cv.lmmlasso: Cross Validation for lmmlasso package

In lmmen: Linear Mixed Model Elastic Net

Description

Cross Validation for lmmlasso package as shown in example xxx

Usage

cv.lmmlasso(dat, lambda = seq(0, 500, 5), ...)

Arguments

dat	matrix, containing y,X,Z and subject variables
lambda	numeric, path of positive regularization parameter, Default: seq(0, 500, 5)
...	parameters to pass to lmmlasso

Value

lmmlasso fit object

References

J. Schelldorfer, P. Buhlmann, and S. Van de Geer. Estimation for high-dimensional linear mixed-effects models using L1-penalization. Scandinavian Journal of Statistics, 38(2):197<e2><80><93>214, 2011.

See Also

lmmlasso

Examples

 ## Not run: cv.lmmlasso(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.