Nothing
print.mgm <- function(x,
...)
{
model_classes <- c('Mixed Graphical Model (MGM)',
'mixed Vector Autoregressive (mVAR) model',
'Time-varying Mixed Graphical Model (tv-MGM)',
'Time-varying mixed Vector Autoregressive (tv-mVAR) model')
# ---------- print for fit objects ----------
if(!('predicted' %in% class(x)) & !('bwSelect' %in% class(x))) {
if('core' %in% class(x)) {
msg_basic <- paste0('mgm fit-object',
'\n\nModel class: ', model_classes[1],
'\nOrder: ' , x$call$k,
'\nNodes: ' , length(x$call$type))
if(!is.null(x$call$moderators)) {
x$call$k <- 3
msg_basic <- paste0('mgm fit-object',
'\n\nModel class: ', model_classes[1],
'\nOrder: ' , x$call$k,
'\nNodes: ' , length(x$call$type)) # call again with updated x$call$k
if(is.matrix(x$call$moderators)) mod_text <- "Custom specification" else mod_text <- paste(x$call$moderators, collapse = ", ")
msg_basic <- paste0(msg_basic,
paste0('\nModerators: ' , mod_text))
}
if(!is.null(x$call$condition)) {
nCond <- length(x$call$condition)
names <- names(x$call$condition)
msgCond <- paste0(names, "=", unlist(x$call$condition))
msg_basic <- paste0(msg_basic, "\nFixed: ", paste(msgCond, collapse = ", "))
}
cat(msg_basic)
} # end if: basic mgm object?
if('mvar' %in% class(x)) {
n_incl <- sum(x$call$data_lagged$included == TRUE)
n_exp <- sum(x$call$data_lagged$included == FALSE)
n <- n_incl + n_exp
perc <- n_incl / n
perc <- round(perc, 4) * 100
cat('mgm fit-object',
'\n\nModel class: ', model_classes[2],
'\nLags: ' , x$call$lags,
'\nRows included in VAR design matrix: ' , n_incl ,'/', n, '(', perc, '%)',
'\nNodes: ' , length(x$call$type))
}
if('tvmgm' %in% class(x)) {
if(!is.null(x$call$moderators)) {
x$call$k <- 3
cat('mgm fit-object',
'\n\nModel class: ', model_classes[3],
'\nOrder: ' , x$call$k,
'\nNodes: ' , length(x$call$type),
'\nModerators: Variable ' , x$call$moderators,
'\nEstimation points: ' , length(x$call$estpoints))
} else {
cat('mgm fit-object',
'\n\nModel class: ', model_classes[3],
'\nOrder: ' , x$call$k,
'\nNodes: ' , length(x$call$type),
'\nEstimation points: ' , length(x$call$estpoints))
}
}
if('tvmvar' %in% class(x)) {
n_incl <- sum(x$call$data_lagged$included == TRUE)
n_exp <- sum(x$call$data_lagged$included == FALSE)
n <- n_incl + n_exp
perc <- n_incl / n
perc <- round(perc, 4) * 100
cat('mgm fit-object',
'\n\nModel class: ', model_classes[4],
'\nLags: ' , x$call$lags,
'\nRows included in VAR design matrix: ' , n_incl ,'/', n, '(', perc, '%)',
'\nNodes: ' , length(x$call$type),
'\nEstimation points: ' , length(x$call$estpoints))
}
}
# ---------- print for prediction object ----------
if('predicted' %in% class(x)) {
if('mgm' %in% class(x)) mc <- model_classes[1]
if('mvar' %in% class(x)) mc <- model_classes[2]
if('tvmgm' %in% class(x)) mc <- model_classes[3]
if('tvmvar' %in% class(x)) mc <- model_classes[4]
cat('mgm prediction-object',
'\n\nModel class: ', mc,
'\nError Types:', paste(names(x$call$errorCon), names(x$call$errorCat)))
}
# ---------- print for bwSelect object ----------
if('bwSelect' %in% class(x)) {
if('mgm' %in% class(x)) mc <- model_classes[3]
if('mvar' %in% class(x)) mc <- model_classes[4]
cat('mgm bandwidth selection-object',
'\n\nModel class: ', mc,
'\nBandwith path: ', paste(x$call$bwSeq),
'\nNumber of Folds: ', paste(x$call$bwFolds),
'\nFoldsize: ', paste(x$call$bwFoldsize),
'\nOptimal Bandwidth: ', x$call$bwSeq[which.min(x$meanError)])
}
} # eoF
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