Nothing
#' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_all (generic) -----------------------------------------------------------
setGeneric("plot_all", function(object, ...) standardGeneric("plot_all"))
# plot_all (collection) --------------------------------------------------------
setMethod(
"plot_all",
signature(object = "familiarCollection"),
function(object, dir_path = NULL, ...) {
if (!is.null(dir_path)) dir_path <- encapsulate_path(dir_path)
# Make sure the collection object is updated.
object <- update_object(object = object)
# Feature univariate p-values (horizontal bars)
do.call(
plot_univariate_importance,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Feature occurrence (unclustered)
do.call(
plot_feature_selection_occurrence,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Feature ranking (unclustered)
do.call(
plot_feature_selection_variable_importance,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Model signature occurrence (unclustered)
do.call(
plot_model_signature_occurrence,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Model signature ranking (unclustered)
do.call(
plot_model_signature_variable_importance,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Permutation variable importance
do.call(
plot_permutation_variable_importance,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Feature similarity heatmap
do.call(
plot_feature_similarity,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Calibration curves
do.call(
plot_calibration_data,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Model performance
do.call(
plot_model_performance,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# AUC-ROC curve
do.call(
plot_auc_roc_curve,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# AUC-PR curve
do.call(
plot_auc_precision_recall_curve,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Decision curve
do.call(
plot_decision_curve,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Kaplan-Meier curves
do.call(
plot_kaplan_meier,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Feature expressions
do.call(
plot_sample_clustering,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Confusion matrix
do.call(
plot_confusion_matrix,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Individual conditional expectation
do.call(
plot_ice,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
# Partial dependence
do.call(
plot_pd,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...)))
return(invisible(NULL))
}
)
# plot_all (general) -----------------------------------------------------------
setMethod(
"plot_all",
signature(object = "ANY"),
function(object, dir_path = NULL, ...) {
# Attempt conversion to familiarCollection object.
object <- do.call(
as_familiar_collection,
args = c(
list(
"object" = object,
"data_element" = "all"),
list(...)))
return(do.call(
plot_all,
args = c(
list(
"object" = object,
"dir_path" = dir_path),
list(...))))
}
)
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