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|---|---|---|
| @@ -1,11 +1,12 @@ | ||
| ^.*\.Rproj$ | ||
| ^\.Rproj\.user$ | ||
| ^NEWS\.md$ | ||
| ^README\.md$ | ||
| ^dev\.R$ | ||
| ^\.travis\.yml$ | ||
| ^cran-comments\.md$ | ||
| ^desktop\.ini$ | ||
| gendat3* | ||
| .vscode | ||
| doc | ||
| ^.*\.Rproj$ | ||
| ^\.Rproj\.user$ | ||
| ^NEWS\.md$ | ||
| ^README\.md$ | ||
| ^dev\.R$ | ||
| ^\.travis\.yml$ | ||
| ^cran-comments\.md$ | ||
| ^desktop\.ini$ | ||
| gendat3* | ||
| .vscode | ||
| ^Meta$ | ||
| ^doc$ |
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|---|---|---|
| @@ -1,6 +1,7 @@ | ||
| Package: lsasim | ||
| Title: Functions to Facilitate the Simulation of Large Scale Assessment Data | ||
| Version: 2.0.2 | ||
| Version: 2.1.0 | ||
| Date: 2021-06-22 | ||
| Authors@R: c( | ||
| person("Tyler", "Matta", | ||
| email = "[email protected]", role = "aut"), | ||
|
|
@@ -13,18 +14,27 @@ Authors@R: c( | |
| person("Kondwani Kajera", "Mughogho", | ||
| email = "[email protected]", role = "ctb"), | ||
| person("Waldir", "Leoncio", | ||
| email = "[email protected]", role = c("aut", "cre")) | ||
| email = "[email protected]", role = c("aut", "cre")), | ||
| person("Sinan", "Yavuz", role = "ctb"), | ||
| person("Paul", "Bailey", role = "ctb") | ||
| ) | ||
| Maintainer: Waldir Leoncio <[email protected]> | ||
| BugReports: https://github.com/tmatta/lsasim/issues | ||
| Description: Provides functions to simulate data from large-scale educational | ||
| assessments, including background questionnaire data and cognitive item | ||
| responses that adhere to a multiple-matrix sampled design. | ||
| Imports: mvtnorm | ||
| responses that adhere to a multiple-matrix sampled design. The theoretical | ||
| foundation can be found on | ||
| Matta, T.H., Rutkowski, L., Rutkowski, D. et al. (2018) | ||
| <doi:10.1186/s40536-018-0068-8>. | ||
| Imports: mvtnorm, cli, methods, polycor | ||
| Depends: | ||
| R (>= 3.3.0) | ||
| R (>= 3.6.0) | ||
| License: GPL-3 | ||
| Encoding: UTF-8 | ||
| LazyData: true | ||
| RoxygenNote: 7.1.1 | ||
| Suggests: testthat, polycor | ||
| Suggests: | ||
| testthat, | ||
| knitr, | ||
| formatR, | ||
| rmarkdown | ||
| VignetteBuilder: knitr | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,88 @@ | ||
| #' @title Generate an ANOVA table for LSASIM clusters | ||
| #' @description Prints Analysis of Variance table for `cluster_gen` output. | ||
| #' @param object list output of `cluster_gen` | ||
| #' @param print if `TRUE`, output will be a list containing estimators; if `FALSE` (default), output are formatted tables of this information | ||
| #' @param calc.se if `TRUE`, will try to calculate the standard error of the intreaclass correlation | ||
| #' @param ... additional objects of the same type (see `help("anova")` for details) | ||
| #' @return Printed ANOVA table or list of parameters | ||
| #' @note If the rhos for different levels are varied in scale, the generated rho will be less accurate. | ||
| #' @references Snijders, T. A. B., & Bosker, R. J. (1999). Multilevel Analysis. Sage Publications. | ||
| #' @importFrom stats anova | ||
| #' @method anova lsasimcluster | ||
| #' @export | ||
| anova.lsasimcluster <- function(object, print = TRUE, calc.se = TRUE, ...) { | ||
| # Wrap data in a list (for !separate_questionnaires) ======================= | ||
| if (all(sapply(object, class) != "list")) { | ||
| data <- list(object) | ||
| names(data) <- gsub("[0-9]", "", names(data[[1]])[1]) | ||
| } else { | ||
| data <- object | ||
| } | ||
|
|
||
| # Create summary statistics ================================================ | ||
| data_summary <- summary.lsasimcluster(data, print = "none") | ||
|
|
||
| # Create other relevant input ============================================== | ||
| out_complete <- list() | ||
| for (n in names(data)) { | ||
| ds <- data_summary[[n]] | ||
| if (ds$N == 1) { | ||
| message("There is only one group of ", pluralize(n), | ||
| ". Skipping ANOVA for that level.") | ||
| } else { | ||
| ## Sample statistics ----------------------------------------------- | ||
| s2_within <- calc_var_within(ds$n_j, ds$s2_j, ds$M, ds$N) | ||
| s2_between <- calc_var_between(ds$n_j, ds$y_bar_j, ds$y_bar, ds$n_tilde, ds$N) | ||
| s2_total <- calc_var_tot(ds$M, ds$N, ds$n_tilde, s2_within, s2_between) | ||
| out <- list(sample_statistics = c(s2_within = s2_within, | ||
| s2_between = s2_between, | ||
| s2_total = s2_total)) | ||
|
|
||
| ## Population parameters and ANOVA table --------------------------- | ||
| sigma2_hat <- s2_within | ||
| X <- names(sigma2_hat) | ||
| for (x in X) { | ||
| tau2_hat <- max(s2_between[x] - s2_within[x] / ds$n_tilde, 0) | ||
| rho_hat <- intraclass_cor(tau2_hat, sigma2_hat[x]) | ||
| if (stats::var(ds$n_j) == 0) { | ||
| if (calc.se) { | ||
| se_rho <- calc_se_rho(rho_hat, ds$n_j, ds$N) | ||
| } else { | ||
| se_rho <- NA | ||
| } | ||
| } else { | ||
| se_rho <- NA | ||
| if (calc.se) { | ||
| warning( | ||
| "SE not yet implemented for different ", | ||
| "sample sizes. You can set calc.se=FALSE ", | ||
| "to get rid of this message." | ||
| ) | ||
| } | ||
| } | ||
| out$population_estimates[[x]] <- c(sigma2_hat = sigma2_hat[x], | ||
| tau2_hat = tau2_hat, | ||
| rho_hat = rho_hat, | ||
| se_rho = se_rho) | ||
|
|
||
| ### ANOVA table ................................................ | ||
| if (print) { | ||
| message("\nANOVA table for ", pluralize(n), ", ", x) | ||
| print_anova(s2_within[x], s2_between[x], s2_total[x], | ||
| sigma2_hat[x], tau2_hat, rho_hat, se_rho, | ||
| ds$n_tilde, ds$M, ds$N) | ||
| } | ||
| } | ||
| if (print & sapply(data, class)[1] == "list" & | ||
| (n != names(data)[length(names(data))])) cli::cat_rule() | ||
| out_complete[[n]] <- out | ||
| } | ||
| } | ||
| if (!print) { | ||
| if (length(out_complete) > 1) { | ||
| return(out_complete) | ||
| } else { | ||
| return(out) | ||
| } | ||
| } | ||
| } |
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| #' @title Attribute Labels in Hierarchical Structure | ||
| #' @description Attributes cluster and respondent labels in the context of `cluster_gen`. | ||
| #' @seealso cluster_gen | ||
| #' @param n numeric vector or list | ||
| #' @return list containing appropriate labels for the clusters and their respondents | ||
| attribute_cluster_labels <- function(n) | ||
| { | ||
| if (!is.null(names(n))) { | ||
| cluster_labels <- names(n) | ||
| resp_labels <- names(n)[-1] | ||
| } else { | ||
| # General lists for all level ranges up to 5 --------------------------- | ||
| cluster_labels <- list(c("school"), | ||
| c("class", "school"), | ||
| c("class", "school", "state"), | ||
| c("class", "school", "city", "state")) | ||
| resp_labels <- list(c("student", "teacher"), | ||
| c("student", "teacher", "principal"), | ||
| c("student", "teacher", "principal", "mayor"), | ||
| c("student", "teacher", "principal", "governor")) | ||
|
|
||
| # Filtering the labels above according to length(n) -------------------- | ||
| cluster_labels <- rev(cluster_labels[[length(n) - 1]]) | ||
| resp_labels <- rev(resp_labels[[length(n) - 1]])[-1] | ||
| } | ||
| return(list(cl = cluster_labels, resp = resp_labels)) | ||
| } |
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