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#' Conversion between EFA results and CFA structure
#'
#' Enables a conversion between Exploratory Factor Analysis (EFA) and
#' Confirmatory Factor Analysis (CFA) `lavaan`-ready structure.
#'
#' @param model An EFA model (e.g., a `psych::fa` object).
#' @param names Vector containing dimension names.
#' @param max_per_dimension Maximum number of variables to keep per dimension.
#' @inheritParams principal_components
#'
#' @examplesIf require("psych") && require("lavaan")
#' \donttest{
#' library(parameters)
#' data(attitude)
#' efa <- psych::fa(attitude, nfactors = 3)
#'
#' model1 <- efa_to_cfa(efa)
#' model2 <- efa_to_cfa(efa, threshold = 0.3)
#' model3 <- efa_to_cfa(efa, max_per_dimension = 2)
#'
#' suppressWarnings(anova(
#' lavaan::cfa(model1, data = attitude),
#' lavaan::cfa(model2, data = attitude),
#' lavaan::cfa(model3, data = attitude)
#' ))
#' }
#' @return Converted index.
#' @export
convert_efa_to_cfa <- function(model, ...) {
UseMethod("convert_efa_to_cfa")
}
#' @rdname convert_efa_to_cfa
#' @inheritParams model_parameters.principal
#' @export
convert_efa_to_cfa.fa <- function(model,
threshold = "max",
names = NULL,
max_per_dimension = NULL,
...) {
.efa_to_cfa(model_parameters(model, threshold = threshold, ...),
names = names,
max_per_dimension = max_per_dimension,
...
)
}
#' @export
convert_efa_to_cfa.fa.ci <- convert_efa_to_cfa.fa
#' @export
convert_efa_to_cfa.parameters_efa <- function(model,
threshold = NULL,
names = NULL,
max_per_dimension = NULL,
...) {
if (!is.null(threshold)) {
model <- model_parameters(attributes(model)$model, threshold = threshold, ...)
}
.efa_to_cfa(model, names = names, max_per_dimension = max_per_dimension, ...)
}
#' @export
convert_efa_to_cfa.parameters_pca <- convert_efa_to_cfa.parameters_efa
#' @rdname convert_efa_to_cfa
#' @export
efa_to_cfa <- convert_efa_to_cfa
#' @keywords internal
.efa_to_cfa <- function(loadings, names = NULL, max_per_dimension = NULL, ...) {
loadings <- attributes(loadings)$loadings_long
# Get dimension names
if (is.null(names)) {
names <- unique(loadings$Component)
}
# Catch error
if (length(names) != insight::n_unique(loadings$Component)) {
insight::format_error(
paste0(
"The `names` vector must be of same length as the number of dimensions, in this case ",
length(unique(loadings$Component)),
"."
)
)
}
cfa <- NULL
# Iterate over dimensions
for (i in seq_along(names)) {
# Find correct subset
items <- loadings[loadings$Component == unique(loadings$Component)[i], ]
# Find corresponding items
items <- as.character(loadings[loadings$Component == unique(loadings$Component)[i], "Variable"])
# Subset if need be to keep only a certain number
if (!is.null(max_per_dimension) && max_per_dimension > 0) {
items <- as.character(stats::na.omit(items[1:max_per_dimension]))
}
# Append that list
cfa <- c(cfa, paste0(names[i], " =~ ", paste(items, collapse = " + ")))
}
cfa <- paste0(cfa, collapse = "\n")
cfa <- paste0("# Latent variables\n", cfa)
class(cfa) <- c("cfa_model", class(cfa))
cfa
}
#' @export
print.cfa_model <- function(x, ...) {
cat(x)
invisible(x)
}
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