | 190 |
    191 |
    192 |

    Developed by Dale J. Barr.

    193 |
    194 | 195 |
    196 |

    Site built with pkgdown 1.3.0.

    197 |
    198 |
    199 |
    200 | 201 | 202 | 203 | 204 | 205 | 206 | -------------------------------------------------------------------------------- /docs/reference/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Function reference • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 45 | 46 | 47 | 48 | 49 | 50 |
    51 |
    52 | 87 | 88 | 89 |
    90 | 91 |
    92 |
    93 |
    94 |

    Reference

    95 |
    96 | 97 | 98 | 99 | 100 | 101 | 102 | 103 | 104 | 105 | 106 | 107 | 111 | 112 | 113 | 114 | 117 | 118 | 119 | 120 | 123 | 124 | 125 | 126 | 129 | 130 | 131 | 132 | 135 | 136 | 137 | 138 | 141 | 142 | 143 | 144 | 147 | 148 | 149 | 150 | 153 | 154 | 155 | 156 | 159 | 160 | 161 | 162 | 165 | 166 | 167 | 168 | 171 | 172 | 173 | 174 |
    108 |

    All functions

    109 |

    110 |
    115 |

    compose_data()

    116 |

    Compose response data from fixed and random effects

    121 |

    contr.dev()

    122 |

    Deviation-Coded Contrast Matrices

    127 |

    design_formula()

    128 |

    Get the GLM formula for a factorially-designed experiment

    133 |

    fac_counts()

    134 |

    Calculate marginal and cell counts for factorially-designed data

    139 |

    gen_pop()

    140 |

    Generate population for data simulation

    145 |

    sim_norm()

    146 |

    Sample data from population with normal error variance

    151 |

    stim_lists()

    152 |

    Generate stimulus presentation lists

    157 |

    term_names()

    158 |

    Get names for predictors in factorial design

    163 |

    trial_lists()

    164 |

    Generate trial lists from a stimulus presentation lists

    169 |

    with_dev_pred()

    170 |

    Generate numerical deviation-coded predictors

    175 |
    176 | 177 | 183 |
    184 | 185 |
    186 |
    187 |

    Developed by Dale J. Barr.

    188 |
    189 | 190 |
    191 |

    Site built with pkgdown 1.3.0.

    192 |
    193 |
    194 |
    195 | 196 | 197 | 198 | 199 | 200 | 201 | -------------------------------------------------------------------------------- /docs/reference/sim_norm.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Sample data from population with normal error variance — sim_norm • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 48 | 49 | 50 | 51 | 52 | 53 |
    54 |
    55 | 90 | 91 | 92 |
    93 | 94 |
    95 |
    96 |
    97 |

    Sample data from population with normal error variance

    98 | 99 | 100 |
    101 | 102 |
    103 | 104 |

    Sample data from population with normal error variance

    105 | 106 |
    107 | 108 | 
    sim_norm(design_args, n_subj, params, contr_type = "contr.dev",
109 |   verbose = FALSE)
    110 | 111 |

    Arguments

    112 | 113 | 114 | 115 | 116 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 130 | 131 | 132 | 133 | 135 | 136 | 137 | 138 | 140 | 141 |
    design_args

    experiment design (see 117 | stim_lists); must contain sub-element n_item 118 | defining the number of items.

    n_subj

    number of subjects

    params

    list with parameters defining population 127 | (fixed, subj_rfx, item_rfx), and 128 | err_var; normally generated by a call to 129 | gen_pop.

    contr_type

    name of function defining IV contrast type; see 134 | ?contrasts

    verbose

    whether to return explicit information about 139 | individual random effects and residual error

    142 | 143 |

    Value

    144 | 145 |

    A data frame with simulated data

    146 | 147 |

    See also

    148 | 149 |

    gen_pop

    150 | 151 | 152 |

    Examples

    153 | 
    design_args <- list(ivs = c(A = 2, B = 3), n_item = 18)
154 | pop_params <- gen_pop(design_args, 12)
155 | 
156 | dat <- sim_norm(design_args, 12, pop_params)
    157 |
    158 | 171 |
    172 | 173 |
    174 |
    175 |

    Developed by Dale J. Barr.

    176 |
    177 | 178 |
    179 |

    Site built with pkgdown 1.3.0.

    180 |
    181 |
    182 |
    183 | 184 | 185 | 186 | 187 | 188 | 189 | -------------------------------------------------------------------------------- /docs/reference/term_names.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Get names for predictors in factorial design — term_names • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 46 | 50 | 51 | 52 | 53 | 54 | 55 |
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    Get names for predictors in factorial design

    100 | 101 | 102 |
    103 | 104 |
    105 | 106 |

    Get the names for the numerical predictors corresponding to all 107 | main effects and interactions of categorical IVs in a factorial 108 | design.

    109 | 110 |
    111 | 112 | 
    term_names(design_args, design_formula = NULL,
113 |   contr_type = "contr.dev")
    114 | 115 |

    Arguments

    116 | 117 | 118 | 119 | 120 | 121 | 122 | 123 | 124 | 125 | 126 | 127 | 128 | 129 | 130 |
    design_args

    A list with experimental design information (see link{stim_lists})

    design_formula

    A formula (default NULL, constructs from design_args)

    contr_type

    Name of formula for generating contrasts (default "contr.dev")

    131 | 132 |

    Value

    133 | 134 |

    A character vector with names of all the terms

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    138 | 147 |
    148 | 149 |
    150 |
    151 |

    Developed by Dale J. Barr.

    152 |
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    155 |

    Site built with pkgdown 1.3.0.

    156 |
    157 |
    158 |
    159 | 160 | 161 | 162 | 163 | 164 | 165 | -------------------------------------------------------------------------------- /docs/reference/trial_lists.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Generate trial lists from a stimulus presentation lists — trial_lists • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 45 | 49 | 50 | 51 | 52 | 53 | 54 |
    55 |
    56 | 91 | 92 | 93 |
    94 | 95 |
    96 |
    97 |
    98 |

    Generate trial lists from a stimulus presentation lists

    99 | 100 | 101 |
    102 | 103 |
    104 | 105 |

    Merge stimulus presentation lists with subject data to create a 106 | trial list.

    107 | 108 |
    109 | 110 | 
    trial_lists(design_args, subjects = NULL, seq_assign = FALSE)
    111 | 112 |

    Arguments

    113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 | 126 | 127 | 128 | 129 | 131 | 132 |
    design_args

    Stimulus presentation lists (see stim_lists).

    subjects

    One of the following three: (1) an integer 122 | specifying the desired number of subjects (must be a multiple of 123 | number of stimulus lists); (2) a data frame with assignment 124 | information (must include a column subj_id); or (3) 125 | NULL, in which case there will be one subject per list.

    seq_assign

    If TRUE, assignment of subjects to lists will 130 | be sequential rather than random (default is FALSE)

    133 | 134 |

    Value

    135 | 136 |

    A data frame containing all trial information.

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    152 |
    153 |

    Developed by Dale J. Barr.

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    Site built with pkgdown 1.3.0.

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    161 | 162 | 163 | 164 | 165 | 166 | 167 | -------------------------------------------------------------------------------- /docs/reference/with_dev_pred.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | Generate numerical deviation-coded predictors — with_dev_pred • funfact 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 | 41 | 42 | 43 | 44 | 48 | 49 | 50 | 51 | 52 | 53 |
    54 |
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    93 | 94 |
    95 |
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    Generate numerical deviation-coded predictors

    98 | 99 | 100 |
    101 | 102 |
    103 | 104 |

    Add deviation-coded predictors to data frame.

    105 | 106 |
    107 | 108 | 
    with_dev_pred(dat, iv_names = NULL)
    109 | 110 |

    Arguments

    111 | 112 | 113 | 114 | 115 | 116 | 117 | 118 | 119 | 120 | 121 |
    dat

    A data frame with columns containing factors to be converted.

    iv_names

    Names of the variables to be converted.

    122 | 123 |

    Value

    124 | 125 |

    A data frame including additional deviation coded predictors.

    126 | 127 | 128 |

    Examples

    129 | 
    with_dev_pred(stim_lists(list(ivs = c(A = 3))), "A")
    #>   list_id item_id  A        AA2        AA3
130 | #> 1       1       1 A1 -0.3333333 -0.3333333
131 | #> 2       1       2 A2  0.6666667 -0.3333333
132 | #> 3       1       3 A3 -0.3333333  0.6666667
133 | #> 4       2       1 A2  0.6666667 -0.3333333
134 | #> 5       2       2 A3 -0.3333333  0.6666667
135 | #> 6       2       3 A1 -0.3333333 -0.3333333
136 | #> 7       3       1 A3 -0.3333333  0.6666667
137 | #> 8       3       2 A1 -0.3333333 -0.3333333
138 | #> 9       3       3 A2  0.6666667 -0.3333333
    139 |
    140 | 151 |
    152 | 153 | 162 |
    163 | 164 | 165 | 166 | 167 | 168 | 169 | -------------------------------------------------------------------------------- /man/compose_data.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stim_lists.R 3 | \name{compose_data} 4 | \alias{compose_data} 5 | \title{Compose response data from fixed and random effects} 6 | \usage{ 7 | compose_data(design_args, fixed = NULL, subj_rmx = NULL, 8 | item_rmx = NULL, verbose = FALSE, contr_type = "contr.dev") 9 | } 10 | \arguments{ 11 | \item{design_args}{List containing information about the experiment 12 | design; see \code{\link{stim_lists}}} 13 | 14 | \item{fixed}{vector of fixed effects} 15 | 16 | \item{subj_rmx}{matrix of by-subject random effects} 17 | 18 | \item{item_rmx}{matrix of by-item random effects} 19 | 20 | \item{verbose}{give debugging info (default = \code{FALSE})} 21 | 22 | \item{contr_type}{contrast type (default "contr.dev"); see ?contrasts} 23 | } 24 | \value{ 25 | a data frame containing response variable \code{Y}, the 26 | linear sum of all fixed and random effects. 27 | } 28 | \description{ 29 | Compose response data from fixed and random effects 30 | } 31 | \details{ 32 | This will add together all of the fixed and random effects 33 | according to the linear model specified by the design. Note, 34 | however, that it does not add in any residual noise; for that, use 35 | the function \code{\link{sim_norm}}. 36 | } 37 | -------------------------------------------------------------------------------- /man/contr.dev.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/misc.R 3 | \name{contr.dev} 4 | \alias{contr.dev} 5 | \title{Deviation-Coded Contrast Matrices} 6 | \usage{ 7 | contr.dev(n, base = 1, contrasts = TRUE) 8 | } 9 | \arguments{ 10 | \item{n}{a vector of levels for a factor, or the number of levels.} 11 | 12 | \item{base}{an integer specifying which group is considered the 13 | baseline group. Ignored if 'contrasts' is \code{FALSE}.} 14 | 15 | \item{contrasts}{a logical indicating whether contrasts should be computed.} 16 | } 17 | \description{ 18 | Return a matrix of deviation-coded contrasts. 19 | } 20 | -------------------------------------------------------------------------------- /man/design_formula.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/misc.R 3 | \name{design_formula} 4 | \alias{design_formula} 5 | \title{Get the GLM formula for a factorially-designed experiment} 6 | \usage{ 7 | design_formula(design_args, n_subj = NULL, dv_name = NULL, 8 | lme4_format = TRUE) 9 | } 10 | \arguments{ 11 | \item{design_args}{A list with experimental design information (see \code{link{stim_lists}})} 12 | 13 | \item{n_subj}{Number of subjects} 14 | 15 | \item{dv_name}{Name of dependent variable; \code{NULL} (default) for one-sided formula} 16 | 17 | \item{lme4_format}{Do you want the results combined as the model formula for a \code{lme4} model? (default \code{TRUE})} 18 | } 19 | \value{ 20 | A formula, character string, or list, depending 21 | } 22 | \description{ 23 | Get the formula corresponding to the general linear model for a 24 | factorially designed experiment, with maximal random effects. 25 | } 26 | -------------------------------------------------------------------------------- /man/fac_counts.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/misc.R 3 | \name{fac_counts} 4 | \alias{fac_counts} 5 | \title{Calculate marginal and cell counts for factorially-designed data} 6 | \usage{ 7 | fac_counts(iv_names, dat, unit_names = c("subj_id", "item_id")) 8 | } 9 | \arguments{ 10 | \item{iv_names}{Names of independent variables in data.frame given by \code{dat}.} 11 | 12 | \item{dat}{A data frame} 13 | 14 | \item{unit_names}{Names of the fields containing sampling units (subjects, items)} 15 | } 16 | \value{ 17 | a list, the elements of which have the marginal/cell counts for each factor in the design 18 | } 19 | \description{ 20 | Calculate marginal and cell counts for factorially-designed data 21 | } 22 | -------------------------------------------------------------------------------- /man/gen_pop.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/sim_data.R 3 | \name{gen_pop} 4 | \alias{gen_pop} 5 | \title{Generate population for data simulation} 6 | \usage{ 7 | gen_pop(design_args, n_subj = NULL, fixed_ranges = NULL, 8 | var_range = c(0, 3), err_range = c(0, 6)) 9 | } 10 | \arguments{ 11 | \item{design_args}{list specifying experimental design (see 12 | \code{\link{stim_lists}}); for best results, n_item should be 13 | 2*minimum number of items needed for the design} 14 | 15 | \item{n_subj}{number of subjects (for defining random effects 16 | structure; for best results, should be 2*number of stimulus lists} 17 | 18 | \item{fixed_ranges}{list of 2-element vectors (min, max) defining 19 | ranges of fixed effect parameters} 20 | 21 | \item{var_range}{2-element vector defining range (min, max) of 22 | random effect variances} 23 | 24 | \item{err_range}{2-element vector defining range of error variance} 25 | } 26 | \value{ 27 | list with parameters for data generation 28 | } 29 | \description{ 30 | Generate population for data simulation 31 | } 32 | \examples{ 33 | 34 | dargs <- list(ivs = c(A = 2, B = 2), n_item = 8) 35 | gen_pop(dargs, 8) 36 | } 37 | \seealso{ 38 | \code{\link{sim_norm}} 39 | } 40 | -------------------------------------------------------------------------------- /man/sim_norm.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/sim_data.R 3 | \name{sim_norm} 4 | \alias{sim_norm} 5 | \title{Sample data from population with normal error variance} 6 | \usage{ 7 | sim_norm(design_args, n_subj, params, contr_type = "contr.dev", 8 | verbose = FALSE) 9 | } 10 | \arguments{ 11 | \item{design_args}{experiment design (see 12 | \code{\link{stim_lists}}); must contain sub-element \code{n_item} 13 | defining the number of items.} 14 | 15 | \item{n_subj}{number of subjects} 16 | 17 | \item{params}{list with parameters defining population 18 | (\code{fixed}, \code{subj_rfx}, \code{item_rfx}), and 19 | \code{err_var}; normally generated by a call to 20 | \code{\link{gen_pop}}.} 21 | 22 | \item{contr_type}{name of function defining IV contrast type; see 23 | \code{?contrasts}} 24 | 25 | \item{verbose}{whether to return explicit information about 26 | individual random effects and residual error} 27 | } 28 | \value{ 29 | A data frame with simulated data 30 | } 31 | \description{ 32 | Sample data from population with normal error variance 33 | } 34 | \examples{ 35 | design_args <- list(ivs = c(A = 2, B = 3), n_item = 18) 36 | pop_params <- gen_pop(design_args, 12) 37 | 38 | dat <- sim_norm(design_args, 12, pop_params) 39 | } 40 | \seealso{ 41 | \code{\link{gen_pop}} 42 | } 43 | -------------------------------------------------------------------------------- /man/stim_lists.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stim_lists.R 3 | \name{stim_lists} 4 | \alias{stim_lists} 5 | \title{Generate stimulus presentation lists} 6 | \usage{ 7 | stim_lists(design_args, as_one = TRUE) 8 | } 9 | \arguments{ 10 | \item{design_args}{A list describing the experimental design, which 11 | must have an element \code{ivs}, giving a named list of independent 12 | variables, with each list element a vector giving the levels of 13 | that IV, or a single number stating the number of desired levels. 14 | If any IVs are administered between-subjects or between-items, 15 | these should be named in list elements \code{between_subj} and 16 | \code{between_item} respectively. The argument \code{design_args} 17 | also can optionally include the following two elements: 18 | \code{n_item}, the desired number of stimulus items, which if 19 | unspecified, will result in lists with the minimum possible number 20 | of items; and \code{n_rep}, the number of repetitions of each 21 | stimulus item for each participant (default 1).} 22 | 23 | \item{as_one}{boolean (default \code{TRUE}) specifying whether the 24 | presentation lists are to be returned as a single data frame or as 25 | elements in a list object} 26 | } 27 | \value{ 28 | a single \code{data.frame} (default) with each list 29 | identified by \code{list_id} or a \code{list} of dataframes, 30 | depending on the value of \code{as_one} 31 | } 32 | \description{ 33 | Generates counterbalanced presentation lists for factorially 34 | designed experiments involving stimulus presentation 35 | } 36 | \examples{ 37 | stim_lists(list(ivs = c(A = 2, B = 2))) # 2x2 within-subjects within-item 38 | 39 | stim_lists(list(ivs = c(A = 2, B = 2, n_item = 16))) # same but w/more items 40 | 41 | stim_lists(list(ivs = c(A = 2, B = 2, n_item = 16, n_rep = 3))) 42 | 43 | # mixed by subject, fully within by item 44 | stim_lists(list(ivs = list(group = c("adult", "child"), 45 | task = c("easy", "hard")), 46 | between_subj = "group", 47 | n_item = 12)) 48 | 49 | # mixed by subject, mixed by item 50 | stim_lists(list(ivs = c(A = 2, B = 2), 51 | between_subj = "A", 52 | between_item = "B")) 53 | } 54 | -------------------------------------------------------------------------------- /man/term_names.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/misc.R 3 | \name{term_names} 4 | \alias{term_names} 5 | \title{Get names for predictors in factorial design} 6 | \usage{ 7 | term_names(design_args, design_formula = NULL, 8 | contr_type = "contr.dev") 9 | } 10 | \arguments{ 11 | \item{design_args}{A list with experimental design information (see \code{link{stim_lists}})} 12 | 13 | \item{design_formula}{A formula (default NULL, constructs from \code{design_args})} 14 | 15 | \item{contr_type}{Name of formula for generating contrasts (default "contr.dev")} 16 | } 17 | \value{ 18 | A character vector with names of all the terms 19 | } 20 | \description{ 21 | Get the names for the numerical predictors corresponding to all 22 | main effects and interactions of categorical IVs in a factorial 23 | design. 24 | } 25 | -------------------------------------------------------------------------------- /man/trial_lists.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/stim_lists.R 3 | \name{trial_lists} 4 | \alias{trial_lists} 5 | \title{Generate trial lists from a stimulus presentation lists} 6 | \usage{ 7 | trial_lists(design_args, subjects = NULL, seq_assign = FALSE) 8 | } 9 | \arguments{ 10 | \item{design_args}{Stimulus presentation lists (see \code{\link{stim_lists}}).} 11 | 12 | \item{subjects}{One of the following three: (1) an integer 13 | specifying the desired number of subjects (must be a multiple of 14 | number of stimulus lists); (2) a data frame with assignment 15 | information (must include a column \code{subj_id}); or (3) 16 | \code{NULL}, in which case there will be one subject per list.} 17 | 18 | \item{seq_assign}{If TRUE, assignment of subjects to lists will 19 | be sequential rather than random (default is FALSE)} 20 | } 21 | \value{ 22 | A data frame containing all trial information. 23 | } 24 | \description{ 25 | Merge stimulus presentation lists with subject data to create a 26 | trial list. 27 | } 28 | -------------------------------------------------------------------------------- /man/with_dev_pred.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/misc.R 3 | \name{with_dev_pred} 4 | \alias{with_dev_pred} 5 | \title{Generate numerical deviation-coded predictors} 6 | \usage{ 7 | with_dev_pred(dat, iv_names = NULL) 8 | } 9 | \arguments{ 10 | \item{dat}{A data frame with columns containing factors to be converted.} 11 | 12 | \item{iv_names}{Names of the variables to be converted.} 13 | } 14 | \value{ 15 | A data frame including additional deviation coded predictors. 16 | } 17 | \description{ 18 | Add deviation-coded predictors to data frame. 19 | } 20 | \examples{ 21 | with_dev_pred(stim_lists(list(ivs = c(A = 3))), "A") 22 | } 23 | --------------------------------------------------------------------------------