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strengejackestrengejacke
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use pkg conditionally
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‎R/model_parameters.gam.R

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#'
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#' @examples
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#' library(parameters)
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#' library(mgcv)
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#'
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#' dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
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#' model <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
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#' model_parameters(model)
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#' if (require("mgcv")) {
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#' dat <- gamSim(1, n = 400, dist = "normal", scale = 2)
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#' model <- gam(y ~ s(x0) + s(x1) + s(x2) + s(x3), data = dat)
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#' model_parameters(model)
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#' }
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#' @export
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model_parameters.gam <- function(model, ci = .95, bootstrap = FALSE, iterations = 1000, standardize = NULL, exponentiate = FALSE, robust = FALSE, p_adjust = NULL, ...) {
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# Processing

‎R/model_parameters.psych.R

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#'
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#' @examples
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#' library(parameters)
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#' library(psych)
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#' if (require("psych")) {
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#' # Principal Component Analysis (PCA) ---------
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#' pca <- psych::principal(attitude)
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#' model_parameters(pca)
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#'
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#' # Principal Component Analysis (PCA) ---------
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#' pca <- psych::principal(attitude)
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#' model_parameters(pca)
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#' pca <- psych::principal(attitude, nfactors = 3, rotate = "none")
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#' model_parameters(pca, sort = TRUE, threshold = 0.2)
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#'
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#' pca <- psych::principal(attitude, nfactors = 3, rotate = "none")
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#' model_parameters(pca, sort = TRUE, threshold = 0.2)
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#' principal_components(attitude, n = 3, sort = TRUE, threshold = 0.2)
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#'
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#' principal_components(attitude, n = 3, sort = TRUE, threshold = 0.2)
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#' \donttest{
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#' # Exploratory Factor Analysis (EFA) ---------
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#' efa <- psych::fa(attitude, nfactors = 3)
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#' model_parameters(efa, threshold = "max", sort = TRUE, labels = as.character(1:ncol(attitude)))
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#' # Exploratory Factor Analysis (EFA) ---------
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#' efa <- psych::fa(attitude, nfactors = 3)
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#' model_parameters(efa, threshold = "max", sort = TRUE, labels = as.character(1:ncol(attitude)))
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#' }
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#'
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#' # Omega ---------
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#' omega <- psych::omega(mtcars, nfactors = 3)
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#' params <- model_parameters(omega)
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#' params
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#' summary(params)
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#'
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#' # Omega ---------
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#' omega <- psych::omega(mtcars, nfactors = 3)
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#' params <- model_parameters(omega)
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#' params
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#' summary(params)
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#' }
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#'
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#' # FactoMineR ---------
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#' \dontrun{
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#' library(FactoMineR)
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#' if( require("FactoMineR")) {
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#' model <- FactoMineR::PCA(iris[, 1:4], ncp = 2)
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#' model_parameters(model)
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#' attributes(model_parameters(model))$scores
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#'
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#' model <- FactoMineR::PCA(iris[, 1:4], ncp = 2)
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#' model_parameters(model)
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#' attributes(model_parameters(model))$scores
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#'
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#' model <- FactoMineR::FAMD(iris, ncp = 2)
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#' model_parameters(model)
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#' model <- FactoMineR::FAMD(iris, ncp = 2)
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#' model_parameters(model)
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#' }
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#' }
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#' @return A data frame of loadings.
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#' @references \itemize{

‎R/model_parameters_mixed.R

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#' model <- lmer(mpg ~ wt + (1 | gear), data = mtcars)
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#' model_parameters(model)
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#' }
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#'
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#' \donttest{
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#' if (require("glmmTMB")) {
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#' data(Salamanders)

‎R/principal_components.R

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#'
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#' @examples
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#' library(parameters)
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#' if (require("psych")) {
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#' principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
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#' principal_components(mtcars[, 1:7], n = 2, rotation = "oblimin",
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#' threshold = "max", sort = TRUE)
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#' principal_components(mtcars[, 1:7], n = 2, threshold = 2, sort = TRUE)
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#'
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#' principal_components(mtcars[, 1:7], n = "all", threshold = 0.2)
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#' principal_components(mtcars[, 1:7], n = 2, rotation = "oblimin", threshold = "max", sort = TRUE)
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#' principal_components(mtcars[, 1:7], n = 2, threshold = 2, sort = TRUE)
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#' pca <- principal_components(mtcars[, 1:5], n = 2, rotation = "varimax")
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#' summary(pca)
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#' predict(pca)
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#'
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#' pca <- principal_components(mtcars[, 1:5], n = 2, rotation = "varimax")
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#' summary(pca)
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#' predict(pca)
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#'
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#' # which variables from the original data belong to which extracted component?
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#' closest_component(pca)
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#' # which variables from the original data belong to which extracted component?
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#' closest_component(pca)
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#'
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#' \donttest{
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#' # Automated number of components
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#' principal_components(mtcars[, 1:4], n = "auto")
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#' # Automated number of components
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#' principal_components(mtcars[, 1:4], n = "auto")
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#' }
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#' }
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#'
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#' @return A data frame of loadings.
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#' @references \itemize{
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#' \item Kaiser, H.F. and Rice. J. (1974). Little jiffy, mark iv. Educational and Psychological Measurement, 34(1):111–117

‎man/model_parameters.gam.Rd

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‎man/model_parameters.merMod.Rd

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‎man/model_parameters.principal.Rd

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‎man/principal_components.Rd

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‎tests/testthat/test-model_parameters_std.R

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.runThisTest <- Sys.getenv("RunAllparametersTests") == "yes"
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if (.runThisTest || Sys.getenv("USER") == "travis") {
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if (.runThisTest) {
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if (require("testthat") &&
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require("parameters") &&

‎tests/testthat/test-model_parameters_std_mixed.R

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.runThisTest <- Sys.getenv("RunAllparametersTests") == "yes"
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if (.runThisTest || Sys.getenv("USER") == "travis") {
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if (.runThisTest) {
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if (require("testthat") &&
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require("parameters") &&
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require("effectsize") &&
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require("lme4")) {
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data(iris)

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