preprocess: Preprocess input data with Principal Component Analysis...
In izhbannikov/rqt: rqt: utilities for gene-level meta-analysis

preprocess

R Documentation

Preprocess input data with Principal Component Analysis method (PCA)

Description

Preprocess input data with Principal Component Analysis method (PCA)

Usage

preprocess(
  data,
  pheno = NULL,
  method = "pca",
  reg.family = "binomial",
  scaleData = FALSE,
  cumvar.threshold = 75,
  out.type = "D",
  penalty = 0.001,
  verbose = FALSE
)

Arguments

`data`	An input matrix with values of independent variables (predictors).
`pheno`	A phenotype - column-vector, needed for LASSO/ridge and `NULL` by default.
`method`	A dimensionality reduction method. Default: `pca`.
`reg.family`	A regression family. Default: `"binomial"`.
`scaleData`	A logical variable, indicates wheither or not scaling should be performed. Default: `FALSE`.
`cumvar.threshold`	A threshold value for explained variance. Default: `75`
`out.type`	An output (phenotype) type. Default: `"D"`
`penalty`	Value of penalty parameter for LASSO/ridge regression. Default: `0.001`
`verbose`	Indicates verbosing output. Default: FALSE.