cv.clogitLasso: Cross-validation of 'clogitLasso' object

In clogitLasso: Sparse Conditional Logistic Regression for Matched Studies

Description

Cross-validation of clogitLasso object

Usage

cv.clogitLasso(objclogitLasso, K = 10, gpe = NULL)

Arguments

objclogitLasso	An objet of type clogitLasso
K	The number of folds used in cross validation
gpe	A list of group defined by the user.

Value

An object of type cv.clogitLasso with the following components:

lambda	Vector of regularisation parameter
mean_cv	vector of mean deviances for each value of the regularisation parameter
beta	Vector of estimated coefficients with optimal regularisation parameter
lambdaopt	Optimal regularisation parameter

Author(s)

Marta Avalos, Helene Pouyes, Marius Kwemou and Binbin Xu

References

Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015). Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm. BMC bioinformatics, 16(6), S1. doi: 10.1186/1471-2105-16-S6-S1.

Examples

## Not run: 
# generate data
y <- rep(c(1,0), 100)
X <- matrix (rnorm(20000, 0, 1), ncol = 100) # pure noise
strata <- sort(rep(1:100, 2))

# fitLasso <- clogitLasso(X,y,strata,log=TRUE)

# Cross validation
cv.fit <- cv.clogitLasso(fitLasso)

## End(Not run)

clogitLasso documentation built on May 2, 2019, 1:10 p.m.