update_pairwise_feature_mat: Remove elder from pairwise feature matrix

In mdkessler/kTSCR: Implements k-Top Scoring Cluster Regression

Description

Remove all columns from pairwise_feature_mat that have elder as one of the features in each pairwise feature

Usage

update_pairwise_feature_mat(pairwise_feature_mat, elder)

Arguments

pairwise_feature_mat	a matrix of feature pair score for each sample
elder	character vector (i.e. string) representing the name of the elder feature

Value

returns pairwise feature matrix but without the columns that had pairwise features where one feature was the elder feature

Examples

C <- 100  # represents samples
R <- 200 # represents features
y <- stats::rnorm(C) # represents outcome variable
X <- matrix(rbeta(R*C, 2, 3), nrow = R)  # simulate data matrix
Is <- get_pairwise_rank_matrices(X)
pairwise_feature_mat <- make_feature_pair_score_matrix(Is)
sorted_corrs <- get_sorted_corrs_pairwise_features(pairwise_feature_mat, y)
elder_corr <- get_elder(sorted_corrs)
elder <- names(elder_corr)
pairwise_feature_mat <- update_pairwise_feature_mat(pairwise_feature_mat, elder)

mdkessler/kTSCR documentation built on Feb. 25, 2021, 10:31 p.m.