extract_univariate_analysis: Internal function to extract data from a univariate analysis.

In familiar: End-to-End Automated Machine Learning and Model Evaluation

Internal function to extract data from a univariate analysis.

Description

Computes and extracts univariate analysis for the features used in a familiarEnsemble object. This assessment includes the computation of p and q-values, as well as robustness (in case of repeated measurements).

Usage

extract_univariate_analysis(
  object,
  data,
  cl = NULL,
  icc_type = waiver(),
  feature_similarity = NULL,
  feature_cluster_method = waiver(),
  feature_cluster_cut_method = waiver(),
  feature_linkage_method = waiver(),
  feature_similarity_threshold = waiver(),
  feature_similarity_metric = waiver(),
  message_indent = 0L,
  verbose = FALSE,
  ...
)

Arguments

object	A familiarEnsemble object, which is an ensemble of one or more familiarModel objects.
data	A dataObject object, data.table or data.frame that constitutes the data that are assessed.
cl	Cluster created using the parallel package. This cluster is then used to speed up computation through parallellisation.
icc_type	String indicating the type of intraclass correlation coefficient (1, 2 or 3) that should be used to compute robustness for features in repeated measurements during the evaluation of univariate importance. These types correspond to the types in Shrout and Fleiss (1979). If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.
feature_cluster_method	The method used to perform clustering. These are the same methods as for the cluster_method configuration parameter: none, hclust, agnes, diana and pam. none cannot be used when extracting data regarding mutual correlation or feature expressions. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.
feature_cluster_cut_method	The method used to divide features into separate clusters. The available methods are the same as for the cluster_cut_method configuration parameter: silhouette, fixed_cut and dynamic_cut. silhouette is available for all cluster methods, but fixed_cut only applies to methods that create hierarchical trees (hclust, agnes and diana). dynamic_cut requires the dynamicTreeCut package and can only be used with agnes and hclust. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.
feature_linkage_method	The method used for agglomerative clustering in hclust and agnes. These are the same methods as for the cluster_linkage_method configuration parameter: average, single, complete, weighted, and ward. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.
feature_similarity_threshold	The threshold level for pair-wise similarity that is required to form feature clusters with the fixed_cut method. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.
feature_similarity_metric	Metric to determine pairwise similarity between features. Similarity is computed in the same manner as for clustering, and feature_similarity_metric therefore has the same options as cluster_similarity_metric: mcfadden_r2, cox_snell_r2, nagelkerke_r2, spearman, kendall and pearson. If not provided explicitly, this parameter is read from settings used at creation of the underlying familiarModel objects.
message_indent	Number of indentation steps for messages shown during computation and extraction of various data elements.
verbose	Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.
...	Unused arguments.

Value

A list with a data.table containing information concerning the univariate analysis of important features.

familiar documentation built on Sept. 30, 2024, 9:18 a.m.