├── .DS_Store ├── .Rbuildignore ├── .gitignore ├── DESCRIPTION ├── LICENSE ├── NAMESPACE ├── R ├── .DS_Store ├── conRelPlot.r ├── itemDistribution.r ├── itemInfoPlot.r ├── itemfitPlot.r ├── itempersonmap.r ├── personDistribution.r ├── personfitPlot.r ├── scaleCharPlot.r ├── sim_irt.r ├── summaryPlot.r ├── testInfoCompare.r ├── testInfoPlot.r ├── tracePlot.r └── utils-pipe.r ├── README.md ├── README.rmd ├── ggmirt.Rproj ├── inst └── figures │ └── logo.png └── man ├── conRelPlot.Rd ├── figures ├── README-unnamed-chunk-10-1.png ├── README-unnamed-chunk-2-1.png ├── README-unnamed-chunk-2-2.png ├── README-unnamed-chunk-3-1.png ├── README-unnamed-chunk-4-1.png ├── README-unnamed-chunk-5-1.png ├── README-unnamed-chunk-6-1.png ├── README-unnamed-chunk-7-1.png ├── README-unnamed-chunk-8-1.png └── README-unnamed-chunk-9-1.png ├── itemDist.Rd ├── itemInfoPlot.Rd ├── itemfitPlot.Rd ├── itempersonMap.Rd ├── personDist.Rd ├── personfitPlot.Rd ├── pipe.Rd ├── scaleCharPlot.Rd ├── sim_irt.Rd ├── summaryPlot.Rd ├── testInfoCompare.Rd ├── testInfoPlot.Rd └── tracePlot.Rd /.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/.DS_Store -------------------------------------------------------------------------------- /.Rbuildignore: -------------------------------------------------------------------------------- 1 | ^.*\.Rproj$ 2 | ^\.Rproj\.user$ 3 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | .Rproj.user 2 | .Rhistory 3 | .RData 4 | .Ruserdata 5 | .DS_Store 6 | -------------------------------------------------------------------------------- /DESCRIPTION: -------------------------------------------------------------------------------- 1 | Package: ggmirt 2 | Title: Plotting functions to extend "mirt" for IRT analyses 3 | Version: 0.1.0 4 | Authors@R: 5 | person(given = "Philipp K.", 6 | family = "Masur", 7 | role = c("aut", "cre"), 8 | email = "phil.masur@gmail.com", 9 | comment = c(ORCID = "https://orcid.org/0000-0003-3065-7305")) 10 | Description: This package provides convenient plotting functions to extend the great package "mirt" with ggplot-based plotting functions. Additionally, it includes some additional summary functions. 11 | License: GPL-3 12 | Imports: 13 | mirt, 14 | magrittr, 15 | dplyr, 16 | tidyr, 17 | ggplot2 18 | Suggests: 19 | tidyverse 20 | Encoding: UTF-8 21 | LazyData: true 22 | Roxygen: list(markdown = TRUE) 23 | RoxygenNote: 7.2.0 24 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /NAMESPACE: -------------------------------------------------------------------------------- 1 | # Generated by roxygen2: do not edit by hand 2 | 3 | export("%>%") 4 | export(conRelPlot) 5 | export(itemDist) 6 | export(itemInfoPlot) 7 | export(itemfitPlot) 8 | export(itempersonMap) 9 | export(personDist) 10 | export(personfitPlot) 11 | export(scaleCharPlot) 12 | export(sim_irt) 13 | export(summaryPlot) 14 | export(testInfoCompare) 15 | export(testInfoPlot) 16 | export(tracePlot) 17 | import(dplyr) 18 | import(ggplot2) 19 | import(mirt) 20 | import(tidyr) 21 | importFrom(magrittr,"%>%") 22 | -------------------------------------------------------------------------------- /R/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/R/.DS_Store -------------------------------------------------------------------------------- /R/conRelPlot.r: -------------------------------------------------------------------------------- 1 | #' Plotting conditional reliability 2 | #' 3 | #' This function takes a fitted mirt-model and visualizes a conditional reliability curve. Heavily inspired by code from Phil Chalmers (author or 'mirt') 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param theta_range range to be shown on the x-axis 8 | #' @param color color of the line 9 | #' @param title title for the plot (defaults to "Conditional Reliability") 10 | #' 11 | #' @return a ggplot 12 | #' @import ggplot2 13 | #' @import dplyr 14 | #' @import tidyr 15 | #' @import mirt 16 | #' @export 17 | #' 18 | #' @examples 19 | #' library(mirt) 20 | #' library(ggmirt) 21 | #' data <- expand.table(LSAT7) 22 | #' (mod <- mirt(data, 1)) 23 | #' 24 | #' conRelPlot(mod) 25 | conRelPlot <- function(model, 26 | theta_range = c(-4,4), 27 | color = "red", 28 | title = "Conditional Reliability") { 29 | 30 | # helper functions 31 | 32 | computeItemtrace <- function(pars, Theta, itemloc, offterm = matrix(0L, 1L, length(itemloc)-1L), 33 | CUSTOM.IND, pis = NULL){ 34 | if(is.null(pis)){ 35 | itemtrace <- .Call('computeItemTrace', pars, Theta, itemloc, offterm) 36 | if(length(CUSTOM.IND)){ 37 | for(i in CUSTOM.IND) 38 | itemtrace[,itemloc[i]:(itemloc[i+1L] - 1L)] <- ProbTrace(pars[[i]], Theta=Theta) 39 | } 40 | } else { 41 | tmp_itemtrace <- vector('list', length(pis)) 42 | for(g in seq_len(length(pis))){ 43 | tmp_itemtrace[[g]] <- .Call('computeItemTrace', pars[[g]]@ParObjects$pars, Theta, itemloc, offterm) 44 | if(length(CUSTOM.IND)){ 45 | for(i in CUSTOM.IND) 46 | tmp_itemtrace[[g]][,itemloc[i]:(itemloc[i+1L] - 1L)] <- ProbTrace(pars[[g]]@ParObjects$pars[[i]], Theta=Theta) 47 | } 48 | } 49 | itemtrace <- do.call(rbind, tmp_itemtrace) 50 | } 51 | return(itemtrace) 52 | } 53 | 54 | ExtractGroupPars <- function(x){ 55 | if(x@itemclass < 0L) return(list(gmeans=0, gcov=matrix(1))) 56 | nfact <- x@nfact 57 | gmeans <- x@par[seq_len(nfact)] 58 | phi_matches <- grepl("PHI", x@parnames) 59 | if (x@dentype == "Davidian") { 60 | phi <- x@par[phi_matches] 61 | tmp <- x@par[-c(seq_len(nfact), which(phi_matches))] 62 | gcov <- matrix(0, nfact, nfact) 63 | gcov[lower.tri(gcov, diag=TRUE)] <- tmp 64 | gcov <- makeSymMat(gcov) 65 | return(list(gmeans=gmeans, gcov=gcov, phi=phi)) 66 | } else { 67 | par <- x@par 68 | if(x@dentype == "mixture") par <- par[-length(par)] # drop pi 69 | tmp <- par[-seq_len(nfact)] 70 | gcov <- matrix(0, nfact, nfact) 71 | gcov[lower.tri(gcov, diag=TRUE)] <- tmp 72 | gcov <- makeSymMat(gcov) 73 | return(list(gmeans=gmeans, gcov=gcov)) 74 | } 75 | } 76 | 77 | makeSymMat <- function(mat){ 78 | if(ncol(mat) > 1L){ 79 | mat[is.na(mat)] <- 0 80 | mat <- mat + t(mat) - diag(diag(mat)) 81 | } 82 | mat 83 | } 84 | 85 | # Actual computation 86 | nfact <- model@Model$nfact 87 | J <- model@Data$nitems 88 | theta <- seq(theta_range[1],theta_range[2], by = .01) 89 | ThetaFull <- Theta <- thetaComb(theta, nfact) 90 | info <- testinfo(model, ThetaFull) 91 | itemtrace <- computeItemtrace(model@ParObjects$pars, ThetaFull, model@Model$itemloc, 92 | CUSTOM.IND=model@Internals$CUSTOM.IND) 93 | mins <- model@Data$mins 94 | maxs <- extract.mirt(model, 'K') + mins - 1 95 | gp <- ExtractGroupPars(model@ParObjects$pars[[J+1]]) 96 | score <- c() 97 | for(i in 1:J) 98 | score <- c(score, (0:(model@Data$K[i]-1) + mins[i]) * (i %in% c(1:J))) 99 | score <- matrix(score, nrow(itemtrace), ncol(itemtrace), byrow = TRUE) 100 | plt <- data.frame(cbind(info,score=rowSums(score*itemtrace),Theta=Theta)) 101 | colnames(plt) <- c("info", "score", "Theta") 102 | plt$SE <- 1 / sqrt(plt$info) 103 | plt$rxx <- plt$info / (plt$info + 1/gp$gcov[1L,1L]) 104 | 105 | ggplot(plt, aes(x = Theta, y = rxx)) + 106 | geom_line(color = color) + 107 | ylim(0, 1) + 108 | theme_minimal() + 109 | labs(title = title, x = expression(theta), y = expression(r[xx](theta))) 110 | 111 | } 112 | -------------------------------------------------------------------------------- /R/itemDistribution.r: -------------------------------------------------------------------------------- 1 | #' Item difficulty distribution 2 | #' 3 | #' This function requires a fitted mirt-model of class `SingleGroupClass` to visualize item difficulty distribution. Currently only works for unidimensional models. 4 | #' 5 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 6 | #' @param ... any argument passed to `geom_point()` 7 | #' 8 | #' @return a ggplot object. 9 | #' @import ggplot2 10 | #' @import dplyr 11 | #' @import tidyr 12 | #' @import mirt 13 | #' @export 14 | #' 15 | #' @examples 16 | #' # Loading packages 17 | #' library(mirt) 18 | #' library(ggmirt) 19 | #' 20 | #' # Getting data 21 | #' data <- expand.table(LSAT7) 22 | #' 23 | #' # Fitting a model 24 | #' (mod <- mirt(data, 1)) 25 | #' 26 | #' # Simple plot 27 | #' itemDist(mod) 28 | #' 29 | #' # Customized plot 30 | #' itemDist(mod, size = 3, shape = 17, color = "blue") 31 | #' 32 | itemDist <- function(model, 33 | theta_range = c(-4, 4), 34 | ...) { 35 | 36 | item.params <- mirt::coef(model, IRTpars = TRUE, simplify = TRUE) %>% 37 | as.data.frame %>% 38 | tibble::rownames_to_column("items") 39 | 40 | p <- item.params %>% 41 | mutate(items = forcats::fct_reorder(items, items.b)) %>% 42 | ggplot(aes(y = items, x = items.b)) + 43 | geom_point(...) 44 | 45 | p + xlim(theta_range) + theme_minimal() + labs(x = expression(theta), y = "") 46 | } 47 | 48 | -------------------------------------------------------------------------------- /R/itemInfoPlot.r: -------------------------------------------------------------------------------- 1 | #' Plotting item information curves 2 | #' 3 | #' This function takes a fitted mirt-model and visualizes items information curves. 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param items numerical vector indicating which items to plot. 8 | #' @param facet Should all items be shown in one plot, or each item received its individal facet? 9 | #' @param theta_range range to be shown on the x-axis 10 | #' @param title title for the plot (defaults to "Item Characteristic Curves") 11 | #' 12 | #' @return a ggplot 13 | #' @import ggplot2 14 | #' @import dplyr 15 | #' @import tidyr 16 | #' @import mirt 17 | #' @export 18 | #' 19 | #' @examples 20 | #' library(mirt) 21 | #' library(ggmirt) 22 | #' data <- expand.table(LSAT7) 23 | #' (mod <- mirt(data, 1)) 24 | #' 25 | #' itemInfoPlot(mod) 26 | #' 27 | itemInfoPlot <- function(model, 28 | items = NULL, 29 | facet = FALSE, 30 | title = "Item Information Curves", 31 | theta_range = c(-4,4), 32 | legend = FALSE) { 33 | 34 | data <- model@Data$data %>% as.data.frame 35 | 36 | theta_range = seq(theta_range[1], theta_range[2], by = .01) 37 | 38 | test <- NULL 39 | for(i in 1:length(data)){ 40 | theta <- matrix(theta_range) 41 | test[[i]] <- testinfo(model, Theta = theta, which.items = i) 42 | } 43 | 44 | if (!is.null(items)) { 45 | test <- test[items] 46 | } 47 | 48 | names(test) <- paste('item', 1:length(test)) 49 | test <- as.data.frame(test, theta) %>% 50 | tibble::rownames_to_column("theta") %>% 51 | gather(key, value, -theta) %>% 52 | mutate(theta = as.numeric(theta)) 53 | 54 | # final plot 55 | if(isFALSE(facet)) { 56 | p <- ggplot(test, aes(theta, value, colour = key)) + 57 | geom_line() + 58 | labs(x = expression(theta), 59 | y = expression(I(theta)), 60 | title = title, 61 | color = "Item") + 62 | theme_minimal() + 63 | scale_color_brewer(palette = 7) 64 | 65 | if(isFALSE(legend)) { 66 | p <- p + guides(color = FALSE) 67 | # change guides(color = "none") 68 | } 69 | 70 | } else { 71 | p <- ggplot(test, aes(theta, value)) + 72 | geom_line() + 73 | facet_wrap(~key) + 74 | labs(x = expression(theta), 75 | y = expression(I(theta)), 76 | title = title) + 77 | theme_minimal() 78 | } 79 | return(p) 80 | } 81 | 82 | 83 | -------------------------------------------------------------------------------- /R/itemfitPlot.r: -------------------------------------------------------------------------------- 1 | #' Plotting itemfit estimates 2 | #' 3 | #' This function takes a fitted mirt-model and visualizes item infit and outfit estimates. The function builds on `mirt::itemfit()`. Currently only supported `fact_stats = "infit"`. 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param fit_stats a character vector indicating which fit statistics should be computed. See `mirt::infit()` for supported inputs. 8 | #' @param color color of the item points. 9 | #' @param shape shape of the item points 10 | #' @param title title for the plot (defaults to "Item Infit and Outfit Statistics") 11 | #' 12 | #' @return a ggplot 13 | #' 14 | #' @references \itemize{ 15 | #' \item Linacre JM. (2002). What do Infit and Outfit, Mean-square and Standardized mean? Rasch Measurement Transactions, 16(2), p.878. https://www.rasch.org/rmt/rmt162f.htm 16 | #' } 17 | #' 18 | #' @import ggplot2 19 | #' @import dplyr 20 | #' @import tidyr 21 | #' @import mirt 22 | #' @export 23 | #' 24 | #' @examples 25 | #' library(mirt) 26 | #' library(ggmirt) 27 | #' data <- expand.table(LSAT7) 28 | #' (mod <- mirt(data, 1)) 29 | #' 30 | #' itemfitPlot(mod, fit_stats = "infit") 31 | #' 32 | itemfitPlot <- function(model, 33 | fit_stats = "infit", 34 | color = "red", 35 | shape = 17, 36 | title = "Item Infit and Outfit Statistics", 37 | ...) { 38 | 39 | 40 | fit <- mirt::itemfit(model, fit_stats = fit_stats, ...) 41 | 42 | if("infit" %in% names(fit)) { 43 | fit %>% 44 | select(item, infit, outfit) %>% 45 | gather(key, value, -item) %>% 46 | ggplot(aes(x = item, y = value)) + 47 | geom_point(size = 3, color = color, shape = shape) + 48 | geom_line() + 49 | geom_hline(yintercept = .5, color = "darkgrey", linetype = "dashed") + 50 | geom_hline(yintercept = 1, color = "darkgrey") + 51 | geom_hline(yintercept = 1.5, color = "darkgrey", linetype = "dashed") + 52 | scale_y_continuous(breaks = c(.5, 1, 1.5), limits = c(0, 2)) + 53 | facet_grid(~key) + 54 | coord_flip() + 55 | theme_minimal() + 56 | labs(y = "", x = "", caption = "Note: Items with values within 0.5 and 1.5 are considered to be productive for measurement.", 57 | title = title) 58 | } 59 | } 60 | 61 | 62 | 63 | -------------------------------------------------------------------------------- /R/itempersonmap.r: -------------------------------------------------------------------------------- 1 | #' Visualize item person map and scale properties based on Rasch model 2 | #' 3 | #' This function takes a fitted mirt-model and visualizes and plots item-person-map (also known as Kernel-Density Plots or Wright maps) on the left, and add a scale characteristic curve, scale information curve, and a marginal reliability curve on the right. 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param limits range to be shown on the x-axis 8 | #' @param title title for the plot (defaults to "Item-Person-Map") 9 | #' @param margin margins around the top figure. Sometimes one might want to adjust this. 10 | #' @param density logical value indicating whether a smoothed density curve or a standard histogram should be plotted. 11 | #' @param color color of the geoms, defaults to "red". 12 | #' @param shape can be used to change the shape of the geom, defaults to triangles (17). 13 | #' @param size size of the geom, default to 3. 14 | #' @param theme any ggplot theme. 15 | #' @param ... any argument passed to `geom_point()`. 16 | #' 17 | #' @return a plot grid as returned by `cowplot::plot_grid()` 18 | #' @import ggplot2 19 | #' @import dplyr 20 | #' @import tidyr 21 | #' @import mirt 22 | #' @export 23 | #' 24 | #' @examples 25 | #' library(mirt) 26 | #' library(ggmirt) 27 | #' data <- expand.table(LSAT7) 28 | #' (mod <- mirt(data, 1)) 29 | #' 30 | #' itempersonMap(mod) 31 | #' 32 | itempersonMap <- function(model, 33 | theta_range = c(-4,4), 34 | title = "Item Person Map", 35 | margin = c(1,0,-1.5,0), 36 | density = FALSE, 37 | color = "red", 38 | shape = 17, 39 | size = 3, 40 | theme = theme_minimal(), 41 | ...) { 42 | 43 | p1 <- personDist(model, theta_range = theta_range, density = density) + 44 | theme + 45 | theme(plot.margin = unit(margin,"cm")) + 46 | labs(title = title) 47 | p2 <- itemDist(model, theta_range = theta_range, shape = shape, color = color, size = size, ...) + 48 | theme 49 | 50 | 51 | p <- cowplot::plot_grid(p1, p2, 52 | nrow = 2, 53 | rel_heights = c(1.5,2.5), 54 | align = "hv", 55 | axis = "tlbr") 56 | 57 | return(p) 58 | } 59 | 60 | -------------------------------------------------------------------------------- /R/personDistribution.r: -------------------------------------------------------------------------------- 1 | #' Person parameter distribution 2 | #' 3 | #' This function requires a fitted mirt-model of class `SingleGroupClass` to visualize a person parameter distribution (theta levels in the studied population). The resulting ggplot can be further customized (e.g., with regard to theme, labels, etc.). It works with both uni- and multidimensional models. 4 | #' 5 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 6 | #' @param density logical value indicating whether a smoothed density curve or a standard histogram should be plotted. 7 | #' @param bins number of bins to be plotted in the histogram 8 | #' 9 | #' @return a ggplot object. 10 | #' @import ggplot2 11 | #' @import dplyr 12 | #' @import tidyr 13 | #' @import mirt 14 | #' @export 15 | #' 16 | #' @examples 17 | #' # Loading packages 18 | #' library(mirt) 19 | #' library(ggmirt) 20 | #' 21 | #' # Getting data 22 | #' data <- expand.table(LSAT7) 23 | #' 24 | #' # Fitting a model 25 | #' (mod <- mirt(data, 1)) 26 | #' 27 | #' # Simple plot 28 | #' personDist(mod) 29 | #' personDist(mod, density = TRUE) 30 | #' 31 | #' # Customized plot 32 | #' personDist(mod, theta_range = c(-3, 3), bins = 10) + 33 | #' theme_classic() 34 | personDist <- function(model, 35 | theta_range = c(-4, 4), 36 | density = FALSE, 37 | bins = 35) { 38 | 39 | person.params <- fscores(model, QMC = TRUE) %>% 40 | as.data.frame() 41 | 42 | if(length(person.params) != 1) { 43 | p <- person.params %>% 44 | tidyr::pivot_longer(names(.), names_to = "dimension") %>% 45 | ggplot(aes(x = value, fill = dimension)) 46 | 47 | } else { 48 | 49 | p <- person.params %>% 50 | pivot_longer(names(.), names_to = "dimension") %>% 51 | ggplot(aes(x = value, fill = dimension)) + 52 | guides(fill = "none") 53 | } 54 | 55 | if(isTRUE(density)) { 56 | 57 | p <- p + geom_density() 58 | 59 | } else { 60 | 61 | p <- p + geom_histogram(bins = bins, color = "white") 62 | } 63 | 64 | 65 | p + xlim(theta_range) + theme_minimal() + labs(x = expression(theta)) 66 | 67 | } 68 | -------------------------------------------------------------------------------- /R/personfitPlot.r: -------------------------------------------------------------------------------- 1 | #' Plotting personfit estimates 2 | #' 3 | #' This function takes a fitted mirt-model and visualizes person infit and outfit estimates. The function builds on `mirt::itemfit()`. The basic idea is to visualize how many individuals in the sample do not show a response pattern that aligns with the suggested model. At best, the number of non-fitting response patterns is low (e.g., < 5%). 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param std logical value indicating whether standardized or non-standardized infit or outfit estimates should be used (leads to different cut-off values). 8 | #' @param title title for the plot (defaults to "Person Infit and Outfit Statistics") 9 | #' 10 | #' @return a ggplot 11 | #' 12 | #' @references \itemize{ 13 | #' \item Linacre JM. (2002). What do Infit and Outfit, Mean-square and Standardized mean? Rasch Measurement Transactions, 16(2), p.878. https://www.rasch.org/rmt/rmt162f.htm 14 | #' } 15 | #' 16 | #' @import ggplot2 17 | #' @import dplyr 18 | #' @import tidyr 19 | #' @import mirt 20 | #' @export 21 | #' 22 | #' @examples 23 | #' library(mirt) 24 | #' library(ggmirt) 25 | #' data <- expand.table(LSAT7) 26 | #' (mod <- mirt(data, 1)) 27 | #' 28 | #' personfitPlot(mod, std = F) 29 | #' 30 | personfitPlot <- function(model, 31 | std = TRUE, 32 | title = "Person Infit and Outfit Statistics"){ 33 | 34 | 35 | if(isTRUE(std)) { 36 | 37 | fit <- mirt::personfit(model) %>% 38 | dplyr::select(z.infit, z.outfit) %>% 39 | gather(key, value) %>% 40 | mutate(color_diff = ifelse(value < -1.96, "red", 41 | ifelse(value > 1.96, "red", "grey"))) 42 | 43 | limits <- c(-1.96, 0, 1.96) 44 | } else { 45 | 46 | fit <- mirt::personfit(model) %>% 47 | dplyr::select(infit, outfit) %>% 48 | gather(key, value) %>% 49 | mutate(color_diff = ifelse(value < .5, "red", 50 | ifelse(value > 1.5, "red", "grey"))) 51 | limits <- c(.5, 1, 1.5) 52 | } 53 | 54 | fit %>% 55 | ggplot(aes(x = value, fill = color_diff)) + 56 | geom_histogram(color = "white") + 57 | geom_vline(xintercept = limits[3], color = "darkgrey", linetype = "dashed") + 58 | geom_vline(xintercept = limits[2], color = "darkgrey") + 59 | geom_vline(xintercept = limits[1], color = "darkgrey", linetype = "dashed") + 60 | facet_wrap(~key) + 61 | theme_minimal() + 62 | theme(legend.position = "none") + 63 | labs(x = "", 64 | y = "", 65 | title = title) 66 | } 67 | -------------------------------------------------------------------------------- /R/scaleCharPlot.r: -------------------------------------------------------------------------------- 1 | #' Scale Characteristic Curve 2 | #' 3 | #' Once model-based theta score estimates are computed, it often is of interest to transform those estimates into the original scale metric. A scale characteristic function provides a means of transforming estimated theta scores to expected true scores in the original scale metric. This transformation back into the original scale metric provides a more familiar frame of reference for interpreting scores. This function provides a visualization for this transformation. 4 | #' 5 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 6 | #' @param theta_range range to be shown on the x-axis 7 | #' @param color color of the line 8 | #' @param title title for the plot (defaults to "Person Infit and Outfit Statistics") 9 | #' 10 | #' @return a ggplot 11 | #' 12 | #' @import ggplot2 13 | #' @import dplyr 14 | #' @import tidyr 15 | #' @import mirt 16 | #' @export 17 | #' 18 | #' @examples 19 | #' library(mirt) 20 | #' library(ggmirt) 21 | #' data <- expand.table(LSAT7) 22 | #' (mod <- mirt(data, 1)) 23 | #' 24 | #' scaleCharPlot(mod) 25 | #' 26 | scaleCharPlot <- function(model, 27 | theta_range = c(-4, 4), 28 | color = "red", 29 | title = "Scale Characteristic Curve") { 30 | 31 | theta <- seq(theta_range[1], theta_range[2], by = .01) 32 | score <- expected.test(model, matrix(theta)) 33 | n.items <- model@Data$nitems 34 | 35 | d <- data.frame(theta, score) 36 | p <- ggplot(d, aes(x = theta, y = score)) + 37 | geom_line(color = color) + 38 | theme_minimal() + 39 | labs(x = expression(theta), y = expression(T(theta)), 40 | title = title) 41 | 42 | return(p) 43 | } 44 | 45 | 46 | -------------------------------------------------------------------------------- /R/sim_irt.r: -------------------------------------------------------------------------------- 1 | #' Helper function to simulate IRT data 2 | #' 3 | #' Function to simulate data that can be used to fit IRT models. 4 | #' 5 | #' @param n.obs Number of observations that should be included in the data set 6 | #' @param n.items Number of items that should be simulated 7 | #' @param discrimination Standard deviation on the log scale 8 | #' @param seed Seed for the random number generation process 9 | #' @param cut Either "random" for a randomized transformation of the model probability matrix into the model 0-1 matrix or an integer value between 0 and 1. 10 | #' 11 | #' @return a tibble 12 | #' @export 13 | #' 14 | #' @examples 15 | #' library(ggmirt) 16 | #' 17 | #' sim_irt(n.obs = 200, n.items = 10) 18 | #' 19 | sim_irt <- function(n.obs = 100, 20 | n.items = 10, 21 | discrimination = 0, 22 | seed = NULL, 23 | cut = "random") { 24 | 25 | # Get item difficulty distribution 26 | if (length(n.items) == 1) { 27 | if (!is.null(seed)) 28 | set.seed(seed) 29 | 30 | difficulty <- rnorm(n.items) 31 | no.items <- n.items 32 | 33 | } else { 34 | 35 | difficulty <- n.items 36 | no.items <- length(n.items) 37 | } 38 | 39 | # Get person ability distribution 40 | if (length(n.obs) == 1) { 41 | if (!is.null(seed)) 42 | set.seed(seed) 43 | 44 | ability <- rnorm(n.obs) 45 | no.obs <- n.obs 46 | 47 | } else { 48 | 49 | ability <- n.obs 50 | no.obs <- length(n.obs) 51 | } 52 | 53 | # Draw discrimination distribution if needed 54 | if (length(discrimination) > 1) { 55 | alpha <- discrimination 56 | 57 | } else { 58 | 59 | if (!is.null(seed)) 60 | set.seed(seed) 61 | 62 | alpha <- rlnorm(no.items, 0, sdlog = discrimination) 63 | } 64 | 65 | # Create empty matrix 66 | psolve <- matrix(0, no.obs, no.items) 67 | 68 | 69 | # Simulate response pattern 70 | for (i in 1:no.obs) for (j in 1:no.items) psolve[i, j] <- exp(alpha[j] * (ability[i] - difficulty[j]))/(1 + exp(alpha[j] * (ability[i] - difficulty[j]))) 71 | 72 | # Transform into binary items 73 | if (cut == "random") { 74 | if (!is.null(seed)) 75 | set.seed(seed) 76 | 77 | m <- (matrix(runif(no.items * no.obs), no.obs, no.items) < psolve) * 1 78 | 79 | } else { 80 | 81 | m <- (cut < psolve) * 1 82 | } 83 | 84 | d <- as_tibble(m) 85 | 86 | return(d) 87 | } 88 | 89 | 90 | -------------------------------------------------------------------------------- /R/summaryPlot.r: -------------------------------------------------------------------------------- 1 | #' A quick summary of IRT analyses 2 | #' 3 | #' This function is essentially just a wrapper around several functions in this package and produces a summary of the most important aspects of an IRT model, including an item-person-map,test information curve, scale characteristic curve, and conditional reliability. 4 | #' 5 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 6 | #' @param theta_range range to be shown on the x-axis 7 | #' @param adj_factor adjustment factor for properly overlaying information and standard error. 8 | #' 9 | #' @return a plot grid as returned by `cowplot::ggdraw()` 10 | #' @import ggplot2 11 | #' @import dplyr 12 | #' @import tidyr 13 | #' @import mirt 14 | #' @export 15 | #' 16 | #' @examples 17 | #' library(mirt) 18 | #' library(ggmirt) 19 | #' 20 | #' # Simulate some data 21 | #' data <- sim_irt(500, 10, seed = 123) 22 | #' 23 | #' # Run IRT model with mirt 24 | #' mod <- mirt(data, 1, itemtype = "2PL", verbose = FALSE) 25 | #' 26 | #' summaryPlot(mod, theta_range = c(-4.5, 3.5), adj_factor = 1.5) 27 | #' 28 | summaryPlot <- function(model, 29 | theta_range = c(-4, 4), 30 | adj_factor = .05) { 31 | 32 | # Get number of items 33 | J <- model@Data$nitems 34 | 35 | # Person parameter distribution 36 | p1 <- personDist(model, theta_range = theta_range) + 37 | labs(title = "Item Person Map") 38 | 39 | # Item difficulty distribution 40 | p2 <- itemDist(model, theta_range = theta_range, shape = 17, color = "red") 41 | 42 | # Change item labelling if no. items > 10 43 | if(J > 10) { 44 | p2 <- p2 + 45 | geom_text(aes(label = items), nudge_x = .75, color = "darkgrey", size = 2, check_overlap = T) + 46 | theme(axis.text.y = element_blank(), 47 | panel.grid.major.y = element_blank()) 48 | } 49 | 50 | # Scale Characteristic Curve 51 | p3 <- scaleCharPlot(model, theta_range = theta_range) 52 | 53 | # Test information curve 54 | p4 <- testInfoPlot(model, theta_range = theta_range, adj_factor = adj_factor) 55 | 56 | # Conditional reliability curve 57 | p5 <- conRelPlot(model, theta_range = theta_range) 58 | 59 | # Bind together 60 | p <- ggpubr::ggarrange(ggpubr::ggarrange(p1, p2, 61 | ncol = 1, 62 | align = "hv", 63 | heights = c(1,2)), 64 | ggpubr::ggarrange(p4, p3, p5, 65 | ncol = 1, 66 | align = "hv", 67 | heights = c(1.25, 1, 1)), 68 | ncol = 2) 69 | 70 | 71 | return(p) 72 | 73 | } 74 | 75 | -------------------------------------------------------------------------------- /R/testInfoCompare.r: -------------------------------------------------------------------------------- 1 | #' Comparing test information curves of parallel tests 2 | #' 3 | #' This function takes two fitted mirt-model and visualizes test their test information curves on top of each other. This can be helpful for finding parallel tests. 4 | #' 5 | #' 6 | #' @param model1 an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param model2 an object of class `SingleGroupClass` 8 | #' @param title title for the plot 9 | #' @param subtitle subtitle for the plot 10 | #' 11 | #' @return a ggplot 12 | #' @import ggplot2 13 | #' @import dplyr 14 | #' @import tidyr 15 | #' @import mirt 16 | #' @export 17 | #' 18 | #' @examples 19 | #' library(mirt) 20 | #' library(ggmirt) 21 | #' data <- expand.table(LSAT7) 22 | #' (mod1 <- mirt(data, 1)) 23 | #' (mod2 <- mirt(data[,1:4], 1)) 24 | #' 25 | #' testInfoCompare(mod1, mod2) 26 | #' 27 | testInfoCompare <- function(model1, model2, 28 | theta_range = c(-4,4), 29 | title = "Parallel Tests", 30 | subtitle = "Test Information Curves") { 31 | 32 | theta_range = seq(theta_range[1], theta_range[2], by = .01) 33 | 34 | Theta <- matrix(theta_range) 35 | information1 <- testinfo(model1, Theta) 36 | information2 <- testinfo(model2, Theta) 37 | 38 | p <- data.frame(Theta, information1, information2) %>% 39 | gather(key, value, -Theta) %>% 40 | ggplot() + 41 | geom_line(aes(x = Theta, y = value, color = key), alpha = .75) + 42 | labs(x = expression(theta), y = expression(I(theta)), 43 | title = title, subtitle = subtitle) + 44 | theme_minimal() + 45 | theme(legend.position = "bottom") + 46 | theme(legend.title=element_blank()) 47 | 48 | return(p) 49 | 50 | } 51 | -------------------------------------------------------------------------------- /R/testInfoPlot.r: -------------------------------------------------------------------------------- 1 | #' Plotting test information curve 2 | #' 3 | #' This function takes a fitted mirt-model and visualizes test information curve. 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param theta_range range to be shown on the x-axis 8 | #' @param adj_factor adjustment factor for properly overlaying information and standard error. 9 | #' @param title title for the plot (defaults to "Item Characteristic Curves") 10 | #' 11 | #' @return a ggplot 12 | #' @import ggplot2 13 | #' @import dplyr 14 | #' @import tidyr 15 | #' @import mirt 16 | #' @export 17 | #' 18 | #' @examples 19 | #' library(mirt) 20 | #' library(ggmirt) 21 | #' data <- expand.table(LSAT7) 22 | #' (mod <- mirt(data, 1)) 23 | #' 24 | #' testInfoPlot(mod) 25 | #' 26 | testInfoPlot <- function(model, 27 | theta_range = c(-4,4), 28 | adj_factor = 3.5, 29 | title = "Test Information Curve") { 30 | 31 | theta_range = seq(theta_range[1], theta_range[2], by = .01) 32 | 33 | Theta <- matrix(theta_range) 34 | information <- testinfo(model, Theta) 35 | SE <- 1/(sqrt(information)) 36 | 37 | p <- data.frame(Theta, information, SE) %>% 38 | mutate(SE = SE/adj_factor) %>% 39 | gather(key, value, -Theta) %>% 40 | ggplot() + 41 | geom_line(aes(x = Theta, y = value, color = key, linetype = key)) + 42 | scale_linetype_manual(values=c("solid", "dashed"))+ 43 | scale_y_continuous(sec.axis = sec_axis(~.*adj_factor, name = expression(SE(theta)))) + 44 | labs(x = expression(theta), y = expression(I(theta)), 45 | title = title) + 46 | theme_minimal() + 47 | theme(legend.position = "bottom") + 48 | theme(legend.title=element_blank()) + 49 | scale_color_manual(values = c("red", "darkred")) 50 | 51 | return(p) 52 | 53 | } 54 | -------------------------------------------------------------------------------- /R/tracePlot.r: -------------------------------------------------------------------------------- 1 | #' Plotting item characteristics curves 2 | #' 3 | #' This function takes a fitted mirt-model and the underlying data and visualizes item characteristic curves. 4 | #' 5 | #' 6 | #' @param model an object of class `SingleGroupClass` returned by the function `mirt()`. 7 | #' @param items numerical vector indicating which items to plot (currently does not yet work for graded response models). 8 | #' @param theta_range range to be shown on the x-axis 9 | #' @param n.answers In a graded response model, number of answer options (e.g., 5-point scale = 5) 10 | #' @param title title for the plot (defaults to "Item Characteristic Curves") 11 | #' @param facet Should all items be shown in one plot, or each item received its individual facet? 12 | #' 13 | #' @return a ggplot 14 | #' @import ggplot2 15 | #' @import dplyr 16 | #' @import tidyr 17 | #' @import mirt 18 | #' @export 19 | #' 20 | #' @examples 21 | #' library(mirt) 22 | #' library(ggmirt) 23 | #' data <- expand.table(LSAT7) 24 | #' (mod <- mirt(data, 1)) 25 | #' 26 | #' tracePlot(mod) 27 | #' tracePlot(mod, items = c(1,2,3), theta_range = c(-5,5), facet = F, legend = T) 28 | #' 29 | tracePlot <- function(model, 30 | items = NULL, 31 | theta_range = c(-4,4), 32 | title = "Item Characteristics Curves", 33 | n.answers = 5, 34 | facet = TRUE, 35 | legend = FALSE) { 36 | 37 | data <- model@Data$data %>% as.data.frame 38 | 39 | # Set theta range as sequence 40 | theta_range = seq(theta_range[1], theta_range[2], by = .01) 41 | 42 | # Check model type 43 | type <- model@Model$itemtype 44 | 45 | # Graded response model 46 | if(type[1] == "graded") { 47 | 48 | trace <- probtrace(model, Theta = theta_range) %>% 49 | as_tibble %>% 50 | mutate(Theta = theta_range) %>% 51 | gather(key, value, -Theta) %>% 52 | separate(key, c("var", "response"), sep = ifelse(n.answers > 10, -4, -3)) 53 | 54 | p <- ggplot(trace, aes(x = Theta, y = value)) + 55 | geom_line(aes(color = response)) + 56 | facet_wrap(~var) + 57 | theme_minimal() + 58 | labs(x = expression(theta), 59 | y = expression(P(theta)), 60 | title = title) + 61 | scale_color_brewer(palette = 7) 62 | 63 | } else { 64 | 65 | trace <- NULL 66 | for(i in 1:length(data)){ 67 | extr <- extract.item(model, i) 68 | theta <- matrix(theta_range) 69 | trace[[i]] <- probtrace(extr, theta) 70 | } 71 | 72 | if (!is.null(items)) { 73 | trace <- trace[items] 74 | } 75 | 76 | names(trace) <- paste('item', 1:length(trace)) 77 | trace_df <- do.call(rbind, trace) 78 | 79 | item <- rep(names(trace), each = length(theta)) 80 | d <- cbind.data.frame(theta, item, trace_df) 81 | d$item <- as.factor(d$item) 82 | 83 | # final plot 84 | if(isFALSE(facet)) { 85 | p <- ggplot(d, aes(theta, P.1, colour = item)) + 86 | geom_line() + 87 | labs(x = expression(theta), 88 | y = expression(P(theta)), 89 | title = title) + 90 | theme_minimal() + 91 | scale_color_brewer(palette = 7) 92 | 93 | if(isFALSE(legend)) { 94 | p <- p + guides(color = "none") 95 | } 96 | 97 | } else { 98 | p <- ggplot(d, aes(theta, P.1)) + 99 | geom_line() + 100 | facet_wrap(~item) + 101 | labs(x = expression(theta), 102 | y = expression(P(theta)), 103 | title = title) + 104 | theme_minimal() 105 | } 106 | 107 | } 108 | return(p) 109 | } 110 | -------------------------------------------------------------------------------- /R/utils-pipe.r: -------------------------------------------------------------------------------- 1 | #' Pipe operator 2 | #' 3 | #' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. 4 | #' 5 | #' @name %>% 6 | #' @rdname pipe 7 | #' @keywords internal 8 | #' @export 9 | #' @importFrom magrittr %>% 10 | #' @usage lhs \%>\% rhs 11 | NULL 12 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 |
5 | 6 | 7 | 8 |
9 | 10 | # ggmirt 11 | 12 | 13 | 14 | [![Lifecycle: 15 | experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) 16 | [![CRAN 17 | status](https://www.r-pkg.org/badges/version/ggmirt)](https://CRAN.R-project.org/package=ggmirt) 18 | 19 | 20 | This package extends the great R-package 21 | [`mirt`](https://github.com/philchalmers/mirt) (Multidimensional item 22 | response theory; Chalmers, 2021) with functions for creating 23 | publication-ready and customizable figures. Although the `mirt`-packages 24 | already includes possibilities to plot various aspects relevant to 25 | understanding IRT analyses (e.g., item plots, trace-plots, etc.), it 26 | does not employ `ggplot2`, which provides more flexibility and 27 | customizability. This package provides some functions to recreate such 28 | plots with ggplot2. 29 | 30 | If you want to learn how to use `mirt` in combination with `ggmirt` to 31 | run various IRT analyses, please check out the following tutorials: 32 | 33 | - [Item Response Theory I: 3PL, 2PL, & 1PL (Rasch) 34 | models](https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/R_test-theory_3_irt.md) 35 | - [Item Response Theory II: Graded response 36 | models](https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/R_test-theory_3_irt_graded.md) 37 | 38 | **Please note:** This package is still under development. It is 39 | currently rather a place where I dump some functions that I use often, 40 | but I have not fully tested them under different scenarios and with 41 | different type of models. If you are interested in contributing, feel 42 | free to reach out. 43 | 44 | ### Installation 45 | 46 | ``` r 47 | # install.packages("devtools") 48 | devtools::install_github("masurp/ggmirt") 49 | ``` 50 | 51 | ### Usage 52 | 53 | ``` r 54 | # Load packages 55 | library(mirt) 56 | library(ggmirt) 57 | 58 | # Simulate some data 59 | data <- sim_irt(500, 8, seed = 123) 60 | 61 | # Run IRT model with mirt 62 | mod <- mirt(data, 1, itemtype = "2PL", verbose = FALSE) 63 | 64 | # Plot item-person map 65 | itempersonMap(mod) 66 | ``` 67 | 68 | 69 | 70 | ``` r 71 | # Item characteristic curves 72 | tracePlot(mod, data) 73 | ``` 74 | 75 | 76 | 77 | ``` r 78 | # Item information curves 79 | itemInfoPlot(mod, data) 80 | ``` 81 | 82 | 83 | 84 | ``` r 85 | # Scale characteristic curve 86 | scaleCharPlot(mod) 87 | ``` 88 | 89 | 90 | 91 | ``` r 92 | # Test information curves 93 | testInfoPlot(mod, adj_factor = 1.75) 94 | ``` 95 | 96 | 97 | 98 | ``` r 99 | # Item infit and outfit statistics 100 | itemfitPlot(mod) 101 | ``` 102 | 103 | 104 | 105 | ``` r 106 | # Person fit statisitcs 107 | personfitPlot(mod) 108 | ``` 109 | 110 | 111 | 112 | ``` r 113 | # Conditional reliability 114 | conRelPlot(mod) 115 | ``` 116 | 117 | 118 | 119 | Next to individual plot functions, there is also a comprehensive 120 | summaryPlot()-function, which provides a lot of information about IRT 121 | models with just a line of code. 122 | 123 | ``` r 124 | summaryPlot(mod, adj_factor = 1.75) 125 | ``` 126 | 127 | 128 | 129 | ### How to cite this package 130 | 131 | ``` r 132 | citation("ggmirt") 133 | #> 134 | #> To cite package 'ggmirt' in publications use: 135 | #> 136 | #> Philipp K. Masur (2022). ggmirt: Plotting functions to extend "mirt" 137 | #> for IRT analyses. R package version 0.1.0. 138 | #> 139 | #> A BibTeX entry for LaTeX users is 140 | #> 141 | #> @Manual{, 142 | #> title = {ggmirt: Plotting functions to extend "mirt" for IRT analyses}, 143 | #> author = {Philipp K. Masur}, 144 | #> year = {2022}, 145 | #> note = {R package version 0.1.0}, 146 | #> } 147 | ``` 148 | -------------------------------------------------------------------------------- /README.rmd: -------------------------------------------------------------------------------- 1 | --- 2 | output: github_document 3 | --- 4 | 5 | 6 | 7 | ```{r, include = FALSE} 8 | knitr::opts_chunk$set( 9 | collapse = TRUE, 10 | comment = "#>", 11 | fig.path = "man/figures/README-", 12 | out.width = "100%", 13 | fig.retina = 2 14 | ) 15 | ``` 16 | 17 | 18 |
19 | 20 |
21 | 22 | 23 | 24 | # ggmirt 25 | 26 | 27 | [![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) 28 | [![CRAN status](https://www.r-pkg.org/badges/version/ggmirt)](https://CRAN.R-project.org/package=ggmirt) 29 | 30 | 31 | This package extends the great R-package [`mirt`](https://github.com/philchalmers/mirt) (Multidimensional item response theory; Chalmers, 2021) with functions for creating publication-ready and customizable figures. Although the `mirt`-packages already includes possibilities to plot various aspects relevant to understanding IRT analyses (e.g., item plots, trace-plots, etc.), it does not employ `ggplot2`, which provides more flexibility and customizability. This package provides some functions to recreate such plots with ggplot2. 32 | 33 | If you want to learn how to use `mirt` in combination with `ggmirt` to run various IRT analyses, please check out the following tutorials: 34 | 35 | - [Item Response Theory I: 3PL, 2PL, & 1PL (Rasch) models](https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/R_test-theory_3_irt.md) 36 | - [Item Response Theory II: Graded response models](https://github.com/ccs-amsterdam/r-course-material/blob/master/tutorials/R_test-theory_3_irt_graded.md) 37 | 38 | 39 | **Please note:** This package is still under development. It is currently rather a place where I dump some functions that I use often, but I have not fully tested them under different scenarios and with different type of models. If you are interested in contributing, feel free to reach out. 40 | 41 | 42 | ### Installation 43 | 44 | ``` r 45 | # install.packages("devtools") 46 | devtools::install_github("masurp/ggmirt") 47 | ``` 48 | 49 | 50 | ### Usage 51 | 52 | ```{r, message=F, warning = F, fig.width=7, fig.height=8.5} 53 | # Load packages 54 | library(mirt) 55 | library(ggmirt) 56 | 57 | # Simulate some data 58 | data <- sim_irt(500, 8, seed = 123) 59 | 60 | # Run IRT model with mirt 61 | mod <- mirt(data, 1, itemtype = "2PL", verbose = FALSE) 62 | 63 | # Plot item-person map 64 | itempersonMap(mod) 65 | ``` 66 | 67 | 68 | ```{r, message=F, warning = F} 69 | # Item characteristic curves 70 | tracePlot(mod) 71 | ``` 72 | 73 | 74 | ```{r, message=F, warning = F} 75 | # Item information curves 76 | itemInfoPlot(mod) 77 | ``` 78 | 79 | 80 | ```{r, message=F, warning = F} 81 | # Scale characteristic curve 82 | scaleCharPlot(mod) 83 | ``` 84 | 85 | 86 | ```{r, message=F, warning = F} 87 | # Test information curves 88 | testInfoPlot(mod, adj_factor = 1.75) 89 | ``` 90 | 91 | 92 | ```{r, message=F, warning = F} 93 | # Item infit and outfit statistics 94 | itemfitPlot(mod) 95 | ``` 96 | 97 | 98 | ```{r, message=F, warning = F} 99 | # Person fit statisitcs 100 | personfitPlot(mod) 101 | ``` 102 | 103 | 104 | ```{r, message=F, warning = F} 105 | # Conditional reliability 106 | conRelPlot(mod) 107 | ``` 108 | 109 | 110 | Next to individual plot functions, there is also a comprehensive summaryPlot()-function, which provides a lot of information about IRT models with just a line of code. 111 | 112 | ```{r, message=F, warning = F, fig.width=8, fig.height=8} 113 | summaryPlot(mod, adj_factor = 1.75) 114 | ``` 115 | 116 | 117 | 118 | 119 | ### How to cite this package 120 | 121 | ```{r, message=F, warning = F} 122 | citation("ggmirt") 123 | ``` 124 | 125 | 126 | -------------------------------------------------------------------------------- /ggmirt.Rproj: -------------------------------------------------------------------------------- 1 | Version: 1.0 2 | 3 | RestoreWorkspace: Default 4 | SaveWorkspace: Default 5 | AlwaysSaveHistory: Default 6 | 7 | EnableCodeIndexing: Yes 8 | UseSpacesForTab: Yes 9 | NumSpacesForTab: 2 10 | Encoding: UTF-8 11 | 12 | RnwWeave: Sweave 13 | LaTeX: pdfLaTeX 14 | 15 | BuildType: Package 16 | PackageUseDevtools: Yes 17 | PackageInstallArgs: --no-multiarch --with-keep.source 18 | PackageRoxygenize: rd,collate,namespace 19 | -------------------------------------------------------------------------------- /inst/figures/logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/inst/figures/logo.png -------------------------------------------------------------------------------- /man/conRelPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/conRelPlot.r 3 | \name{conRelPlot} 4 | \alias{conRelPlot} 5 | \title{Plotting conditional reliability} 6 | \usage{ 7 | conRelPlot( 8 | model, 9 | theta_range = c(-4, 4), 10 | color = "red", 11 | title = "Conditional Reliability" 12 | ) 13 | } 14 | \arguments{ 15 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 16 | 17 | \item{theta_range}{range to be shown on the x-axis} 18 | 19 | \item{color}{color of the line} 20 | 21 | \item{title}{title for the plot (defaults to "Conditional Reliability")} 22 | } 23 | \value{ 24 | a ggplot 25 | } 26 | \description{ 27 | This function takes a fitted mirt-model and visualizes a conditional reliability curve. Heavily inspired by code from Phil Chalmers (author or 'mirt') 28 | } 29 | \examples{ 30 | library(mirt) 31 | library(ggmirt) 32 | data <- expand.table(LSAT7) 33 | (mod <- mirt(data, 1)) 34 | 35 | conRelPlot(mod) 36 | } 37 | -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-10-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-10-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-2-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-2-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-2-2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-2-2.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-3-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-3-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-4-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-4-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-5-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-5-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-6-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-6-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-7-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-7-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-8-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-8-1.png -------------------------------------------------------------------------------- /man/figures/README-unnamed-chunk-9-1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/masurp/ggmirt/4cfa1069f12409e0d8420ec67468a7f32351d0b3/man/figures/README-unnamed-chunk-9-1.png -------------------------------------------------------------------------------- /man/itemDist.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/itemDistribution.r 3 | \name{itemDist} 4 | \alias{itemDist} 5 | \title{Item difficulty distribution} 6 | \usage{ 7 | itemDist(model, theta_range = c(-4, 4), ...) 8 | } 9 | \arguments{ 10 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 11 | 12 | \item{...}{any argument passed to \code{geom_point()}} 13 | } 14 | \value{ 15 | a ggplot object. 16 | } 17 | \description{ 18 | This function requires a fitted mirt-model of class \code{SingleGroupClass} to visualize item difficulty distribution. Currently only works for unidimensional models. 19 | } 20 | \examples{ 21 | # Loading packages 22 | library(mirt) 23 | library(ggmirt) 24 | 25 | # Getting data 26 | data <- expand.table(LSAT7) 27 | 28 | # Fitting a model 29 | (mod <- mirt(data, 1)) 30 | 31 | # Simple plot 32 | itemDist(mod) 33 | 34 | # Customized plot 35 | itemDist(mod, size = 3, shape = 17, color = "blue") 36 | 37 | } 38 | -------------------------------------------------------------------------------- /man/itemInfoPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/itemInfoPlot.r 3 | \name{itemInfoPlot} 4 | \alias{itemInfoPlot} 5 | \title{Plotting item information curves} 6 | \usage{ 7 | itemInfoPlot( 8 | model, 9 | items = NULL, 10 | facet = FALSE, 11 | title = "Item Information Curves", 12 | theta_range = c(-4, 4), 13 | legend = FALSE 14 | ) 15 | } 16 | \arguments{ 17 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 18 | 19 | \item{items}{numerical vector indicating which items to plot.} 20 | 21 | \item{facet}{Should all items be shown in one plot, or each item received its individal facet?} 22 | 23 | \item{title}{title for the plot (defaults to "Item Characteristic Curves")} 24 | 25 | \item{theta_range}{range to be shown on the x-axis} 26 | } 27 | \value{ 28 | a ggplot 29 | } 30 | \description{ 31 | This function takes a fitted mirt-model and visualizes items information curves. 32 | } 33 | \examples{ 34 | library(mirt) 35 | library(ggmirt) 36 | data <- expand.table(LSAT7) 37 | (mod <- mirt(data, 1)) 38 | 39 | itemInfoPlot(mod) 40 | 41 | } 42 | -------------------------------------------------------------------------------- /man/itemfitPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/itemfitPlot.r 3 | \name{itemfitPlot} 4 | \alias{itemfitPlot} 5 | \title{Plotting itemfit estimates} 6 | \usage{ 7 | itemfitPlot( 8 | model, 9 | fit_stats = "infit", 10 | color = "red", 11 | shape = 17, 12 | title = "Item Infit and Outfit Statistics", 13 | ... 14 | ) 15 | } 16 | \arguments{ 17 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 18 | 19 | \item{fit_stats}{a character vector indicating which fit statistics should be computed. See \code{mirt::infit()} for supported inputs.} 20 | 21 | \item{color}{color of the item points.} 22 | 23 | \item{shape}{shape of the item points} 24 | 25 | \item{title}{title for the plot (defaults to "Item Infit and Outfit Statistics")} 26 | } 27 | \value{ 28 | a ggplot 29 | } 30 | \description{ 31 | This function takes a fitted mirt-model and visualizes item infit and outfit estimates. The function builds on \code{mirt::itemfit()}. Currently only supported \code{fact_stats = "infit"}. 32 | } 33 | \examples{ 34 | library(mirt) 35 | library(ggmirt) 36 | data <- expand.table(LSAT7) 37 | (mod <- mirt(data, 1)) 38 | 39 | itemfitPlot(mod, fit_stats = "infit") 40 | 41 | } 42 | \references{ 43 | \itemize{ 44 | \item Linacre JM. (2002). What do Infit and Outfit, Mean-square and Standardized mean? Rasch Measurement Transactions, 16(2), p.878. https://www.rasch.org/rmt/rmt162f.htm 45 | } 46 | } 47 | -------------------------------------------------------------------------------- /man/itempersonMap.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/itempersonMap.r 3 | \name{itempersonMap} 4 | \alias{itempersonMap} 5 | \title{Visualize item person map and scale properties based on Rasch model} 6 | \usage{ 7 | itempersonMap( 8 | model, 9 | theta_range = c(-4, 4), 10 | title = "Item Person Map", 11 | margin = c(1, 0, -1.5, 0), 12 | density = FALSE, 13 | color = "red", 14 | shape = 17, 15 | size = 3, 16 | theme = theme_minimal(), 17 | ... 18 | ) 19 | } 20 | \arguments{ 21 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 22 | 23 | \item{title}{title for the plot (defaults to "Item-Person-Map")} 24 | 25 | \item{margin}{margins around the top figure. Sometimes one might want to adjust this.} 26 | 27 | \item{density}{logical value indicating whether a smoothed density curve or a standard histogram should be plotted.} 28 | 29 | \item{color}{color of the geoms, defaults to "red".} 30 | 31 | \item{shape}{can be used to change the shape of the geom, defaults to triangles (17).} 32 | 33 | \item{size}{size of the geom, default to 3.} 34 | 35 | \item{theme}{any ggplot theme.} 36 | 37 | \item{...}{any argument passed to \code{geom_point()}.} 38 | 39 | \item{limits}{range to be shown on the x-axis} 40 | } 41 | \value{ 42 | a plot grid as returned by \code{cowplot::plot_grid()} 43 | } 44 | \description{ 45 | This function takes a fitted mirt-model and visualizes and plots item-person-map (also known as Kernel-Density Plots or Wright maps) on the left, and add a scale characteristic curve, scale information curve, and a marginal reliability curve on the right. 46 | } 47 | \examples{ 48 | library(mirt) 49 | library(ggmirt) 50 | data <- expand.table(LSAT7) 51 | (mod <- mirt(data, 1)) 52 | 53 | itempersonMap(mod) 54 | 55 | } 56 | -------------------------------------------------------------------------------- /man/personDist.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/personDistribution.r 3 | \name{personDist} 4 | \alias{personDist} 5 | \title{Person parameter distribution} 6 | \usage{ 7 | personDist(model, theta_range = c(-4, 4), density = FALSE, bins = 35) 8 | } 9 | \arguments{ 10 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 11 | 12 | \item{density}{logical value indicating whether a smoothed density curve or a standard histogram should be plotted.} 13 | 14 | \item{bins}{number of bins to be plotted in the histogram} 15 | } 16 | \value{ 17 | a ggplot object. 18 | } 19 | \description{ 20 | This function requires a fitted mirt-model of class \code{SingleGroupClass} to visualize a person parameter distribution (theta levels in the studied population). The resulting ggplot can be further customized (e.g., with regard to theme, labels, etc.). It works with both uni- and multidimensional models. 21 | } 22 | \examples{ 23 | # Loading packages 24 | library(mirt) 25 | library(ggmirt) 26 | 27 | # Getting data 28 | data <- expand.table(LSAT7) 29 | 30 | # Fitting a model 31 | (mod <- mirt(data, 1)) 32 | 33 | # Simple plot 34 | personDist(mod) 35 | personDist(mod, density = TRUE) 36 | 37 | # Customized plot 38 | personDist(mod, theta_range = c(-3, 3), bins = 10) + 39 | theme_classic() 40 | } 41 | -------------------------------------------------------------------------------- /man/personfitPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/personfitPlot.r 3 | \name{personfitPlot} 4 | \alias{personfitPlot} 5 | \title{Plotting personfit estimates} 6 | \usage{ 7 | personfitPlot(model, std = TRUE, title = "Person Infit and Outfit Statistics") 8 | } 9 | \arguments{ 10 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 11 | 12 | \item{std}{logical value indicating whether standardized or non-standardized infit or outfit estimates should be used (leads to different cut-off values).} 13 | 14 | \item{title}{title for the plot (defaults to "Person Infit and Outfit Statistics")} 15 | } 16 | \value{ 17 | a ggplot 18 | } 19 | \description{ 20 | This function takes a fitted mirt-model and visualizes person infit and outfit estimates. The function builds on \code{mirt::itemfit()}. The basic idea is to visualize how many individuals in the sample do not show a response pattern that aligns with the suggested model. At best, the number of non-fitting response patterns is low (e.g., < 5\%). 21 | } 22 | \examples{ 23 | library(mirt) 24 | library(ggmirt) 25 | data <- expand.table(LSAT7) 26 | (mod <- mirt(data, 1)) 27 | 28 | personfitPlot(mod, std = F) 29 | 30 | } 31 | \references{ 32 | \itemize{ 33 | \item Linacre JM. (2002). What do Infit and Outfit, Mean-square and Standardized mean? Rasch Measurement Transactions, 16(2), p.878. https://www.rasch.org/rmt/rmt162f.htm 34 | } 35 | } 36 | -------------------------------------------------------------------------------- /man/pipe.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/utils-pipe.r 3 | \name{\%>\%} 4 | \alias{\%>\%} 5 | \title{Pipe operator} 6 | \usage{ 7 | lhs \%>\% rhs 8 | } 9 | \description{ 10 | See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details. 11 | } 12 | \keyword{internal} 13 | -------------------------------------------------------------------------------- /man/scaleCharPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/scaleCharPlot.r 3 | \name{scaleCharPlot} 4 | \alias{scaleCharPlot} 5 | \title{Scale Characteristic Curve} 6 | \usage{ 7 | scaleCharPlot( 8 | model, 9 | theta_range = c(-4, 4), 10 | color = "red", 11 | title = "Scale Characteristic Curve" 12 | ) 13 | } 14 | \arguments{ 15 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 16 | 17 | \item{theta_range}{range to be shown on the x-axis} 18 | 19 | \item{color}{color of the line} 20 | 21 | \item{title}{title for the plot (defaults to "Person Infit and Outfit Statistics")} 22 | } 23 | \value{ 24 | a ggplot 25 | } 26 | \description{ 27 | Once model-based theta score estimates are computed, it often is of interest to transform those estimates into the original scale metric. A scale characteristic function provides a means of transforming estimated theta scores to expected true scores in the original scale metric. This transformation back into the original scale metric provides a more familiar frame of reference for interpreting scores. This function provides a visualization for this transformation. 28 | } 29 | \examples{ 30 | library(mirt) 31 | library(ggmirt) 32 | data <- expand.table(LSAT7) 33 | (mod <- mirt(data, 1)) 34 | 35 | scaleCharPlot(mod) 36 | 37 | } 38 | -------------------------------------------------------------------------------- /man/sim_irt.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/sim_irt.r 3 | \name{sim_irt} 4 | \alias{sim_irt} 5 | \title{Helper function to simulate IRT data} 6 | \usage{ 7 | sim_irt( 8 | n.obs = 100, 9 | n.items = 10, 10 | discrimination = 0, 11 | seed = NULL, 12 | cut = "random" 13 | ) 14 | } 15 | \arguments{ 16 | \item{n.obs}{Number of observations that should be included in the data set} 17 | 18 | \item{n.items}{Number of items that should be simulated} 19 | 20 | \item{discrimination}{Standard deviation on the log scale} 21 | 22 | \item{seed}{Seed for the random number generation process} 23 | 24 | \item{cut}{Either "random" for a randomized transformation of the model probability matrix into the model 0-1 matrix or an integer value between 0 and 1.} 25 | } 26 | \value{ 27 | a tibble 28 | } 29 | \description{ 30 | Function to simulate data that can be used to fit IRT models. 31 | } 32 | \examples{ 33 | library(ggmirt) 34 | 35 | sim_irt(n.obs = 200, n.items = 10) 36 | 37 | } 38 | -------------------------------------------------------------------------------- /man/summaryPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/summaryPlot.r 3 | \name{summaryPlot} 4 | \alias{summaryPlot} 5 | \title{A quick summary of IRT analyses} 6 | \usage{ 7 | summaryPlot(model, theta_range = c(-4, 4), adj_factor = 0.05) 8 | } 9 | \arguments{ 10 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 11 | 12 | \item{theta_range}{range to be shown on the x-axis} 13 | 14 | \item{adj_factor}{adjustment factor for properly overlaying information and standard error.} 15 | } 16 | \value{ 17 | a plot grid as returned by \code{cowplot::ggdraw()} 18 | } 19 | \description{ 20 | This function is essentially just a wrapper around several functions in this package and produces a summary of the most important aspects of an IRT model, including an item-person-map,test information curve, scale characteristic curve, and conditional reliability. 21 | } 22 | \examples{ 23 | library(mirt) 24 | library(ggmirt) 25 | 26 | # Simulate some data 27 | data <- sim_irt(500, 10, seed = 123) 28 | 29 | # Run IRT model with mirt 30 | mod <- mirt(data, 1, itemtype = "2PL", verbose = FALSE) 31 | 32 | summaryPlot(mod, theta_range = c(-4.5, 3.5), adj_factor = 1.5) 33 | 34 | } 35 | -------------------------------------------------------------------------------- /man/testInfoCompare.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/testInfoCompare.r 3 | \name{testInfoCompare} 4 | \alias{testInfoCompare} 5 | \title{Comparing test information curves of parallel tests} 6 | \usage{ 7 | testInfoCompare( 8 | model1, 9 | model2, 10 | theta_range = c(-4, 4), 11 | title = "Parallel Tests", 12 | subtitle = "Test Information Curves" 13 | ) 14 | } 15 | \arguments{ 16 | \item{model1}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 17 | 18 | \item{model2}{an object of class \code{SingleGroupClass}} 19 | 20 | \item{title}{title for the plot} 21 | 22 | \item{subtitle}{subtitle for the plot} 23 | } 24 | \value{ 25 | a ggplot 26 | } 27 | \description{ 28 | This function takes two fitted mirt-model and visualizes test their test information curves on top of each other. This can be helpful for finding parallel tests. 29 | } 30 | \examples{ 31 | library(mirt) 32 | library(ggmirt) 33 | data <- expand.table(LSAT7) 34 | (mod1 <- mirt(data, 1)) 35 | (mod2 <- mirt(data[,1:4], 1)) 36 | 37 | testInfoCompare(mod1, mod2) 38 | 39 | } 40 | -------------------------------------------------------------------------------- /man/testInfoPlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/testInfoPlot.r 3 | \name{testInfoPlot} 4 | \alias{testInfoPlot} 5 | \title{Plotting test information curve} 6 | \usage{ 7 | testInfoPlot( 8 | model, 9 | theta_range = c(-4, 4), 10 | adj_factor = 3.5, 11 | title = "Test Information Curve" 12 | ) 13 | } 14 | \arguments{ 15 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 16 | 17 | \item{theta_range}{range to be shown on the x-axis} 18 | 19 | \item{adj_factor}{adjustment factor for properly overlaying information and standard error.} 20 | 21 | \item{title}{title for the plot (defaults to "Item Characteristic Curves")} 22 | } 23 | \value{ 24 | a ggplot 25 | } 26 | \description{ 27 | This function takes a fitted mirt-model and visualizes test information curve. 28 | } 29 | \examples{ 30 | library(mirt) 31 | library(ggmirt) 32 | data <- expand.table(LSAT7) 33 | (mod <- mirt(data, 1)) 34 | 35 | testInfoPlot(mod) 36 | 37 | } 38 | -------------------------------------------------------------------------------- /man/tracePlot.Rd: -------------------------------------------------------------------------------- 1 | % Generated by roxygen2: do not edit by hand 2 | % Please edit documentation in R/tracePlot.r 3 | \name{tracePlot} 4 | \alias{tracePlot} 5 | \title{Plotting item characteristics curves} 6 | \usage{ 7 | tracePlot( 8 | model, 9 | items = NULL, 10 | theta_range = c(-4, 4), 11 | title = "Item Characteristics Curves", 12 | n.answers = 5, 13 | facet = TRUE, 14 | legend = FALSE 15 | ) 16 | } 17 | \arguments{ 18 | \item{model}{an object of class \code{SingleGroupClass} returned by the function \code{mirt()}.} 19 | 20 | \item{items}{numerical vector indicating which items to plot (currently does not yet work for graded response models).} 21 | 22 | \item{theta_range}{range to be shown on the x-axis} 23 | 24 | \item{title}{title for the plot (defaults to "Item Characteristic Curves")} 25 | 26 | \item{n.answers}{In a graded response model, number of answer options (e.g., 5-point scale = 5)} 27 | 28 | \item{facet}{Should all items be shown in one plot, or each item received its individual facet?} 29 | } 30 | \value{ 31 | a ggplot 32 | } 33 | \description{ 34 | This function takes a fitted mirt-model and the underlying data and visualizes item characteristic curves. 35 | } 36 | \examples{ 37 | library(mirt) 38 | library(ggmirt) 39 | data <- expand.table(LSAT7) 40 | (mod <- mirt(data, 1)) 41 | 42 | tracePlot(mod) 43 | tracePlot(mod, items = c(1,2,3), theta_range = c(-5,5), facet = F, legend = T) 44 | 45 | } 46 | --------------------------------------------------------------------------------