cv.clogitLasso: Plot coefficients from a 'clogitLasso' object

Description Usage Arguments Value Author(s) References Examples

View source: R/cv.clogitLasso.R

Description

Plot the parameter profile associated clogitLasso object

Usage

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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)

Marius Kwemou and Marta Avalos

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), 1.

Examples

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## 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 30, 2017, 2:19 a.m.