RunCrossValidation: Run cross validation for both alpha and lambda

In yanwu2014/perturbLM: A lightweight R package that uses linear models to analyze the effects of chemical/genetic perturbations, conditions, and disease states on single gene expression.

Run cross validation for both alpha and lambda

Description

Run cross validation for both alpha and lambda

Usage

RunCrossValidation(
  x,
  y,
  groups,
  alpha.seq = c(0.1, 0.4, 0.7, 0.9, 0.95, 1),
  plot = T,
  n.cores = 4,
  family = "mgaussian",
  nlambda = 10,
  lambda.min.ratio = 0.01,
  nfolds = 4,
  seed = NULL,
  seq.lambda.pred = F
)

Arguments

x	Design matrix + covariates matrix
y	Expression response
groups	Perturbation dictionary (in list format) or named vector
alpha.seq	Sequence of alpha values to test
plot	Plot cross validation results (default: True)
n.cores	Number of cores to use (default: 4)
family	GLM family to use for elasticnet (default: mgaussian)
nlambda	Number of lambda values to test (default: 10)
lambda.min.ratio	Sets the minimum lambda value to test (default: 0.01)
nfolds	Number of folds to cross validate over (default: 5)
seed	Random seed for fold reproducibility
seq.lambda.pred	Predict expression at each lambda value sequentially to save memory (default: F)

Value

List of results containing cross validation objects (cv.list), cross validation summary stats (cv.summary), and the optimal lambda/alpha combo (alpha, lambda)

yanwu2014/perturbLM documentation built on Aug. 24, 2023, 2:28 p.m.