cv.glmmLasso: Cross Validation for glmmLasso package

In lmmen: Linear Mixed Model Elastic Net

Description

Cross Validation for glmmLasso package as shown in example xxx

Usage

cv.glmmLasso(dat, form.fixed = NULL, form.rnd = NULL, lambda = seq(500, 0,
  by = -5), family = stats::gaussian(link = "identity"))

Arguments

dat	data.frame, containing y,X,Z and subject variables
form.fixed	formaula, fixed param formula, Default: NULL
form.rnd	list, named list containing random effect formula, Default: NULL
lambda	numeric, vector containing lasso penalty levels, Default: seq(500, 0, by = -5)
family	family, family function that defines the distribution link of the glmm, Default: gaussian(link = "identity")

Value

list of a fitted glmmLasso object and the cv BIC path

References

A. Groll and G. Tutz. Variable selection for generalized linear mixed models by L1-penalized estimation. Statistics and Computing, pages 1<e2><80><93>18, 2014.

cv function is the generalized form of last example glmmLasso package demo file

See Also

glmmLasso

Examples

## Not run: cv.glmmLasso(initialize_example(seed=1))

Example output

Loading required package: lmmlasso
Loading required package: emulator
Loading required package: mvtnorm
Loading required package: miscTools
Loading required package: penalized
Loading required package: survival
Welcome to penalized. For extended examples, see vignette("penalized").
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This is a test release of the package 'lmmlasso'. If you have any questions or problems, do not hesitate to contact the author.
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lmmen documentation built on Aug. 15, 2017, 1:02 a.m.