superpc.predict {superpc} | R Documentation |
Form principal components predictor from a trained superpc object
Description
Computes supervised principal components, using scores from "object"
Usage
superpc.predict(object, data, newdata, threshold, n.components = 3, prediction.type = c("continuous", "discrete", "nonzero"), n.class = 2)
Arguments
object |
Obect returned by superpc.train |
data |
List of training data, of form described in superpc.train documentation, |
newdata |
List of test data; same form as training data |
threshold |
Threshold for scores: features with abs(score)>threshold
are retained. |
n.components |
Number of principal components to compute.
Should be 1,2 or 3. |
prediction.type |
"continuous" for raw principal component(s);
"discrete" for principal component categorized in equal bins;
"nonzero" for indices of features that pass the threshold |
n.class |
Number of classes into which predictor is binned
(for prediction.type="discrete" |
Value
v.pred |
Supervised principal componients predictor |
u |
U matrix from svd of feature matrix x |
d |
singual values from svd of feature matrix x |
which.features |
Indices of features exceeding threshold |
n.components |
Number of supervised principal components requested |
call |
calling sequence |
Author(s)
Eric Bair and Robert Tibshirani
Examples
set.seed(332)
#generate some data
x<-matrix(rnorm(1000*20),ncol=20)
y<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
ytest<-10+svd(x[1:30,])$v[,1]+ .1*rnorm(20)
censoring.status<- sample(c(rep(1,17),rep(0,3)))
censoring.status.test<- sample(c(rep(1,17),rep(0,3)))
featurenames <- paste("feature",as.character(1:1000),sep="")
data<-list(x=x,y=y, censoring.status=censoring.status, featurenames=featurenames)
data.test<-list(x=x,y=ytest, censoring.status=censoring.status.test, featurenames= featurenames)
a<- superpc.train(data, type="survival")
fit<- superpc.predict(a, data, data.test, threshold=1.0, n.components=1)
plot(fit$v.pred,ytest)
[Package
superpc version 1.05
Index]