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#' `cSEMSummarize` method for `print()`
#'
#' The [cSEMSummary] method for the generic function [print()].
#'
#' @inheritParams csem_arguments
#'
#' @seealso [csem()], [cSEMResults], [summarize()]
#'
#' @export
#' @keywords internal
print.cSEMSummarize <- function(x, .full_output = TRUE, ...) {
## Check the class
if(inherits(x, "cSEMSummarize_2ndorder")) {
x11 <- x$First_stage$Estimates
x12 <- x$First_stage$Information
x21 <- x$Second_stage$Estimates
x22 <- x$Second_stage$Information
# Correlations
construct_cor <- x21$Exo_construct_correlation
res_cor <- x11$Residual_correlation
indi_cor <- x11$Indicator_correlation
} else {
x21 <- x$Estimates
x22 <- x$Information
# Correlation
construct_cor <- x21$Exo_construct_correlation
res_cor <- x21$Residual_correlation
indi_cor <- x21$Indicator_correlation
}
cat2(
rule2(type = 2), "\n",
rule2("Overview"),
"\n"
)
### Overview -----------------------------------------------------------------
## General information + resample information
printSummarizeOverview(x)
## Construct details
cat2("\n\n\tConstruct details:\n\t","------------------")
printSummarizeConstructDetails(x)
### Estimates ----------------------------------------------------------------
cat2("\n\n", rule2("Estimates"))
## Confidence intervals
# Get the column names of the columns containing confidence intervals
ci_colnames <- colnames(x21$Loading_estimates)[-c(1:6)]
# Are there more confidence intervals than the default (the 95% percentile CI)
# Inform the user to use xxx instead.
if(length(ci_colnames) > 2) {
cat2(
"By default, only one confidence interval supplied to `.ci` is printed.\n",
"Use `xxx` to print all confidence intervals (not yet implemented)."
)
ci_colnames <- ci_colnames[1:2]
cat("\n\n")
}
## Path estimates
if(!is.null(x21$Path_estimates)) {
cat2("\n\nEstimated path coefficients:\n============================")
printSummarizePathCorrelation(x, .ci_colnames = ci_colnames)
}
## Loadings and Weights
printSummarizeLoadingsWeights(x, .ci_colnames = ci_colnames)
## Exogenous construct correlation
if(.full_output && nrow(construct_cor) != 0) {
cat2("\n\nEstimated construct correlations:\n=================================")
printSummarizePathCorrelation(x, .ci_colnames = ci_colnames,
.what = "Construct correlation")
}
## Residual correlation
if(.full_output && nrow(res_cor) != 0) {
cat2("\n\nEstimated measurement error correlations:\n=========================================")
printSummarizePathCorrelation(x, .ci_colnames = ci_colnames,
.what = "Residual correlation")
}
## Indicator correlation
if(.full_output && nrow(indi_cor) != 0) {
cat2("\n\nEstimated indicator correlations:\n=================================")
printSummarizePathCorrelation(x, .ci_colnames = ci_colnames,
.what = "Indicator correlation")
}
if(.full_output && x22$Model$model_type == "Linear" && !is.null(x21$Path_estimates)) {
### Effects ----------------------------------------------------------------
cat2("\n\n", rule2("Effects"), "\n\n")
## Path estimates
cat2("Estimated total effects:\n========================")
printSummarizePathCorrelation(x, .ci_colnames = ci_colnames,
.what = "Total effect")
if(any(names(x21$Effect_estimates) == "Indirect_effect")) {
cat2("\n\nEstimated indirect effects:\n===========================")
printSummarizePathCorrelation(x, .ci_colnames = ci_colnames,
.what = "Indirect effect")
}
}
cat2("\n", rule2(type = 2), "\n")
}
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