R Graphical Manual

Browse All

Last data update: 2014.03.03

R: Cross-validation in main effect linear AIM

cv.lm.main

R Documentation

Cross-validation in main effect linear AIM

Description

Cross-validation for selecting the number of binary rules in the main effect linear AIM

Usage

cv.lm.main(x, y, K.cv=5, num.replicate=1, nsteps, mincut=0.1, backfit=F, maxnumcut=1, dirp=0)

Arguments

`x`	n by p matrix. The covariate matrix
`y`	n vector. The continuous response variable
`K.cv`	K.cv-fold cross validation
`num.replicate`	number of independent replications of K-fold cross validations.
`nsteps`	the maximum number of binary rules to be included in the index
`mincut`	the minimum cutting proportion for the binary rule at either end. It typically is between 0 and 0.2.
`backfit`	T/F. Whether the existing split points are adjusted after including new a binary rule
`maxnumcut`	the maximum number of binary splits per predictor
`dirp`	p vector. The given direction of the binary split for each of the p predictors. 0 represents "no pre-given direction"; 1 represents "(x>cut)"; -1 represents "(x<cut)". Alternatively, "dirp=0" represents that there is no pre-given direction for any of the predictor.

Details

cv.lm.main implements the K-fold cross-validation for the main effect linear AIM. It estimates the score test statistics in the test set for testing the association between the continuous response and index constructed using training data. It also provides pre-validated fits for each observation and the pre-validated score test statistics. The output can be used to select the optimal number of binary rules.

Value

cv.lm.main returns

`kmax`	the optimal number of binary rules based the cross-validation
`meanscore`	nsteps-vector. The cross-validated score test statistics (significant at 0.05, if greater than 1.96) for the association between survival time and index.
`pvfit.score`	nsteps-vector. The pre-validated score test statistics (significant at 0.05, if greater than 1.96) for the association between survival time and index.
`preval`	nsteps by n matrix. Pre-validated fits for individual observation

References

L Tian and R Tibshirani Adaptive index models for marker-based risk stratification, Tech Report, available at http://www-stat.stanford.edu/~tibs/AIM.

R Tibshirani and B Efron, Pre-validation and inference in microarrays, Statist. Appl. Genet. Mol. Biol., 1:1-18, 2002.

Author(s)

Lu Tian and Robert Tibshirani

Examples

## generate data
set.seed(1)

n=400
p=10
x=matrix(rnorm(n*p), n, p)
z=(x[,1]<0.2)+(x[,5]>0.2)
beta=1
y=beta*z+rnorm(n)


## cross-validate the linear main effects AIM
a=cv.lm.main(x, y,  nsteps=10, K.cv=5, num.replicate=3)
 
## examine score test statistics in the test set 
par(mfrow=c(1,2))
plot(a$meanscore, type="l")
plot(a$pvfit.score, type="l")


## construct the index with the optimal number of binary rules 
k.opt=a$kmax
a=lm.main(x, y, nsteps=k.opt)
print(a)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(AIM)
Loading required package: survival
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/AIM/cv.lm.main.Rd_%03d_medium.png", width=480, height=480)
> ### Name: cv.lm.main
> ### Title: Cross-validation in main effect linear AIM
> ### Aliases: cv.lm.main
> 
> ### ** Examples
> 
> ## generate data
> set.seed(1)
> 
> n=400
> p=10
> x=matrix(rnorm(n*p), n, p)
> z=(x[,1]<0.2)+(x[,5]>0.2)
> beta=1
> y=beta*z+rnorm(n)
> 
> 
> ## cross-validate the linear main effects AIM
> a=cv.lm.main(x, y,  nsteps=10, K.cv=5, num.replicate=3)
>  
> ## examine score test statistics in the test set 
> par(mfrow=c(1,2))
> plot(a$meanscore, type="l")
> plot(a$pvfit.score, type="l")
> 
> 
> ## construct the index with the optimal number of binary rules 
> k.opt=a$kmax
> a=lm.main(x, y, nsteps=k.opt)
> print(a)
$res
$res[[1]]
     jmax      cutp maxdir    maxsc
[1,]    5 0.1841851      1 9.592823

$res[[2]]
     jmax      cutp maxdir     maxsc
[1,]    5 0.1841851      1  9.592823
[2,]    1 0.2075383     -1 12.484655


$maxsc
[1]  9.592823 12.484655

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>