CV.ENET: Fit a regularized linear model

In biobakery/melonnpan: Model-based Genomically Informed High-dimensional Predictor of Microbial Community Metabolite Profiles

Fit a regularized linear model

Description

Fit a regularized linear model

Usage

CV.ENET(
  metab = metab,
  metag = metag,
  alpha = alpha,
  lambda.choice = lambda.choice,
  nfolds = nfolds,
  foldid = foldid,
  verbose = verbose,
  plot = plot,
  outputDirectory = outputDirectory
)

Arguments

metab	Training data of metabolite relative abundances. Should have the exact same rows (subjects/samples) as metag.
metag	Training data of microbial sequence features' relative abundances. Should have the exact same rows (subjects/samples) as metab.
alpha	Grid of alpha values between 0 and 1. Default is 'seq(0.05, 0.95, 0.05)'.
lambda.choice	Choice of optimal lambda ('lambda.min' or 'lambda.1se'). Default is 'lambda.1se'.
nfolds	Number of folds for internal cross-validation. Default is 10.
foldid	A vector of values between 1 and nfold identifying what fold each observation is in.
verbose	Should progress bar be printed. Default is TRUE.
plot	Should CV error as a function of lambda be plotted. Default is FALSE.
outputDirectory	Name of the desired output directory.

biobakery/melonnpan documentation built on March 26, 2024, 11:42 p.m.