plotVarExplained: Plot data summary statistics

In BioMM: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data

Description

Plot data summary statistics in terms of the proportion of variance explained.

Usage

plotVarExplained(
  data,
  posF = TRUE,
  binarize = FALSE,
  core = MulticoreParam(),
  pathTitle = "GO pathways",
  fileName = NULL
)

Arguments

data	The input dataset (either data.frame or matrix). Rows are the samples, columns are the probes/genes, except that the first column is the label (the outcome).
posF	A logical value indicating if only positively outcome-associated features should be used. (Default: TRUE)
binarize	A logical value indicating if the individual features under investigation should be binarized. The default is FALSE, which provides the estimated class probabilities for each pathway-level feature. If TRUE, then the binary output is given for each feature.
core	The number of cores used for computation. (Default: 1)
pathTitle	A string indicating the name of pathway under investigation. This will be displayed as the name of y-axis.
fileName	The file name specified for the plot. If it is not NULL, then the plot will be generated. The plot will project the data on the first two components. (Default: 'R2explained.png')

Value

An output image file with '.png' format.

References

Yu, Guangchuang, et al. 'clusterProfiler: an R package for comparing biological themes among gene clusters.' Omics: a journal of integrative biology 16.5 (2012): 284-287.

Perlich, C., & Swirszcz, G. (2011). On cross-validation and stacking: Building seemingly predictive models on random data. ACM SIGKDD Explorations Newsletter, 12(2), 11-15.

BioMM documentation built on Nov. 8, 2020, 11:04 p.m.