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).
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,
...
)A list with a data.table containing information concerning the univariate analysis of important features.
A familiarEnsemble object, which is an ensemble of one or more
familiarModel objects.
A dataObject object, data.table or data.frame that
constitutes the data that are assessed.
Cluster created using the parallel package. This cluster is then
used to speed up computation through parallellisation.
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.
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.
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.
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.
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.
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.
Number of indentation steps for messages shown during computation and extraction of various data elements.
Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.
Unused arguments.