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‎R/check_autocorrelation.R

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@@ -49,7 +49,6 @@ check_autocorrelation.default <- function(x, nsim = 1000, ...) {
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}
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# methods ------------------------------
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#' @export
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}
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# utilities -------------------------------
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.durbWats <- function(.residuals) {

‎R/check_clusterstructure.R

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}
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#' @export
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plot.check_clusterstructure <- function(x, ...) {
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# Can be reimplemented with ggplot in see
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}
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#' @keywords internal
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.clusterstructure_dm <- function(x, distance = "euclidean", method = "ward.D2") {
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d <- stats::dist(x, method = distance)
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}
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#' @keywords internal
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.clusterstructure_hopkins <- function(x, distance = "euclidean") {
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# This is based on the hopkins() function from the clustertend package

‎R/check_collinearity.R

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multicollinearity <- check_collinearity
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# default ------------------------------
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#' @rdname check_collinearity
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}
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# methods -------------------------------------------
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#' @export
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}
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# other classes ----------------------------------
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#' @export
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check_collinearity.betamfx <- check_collinearity.logitor
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# zi-models -------------------------------------
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#' @rdname check_collinearity
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}
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# utilities ---------------------------------
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.check_collinearity_zi_model <- function(x, component, ci = 0.95, verbose = TRUE) {
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}
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.check_collinearity <- function(x, component, ci = 0.95, verbose = TRUE) {
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v <- .safe(insight::get_varcov(x, component = component, verbose = FALSE))
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}
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.term_assignments <- function(x, component, verbose = TRUE) {
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tryCatch(
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{
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}
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.find_term_assignment <- function(x, component, verbose = TRUE) {
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pred <- insight::find_predictors(x)[[component]]
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.zi_term_assignment <- function(x, component = "zero_inflated", verbose = TRUE) {
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tryCatch(
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{

‎R/check_concurvity.R

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}
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# methods ---------------------------------
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#' @export

‎R/check_distribution.R

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}
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# default -----------------------------
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#' @export
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}
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# methods --------------------------
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#' @export
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# other classes -------------------
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#' @export
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}
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# utilities -----------------------------
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.extract_features <- function(x, type = NULL) {
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.is_integer <- function(x) {
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tryCatch(
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ifelse(is.infinite(x), FALSE, x %% 1 == 0),

‎R/check_factorstructure.R

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#' @rdname check_factorstructure
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#' @export
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check_kmo <- function(x, n = NULL, ...) {
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#' @rdname check_factorstructure
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#' @export
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check_sphericity_bartlett <- function(x, n = NULL, ...) {
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# Helpers -----------------------------------------------------------------
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#' @keywords internal

‎R/check_heterogeneity_bias.R

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#' @export
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print.check_heterogeneity_bias <- function(x, ...) {
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cat("Possible heterogeneity bias due to following predictors: ")

‎R/check_heteroscedasticity.R

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# methods -----------------------
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#' @export
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# utilities -----------------------
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.sigma <- function(x) {

‎R/check_homogeneity.R

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# default -------------------------
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#' @export
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# methods -----------------------
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# other classes -----------------------------
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#' @rdname check_homogeneity

‎R/check_htest.R

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# }
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# Print -------------------------------------------------------------------
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#' @export

‎R/check_itemscale.R

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# methods -------------------------------------
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#' @export

‎R/check_model.R

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# default ----------------------------
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#' @rdname check_model
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# other classes ---------------------------
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## TODO for now, convert to freq, see https://github.com/easystats/performance/issues/354
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check_model.DHARMa <- check_model.performance_simres
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# compile plots for checks of linear models ------------------------
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.check_assumptions_linear <- function(model, model_info, check = "all", residual_type = "normal", verbose = TRUE, ...) {
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# compile plots for checks of generalized linear models ------------------------
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.check_assumptions_glm <- function(model, model_info, check = "all", residual_type = "simulated", verbose = TRUE, ...) {
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# compile plots for checks of Bayesian models ------------------------
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.check_assumptions_stan <- function(model, ...) {

‎R/check_model_diagnostics.R

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# prepare data for QQ plot ----------------------------------
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.model_diagnostic_qq <- function(model, model_info = NULL, verbose = TRUE) {
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# prepare data for random effects QQ plot ----------------------------------
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.model_diagnostic_ranef_qq <- function(model, level = 0.95, model_info = NULL, verbose = TRUE) {
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# prepare data for normality of residuals plot ----------------------------------
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.model_diagnostic_normality <- function(model, verbose = TRUE) {
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# prepare data for influential obs plot ----------------------------------
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.model_diagnostic_outlier <- function(model, threshold = NULL) {
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# prepare data for non-constant variance plot ----------------------------------
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.model_diagnostic_ncv <- function(model, verbose = TRUE) {
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# prepare data for homogeneity of variance plot ----------------------------------
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.model_diagnostic_homogeneity <- function(model, verbose = TRUE) {
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.new_diag_overdispersion <- function(model, ...) {
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.model_diagnostic_overdispersion <- function(model, ...) {
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faminfo <- insight::model_info(model)
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‎R/check_multimodal.R

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check_multimodal.numeric <- function(x, ...) {
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insight::check_if_installed("multimode")

‎R/check_normality.R

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# mixed models ---------------------
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check_normality.BFBayesFactor <- check_normality.afex_aov
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.check_normality <- function(x, model, type = "residuals") {

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