Matrix of training set feature values, with
genes in the rows, samples in the columns
adjusting.predictors
List of training set predictors to be used for adjustment
xtest
Optional matrix of test set feature values, to be adjusted
in the same way as the training set
adjusting.predictors.test
Optional list of test set predictors to be used for adjustment
Details
pamr.decorrelate
Does a least squares regression of each row of x on the adjusting
predictors, and returns the residuals. If xtest is provided, it also
returns the adjusted version of xtest, using the
training set least squares regression model for adjustment
Value
A list with components
x.adj
Adjusted x matrix
xtest.adj
Adjusted xtest matrix, if xtest we provided
Author(s)
Trevor Hastie,Robert Tibshirani, Balasubramanian Narasimhan, and Gilbert Chu
References
Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu
Diagnosis of multiple cancer types by shrunken centroids of gene expression
PNAS 99: 6567-6572. Available at www.pnas.org
Examples
#generate some data
set.seed(120)
x<-matrix(rnorm(1000*20),ncol=20)
y<-c(rep(1,10),rep(2,10))
adjusting.predictors=list(pred1=rnorm(20), pred2=as.factor(sample(c(1,2),replace
=TRUE,size=20)))
xtest=matrix(rnorm(1000*10),ncol=10)
adjusting.predictors.test=list(pred1=rnorm(10), pred2=as.factor(sample(c(1,2),replace
=TRUE,size=10)))
# decorrelate training x wrt adjusting predictors
x.adj=pamr.decorrelate(x,adjusting.predictors)$x.adj
# train classifier with adjusted x
d=list(x=x.adj,y=y)
a<-pamr.train(d)
# decorrelate training and test x wrt adjusting predictors, then make
#predictions for test set
temp <- pamr.decorrelate(x,adjusting.predictors, xtest=xtest,
adjusting.predictors.test=adjusting.predictors.test)
d=list(x=temp$x.adj,y=y)
a<-pamr.train(d)
aa<-pamr.predict(a,temp$xtest.adj, threshold=.5)