Compute the covariance test for adaptive linear modelling

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Description

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.

Details

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

Author(s)

Rob Tibshirani tibs@stanford.edu

References

A significance test for the lasso (2013). Lockhart, R., Taylor, J., Tibshirani (Ryan) and Tibshirani (Robert)

See Also

covTest, lars.glm, lars.en

Examples

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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)