AIM (version 1.01)

index.prediction: Predict index based on fitted AIM

Description

Compute the index for new observations using output from lm.main, lm.interaction, logistic.main, logistic.interaction, cox.main and cox.interaction.

Usage

index.prediction(res, x)

Arguments

res

list "res" term from the outputs in lm.main, lm.interaction, logistic.main, logistic.interaction, cox.main and cox.interaction

x

New covariate matrix

Value

index.prediction returns score which is the index for new observations with covariate matrix "x".

Details

index.prediction computes the new index for given observations based on the fitted AIM

References

Lu Tian and Robert Tibshirani (2010) Adaptive index models for marker-based risk stratification. Tech Report. Available at http://www-stat.stanford.edu/~tibs/AIM.

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)
trt=rbinom(n,1, 0.5)
beta=1
prb=1/(1+exp(trt-beta*trt*z-0.5))
y=rbinom(n,1,prb)

## fit the interaction logistic AIM model 
a=logistic.interaction(x, trt, y, nsteps=10)
 
## examine the model sequence 
print(a)

## compute the index based on the 2nd model of the sequence, using data x 
z.prd=index.prediction(a$res[[2]],x)

## compute the index based on the 2nd model of the sequence using new data xx, and compare the result with the true index
nn=10
xx=matrix(rnorm(nn*p), nn, p)
zz=(xx[,1]<0.2)+(xx[,5]>0.2)
zz.prd=index.prediction(a$res[[2]],xx) 
cbind(zz, zz.prd)