tuneClassifier: Evaluate model performance by initializing many analysis...

In taylo5jm/interactions: Classification of gene expression profile sets generated from combination stimuli

Description

Evaluate model performance by initializing many analysis objects given a vector of alphas for coding confidence intervals

Usage

tuneClassifier(counts, genes, metadata, alphas, DRUG_COMBINATION = TRUE,
  null = FALSE)

Arguments

counts	numeric matrix of counts where rows are genes and columns are libraries
genes	data.frame of gene metadata
metadata	data.frame with metadata retrieved for 4 conditions
alphas	numeric vector of length n with alphas to code outcome vectors with
DRUG_COMBINATION	Boolean TRUE if drug combination
null	Boolean TRUE if null distribution should be simulated by permuting columns of count matrix

Value

list of n = length(alphas), see output for edgeRModeClassifier and limmaModeClassifier

taylo5jm/interactions documentation built on May 31, 2019, 3:57 a.m.