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.

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

Author

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

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. doi: 10.18637/jss.v062.i05

Examples

if (FALSE) {
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"))
}