lm.compare.method: Get fitted models by fitting some variable selection methods

In CSUV: Combined Selection and Uncertainty Visualiser (CSUV)

Description

Get fitted models by fitting some variable selection methods

Usage

lm.compare.method(X, Y, intercept, method.names = NULL, log.level = NULL)

Arguments

X	covariates (n times p matrix, n: number of entries, p: number of covariates)
Y	response (vector with n entries)
intercept	TRUE to fit the data with an intercept, FALSE to fit the data without an intercept
method.names	vector of method names to be used for fitting. Choose among "lasso", "elastic", "relaxo", "mcp" and "scad". Default is to fit the data using all methods listed above
log.level	log level to set. Default is NULL, which means no change in log level. See the function CSUV::set.log.level for more details

Value

estimated coefficients in a form of matrix. Each row corresponds to a method and each column corresponds to a covariate, with the first column corresponds to the intercept

Examples

X = matrix(rnorm(1000), nrow = 100)
Y = rowSums(X[,1:3])+rnorm(100)
compare.mod = lm.compare.method(X, Y, intercept = FALSE)
print(compare.mod)

CSUV documentation built on Oct. 23, 2020, 5:49 p.m.