covTest-package: Compute the covariance test for adaptive linear modelling

Description Details Author(s) References See Also Examples

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

 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)


covTest documentation built on May 19, 2017, 6:48 p.m.
Search within the covTest package
Search all R packages, documentation and source code

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

Please suggest features or report bugs in the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.