Nothing
# psychonetrics print method
setMethod(f = "show",
signature = "psychonetrics",
definition = function(object){
# Bootstrap warning:
if (object@sample@bootstrap){
boot_warning()
}
# version:
version <- read.dcf(file=system.file("DESCRIPTION", package="psychonetrics"),
fields="Version")
# Make super cool header:
# cat(
# paste0("\t\t#########################\n",
# "\t\t## psychonetrics model ##\n",
# "\t\t#########################\n\n"))
psychonetrics_print_logo()
# output some general stats:
cat("General:",
"\n\t- psychonetrics version:",version,
"\n\t- Model last edited at:",as.character(object@log[[length(object@log)]]@time))
# output some sample specific stats:
cat("\n\nSample:",
"\n\t- Number of cases:",sum(object@sample@groups$nobs),
"\n\t- Number of groups:",nrow(object@sample@groups),
"\n\t- Number of observed summary statistics:",object@sample@nobs)
# output some model specific stats:
mod <- switch(
object@model,
# "lnm" = "Latent Network Model (LNM)",
# "ggm" = "Gaussian graphical model (GGM)",
# "rnm" = "Residual network model (RNM)",
"gvar" = "Graphical vector-autoregression (GVAR)",
"varcov" = "Variance-covariance matrix (varcov)",
# "cholesky" = "Cholesky decomposition (cholesky)",
"lvm" = "Latent variable model (LVM)",
"var1" = "Lag-1 vector-autoregression (VAR1)",
"panelvar1" = "Lag-1 panel vector auto-regression (panelvar1)",
"dlvm1" = "Lag-1 dynamic latent variable model for panel data (dlvm1)",
"tsdlvm1" = "Lag-1 dynamic latent variable model for time-series data (tsdlvm1)",
"meta_varcov" = "Variance-covariance matrix meta analysis",
"Ising" = "Ising model",
"ml_lvm" = "Multi-level latent variable model"
)
submod <- switch(
object@submodel,
"none" = "none",
"lnm" = "Latent Network Model (LNM)",
"ggm" = "Gaussian graphical model (GGM)",
"rnm" = "Residual network model (RNM)",
"gvar" = "Graphical vector-autoregression (GVAR)",
"cholesky" = "Cholesky decomposition (cholesky)",
"sem" = "Structural equation model (SEM)",
"lrnm" = "Latent & residual network model (LRNM)",
"gvar" = "Graphical vector-autoregression (GVAR)",
"var" = "Vector-autoregression (VAR)",
"ml_lnm" = "Multi-level latent network model",
"ml_rnm" = "Multi-level residual network model"
)
if (is.null(submod)){
submod <- object@submodel
}
cat("\n\nModel:",
"\n\t- Model used:",mod,
"\n\t- Submodel used:",submod,
"\n\t- Number of parameters:",max(object@parameters$par))
# Not computeD:
if (!object@computed){
cat("\n\nModel has not yet been computed. Use 'runmodel' to compute parameters and fit measures.")
} else {
estimator <- switch(object@estimator,
"ML" = "Maximum likelihood estimation (ML)",
"FIML" = "Full information maximum likelihood (FIML)",
"ULS" = "Unweighted least squares (ULS)",
"WLS" = "Weighted least squares (WLS)",
"DWLS" = "Diagonally weighted least squares (DWLS)")
# output some optimizer results:
cat("\n\nEstimation:",
"\n\t- Optimizer used:",object@optim$optimizer,
"\n\t- Estimator used:",estimator,
# "\n\t- Number of iterations:",object@optim$iterations,
"\n\t- Message:",object@optim$message
)
# output some fit measures (inspired by Lavaan):
if (!is.null(object@fitmeasures)){
cat("\n\nFit:",
"\n\t- Model Fit Test Statistic:",goodNum(object@fitmeasures$chisq),
"\n\t- Degrees of freedom:",object@fitmeasures$df,
"\n\t- p-value (Chi-square):",goodNum(object@fitmeasures$pvalue)
# "\n\t- RMSEA:",goodNum(object@fitmeasures$rmsea)
)
} else {
cat("\n\nFit has not yet been computed. Use 'addfit' to compute fit measures.")
}
# Next steps:
cat("\n\nTips:",
"\n\t- Use 'psychonetrics::compare' to compare psychonetrics models",
"\n\t- Use 'psychonetrics::fit' to inspect model fit",
"\n\t- Use 'psychonetrics::parameters' to inspect model parameters",
"\n\t- Use 'psychonetrics::MIs' to inspect modification indices"
)
}
# Newline to end:
cat("\n")
})
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