Nothing
## \code{$print()} prints the \code{lslx} object. ##
lslx$set("public",
"print",
function() {
cat("Brief Description of Object:\n")
if (length(private$data$response) > 0) {
cat(" Data field is initialized via raw data.\n")
} else {
cat(" Data field is initialized via moment data.\n")
}
if (length(private$model$name_factor) == 0) {
if (!(c("y<-y") %in% private$model$specification$block)) {
cat(" Model field is specified as a covariance analysis (CA) model.\n")
} else {
cat(" Model field is specified as a path analysis (PA) model.\n")
}
} else {
if (!any(c("f<-f", "f<-y", "y<-y", "y<->f", "f<->y") %in% private$model$specification$block)) {
cat(" Model field is specified as a factor analysis (FA) model.\n")
} else {
if (!any(c("f<-f", "y<->f", "f<->y") %in% private$model$specification$block)) {
cat(" Model field is specified as a multiple indicators and multiple causes (MIMIC) model.\n")
} else {
cat(" Model field is specified as a geneal structural equation modeling (SEM) model.\n")
}
}
}
cat(" Response Variable(s):",
private$model$name_response,
"\n")
if (length(private$model$name_factor) > 0) {
cat(" Latent Factor(s):",
private$model$name_factor,
"\n")
}
if (length(private$data$auxiliary) > 0) {
cat(" Auxiliary Variable(s):",
colnames(private$data$auxiliary[[1]]),
"\n")
}
if (length(private$model$level_group) > 1) {
cat(" Group(s):",
private$model$level_group,
"\n")
if (!is.null(private$model$reference_group)) {
cat(" Reference Group:",
private$model$reference_group,
"\n")
}
}
if (is.null(private$fitting)) {
cat(" Fitting field is not yet derived. Please use fit-related methods.\n")
} else {
cat(" Fitting field is derived via maximum likelihood (ML) estimation with",
private$fitting$control$penalty_method, "penalty.\n", sep = " ")
}
cat("\n")
cat("Methods to Manipulate Object: \n")
if (is.null(private$fitting)) {
cat(" To fit the specified model to data, please use fit-related methods.\n")
cat(" $fit() / $fit_lasso() / $fit_mcp() / $fit_none()\n")
cat(" To modify the specified model, please use set-related methods.\n")
cat(" $free_coefficient() / $penalize_coefficient() / $fix_coefficient()\n")
cat(" $free_directed() / $penalize_directed() / $fix_directed()\n")
cat(" $free_undirected() / $penalize_undirected() / $fix_undirected()\n")
cat(" $free_block() / $penalize_block() / $fix_block()\n")
cat(" $free_heterogeneity() / $penalize_heterogeneity() / $fix_heterogeneity()\n")
} else {
cat(" To summarize the fitting results, please use $summarize().\n")
cat(" To plot the fitting results, please use plot-related methods.\n")
cat(" $plot_numerical_condition() / $plot_information_criterion()\n")
cat(" $plot_fit_index() / $plot_coefficients()\n")
cat(" To obtain the test results for model or coefficients, please use test-related methods.\n")
cat(" $test_lr() / $test_rmsea() / $test_coefficient()\n")
cat(" To get a deep copy of field, please use get-related methods.\n")
cat(" $get_model() / $get_data() / $get_fitting()\n")
cat(" To extract specific quantity related to SEM, please use extract-related methods.\n")
cat(" $extract_specification() / $extract_saturated_mean() / $extract_saturated_cov()\n")
cat(" $extract_saturated_moment_acov() \ $extract_penalty_level()\n")
cat(" $extract_numerical_condition() / $extract_information_criterion()\n")
cat(" $extract_fit_index() / $extract_coefficient()\n")
cat(" $extract_implied_cov() / $extract_implied_mean()\n")
cat(" $extract_residual_cov() / $extract_residual_mean()\n")
cat(" $extract_coefficient_matrix() / $extract_moment_jacobian()\n")
cat(" $extract_expected_information() / $extract_observed_information()\n")
cat(" $extract_score_acov() / $extract_coefficient_acov()\n")
cat(" $extract_loss_gradient() / $extract_regularizer_gradient() / $extract_objective_gradient()\n")
}
cat("\n")
cat("To learn more about 'lslx' object, please try 'help(lslx)' or see GitHub wiki (https://github.com/psyphh/lslx/wiki).")
})
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