function to highlight the path of a pre-specified set of variables within the coefficient path

Creates several plots showing the coefficient path for the final model of a cv.glmnet fit and highlights the path of a pre-specified set of variables within the coefficient path.

Usage

Plot.coef.glmnet(cvfit, betas)

Arguments

cvfit: an object of class "cv.glmnet" as returned by the function cv.glmnet.
betas: a vector of names of variables; must be a subset of rownames(coef(cvfit)).

Value

a list of four objects

stable: a vector giving the positions of the estimated stable variables
lambda: the penalization parameter used for the stability selection
lpos: the position of the penalization parameter in the regularization path
error: the desired type I error level w.r.t. to the chosen type I error rate
type: the type I error rate

References

Sill M., Hielscher T., Becker N. and Zucknick M. (2014), c060: Extended Inference with Lasso and Elastic-Net Regularized Cox and Generalized Linear Models, Journal of Statistical Software, Volume 62(5), pages 1–22. https://doi.org/10.18637/jss.v062.i05.

Author

Manuela Zucknick \ m.zucknick@dkfz-heidelberg.de

Examples


if (FALSE) { # \dontrun{
set.seed(1010)
n=1000;p=100
nzc=trunc(p/10)
x=matrix(rnorm(n*p),n,p)
beta=rnorm(nzc)
fx= x[,seq(nzc)] %*% beta
eps=rnorm(n)*5
y=drop(fx+eps)
px=exp(fx)
px=px/(1+px)
ly=rbinom(n=length(px),prob=px,size=1)
set.seed(1011)
cvob1=cv.glmnet(x,y)
Plot.coef.glmnet(cvob1, c("V1","V100"))
} # }