Description Details Author(s) References See Also Examples
Compute the covariance test significance testing in adaptive linear modelling. Can be used with LARS (lasso) for linear models, elastic net, binomial and Cox survival model. This package should be considered EXPERIMENTAL. The background paper is not yet published and rigorous theory does not yet exist for the logistic and Cox models.
Package: | covTest |
Type: | Package |
Version: | 1.0 |
Date: | 2013-01-08 |
License: | GPL-2 |
Very simple to use. Takes output from one of lars
, lars.en
,lars.glm
and compute covariance test and p-values.
Requires lars
and glmpath
packages. lars.en
and lars.glm
are included in this package.
Functions are:
covTest
lars.en
lars.glm
predict.lars.en
predict.lars.glm
Rob Tibshirani tibs@stanford.edu
A significance test for the lasso (2013). Lockhart, R., Taylor, J., Tibshirani (Ryan) and Tibshirani (Robert)
covTest, lars.glm, lars.en
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | x=matrix(rnorm(100*10),ncol=10)
x=scale(x,TRUE,TRUE)/sqrt(99)
#Gaussian
beta=c(4,rep(0,9))
y=x%*%beta+.4*rnorm(100)
a=lars(x,y)
covTest(a,x,y)
#Elastic net
a=lars.en(x,y,lambda2=1)
covTest(a,x,y)
#logistic
y=1*(y>0)
a=lars.glm(x,y,family="binomial")
covTest(a,x,y)
# Cox model
#y=6*x[,2]+rnorm(100)+10
#status=sample(c(0,1),size=length(y),replace=TRUE)
#a=lars.glm(x,y,status=status,family="cox")
#covTest(a,x,y,status=status)
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