export_univariate_analysis_data {familiar}

Extract and export univariate analysis data of features.

Description

Extract and export univariate analysis data of features for data in a familiarCollection.

Usage

export_univariate_analysis_data(
  object,
  dir_path = NULL,
  p_adjustment_method = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'familiarCollection'
export_univariate_analysis_data(
  object,
  dir_path = NULL,
  p_adjustment_method = waiver(),
  export_collection = FALSE,
  ...
)

## S4 method for signature 'ANY'
export_univariate_analysis_data(
  object,
  dir_path = NULL,
  p_adjustment_method = waiver(),
  export_collection = FALSE,
  ...
)

Arguments

object

A familiarCollection object, or other other objects from which a familiarCollection can be extracted. See details for more information.

dir_path

Path to folder where extracted data should be saved. NULL will allow export as a structured list of data.tables.

p_adjustment_method

(optional) Indicates type of p-value that is shown. One of holm, hochberg, hommel, bonferroni, BH, BY, fdr, none, p_value or q_value for adjusted p-values, uncorrected p-values and q-values. q-values may not be available.

export_collection

(optional) Exports the collection if TRUE.

...

Arguments passed on to extract_univariate_analysis, as_familiar_collection

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.

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_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_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_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.

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.

verbose

Flag to indicate whether feedback should be provided on the computation and extraction of various data elements.

message_indent

Number of indentation steps for messages shown during computation and extraction of various data elements.

familiar_data_names

Names of the dataset(s). Only used if the object parameter is one or more familiarData objects.

collection_name

Name of the collection.

Details

Data is usually collected from a familiarCollection object. However, you can also provide one or more familiarData objects, that will be internally converted to a familiarCollection object. It is also possible to provide a familiarEnsemble or one or more familiarModel objects together with the data from which data is computed prior to export. Paths to the previous files can also be provided.

All parameters aside from object and dir_path are only used if object is not a familiarCollection object, or a path to one.

Univariate analysis includes the computation of p and q-values, as well as robustness (in case of repeated measurements). p-values are derived from Wald's test.

Value

A data.table (if dir_path is not provided), or nothing, as all data is exported to csv files.