Calculate feature NMIs for a data_list and a derived solutions_matrix

Usage

batch_nmi(
  data_list,
  solutions_matrix,
  clust_algs_list = NULL,
  distance_metrics_list = NULL,
  automatic_standard_normalize = FALSE,
  transpose = TRUE,
  ignore_inclusions = TRUE,
  verbose = FALSE
)

Arguments

data_list: A nested list of input data from generate_data_list(). Use the same value as was used in the original call to batch_snf().
solutions_matrix: Result of batch_snf storing cluster solutions and the settings that were used to generate them. Use the same value as was used in the original call to batch_snf().
clust_algs_list: List of custom clustering algorithms to apply to the final fused network. See ?generate_clust_algs_list. Use the same value as was used in the original call to batch_snf().
distance_metrics_list: An optional nested list containing which distance metric function should be used for the various feature types (continuous, discrete, ordinal, categorical, and mixed). Use the same value as was used in the original call to batch_snf().
automatic_standard_normalize: If TRUE, will automatically apply standard normalization prior to calculation of any distance matrices. Use the same value as was used in the original call to batch_snf().
transpose: If TRUE, will transpose the output dataframe.
ignore_inclusions: If TRUE, will ignore the inclusion columns in the solutions matrix and calculate NMIs for all features. If FALSE, will give NAs for features that were dropped on a given settings_matrix row.
verbose: If TRUE, print progress to console.

Value

A "data.frame" class object containing one row for every feature in the provided data list and one column for every solution in the provided solutions matrix. Populated values show the calculated NMI score for each feature-solution combination.