superpc.predictionplot {superpc}

Plot outcome predictions from superpc

Description

Plots outcome predictions from superpc

Usage

    superpc.predictionplot(train.obj, 
                           data, 
                           data.test,
                           threshold, 
                           n.components=3,
                           n.class=2, 
                           shrinkage=NULL, 
                           call.win.metafile=FALSE)

Arguments

train.obj

Object returned by superpc.train

data

List of training data, of form described in superpc.train documentation

data.test

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.

n.class

Number of classes for survival stratification. Only applicable for survival data. Default 2.

shrinkage

Shrinkage to be applied to feature loadings. Default is NULL, meaning no shrinkage

call.win.metafile

Used only by Excel interface call to function

Author(s)

• "Eric Bair, Ph.D."

• "Jean-Eudes Dazard, Ph.D."

• "Rob Tibshirani, Ph.D."

Maintainer: "Jean-Eudes Dazard, Ph.D."

References

• E. Bair and R. Tibshirani (2004). "Semi-supervised methods to predict patient survival from gene expression data." PLoS Biol, 2(4):e108.

• E. Bair, T. Hastie, D. Paul, and R. Tibshirani (2006). "Prediction by supervised principal components." J. Am. Stat. Assoc., 101(473):119-137.

Examples

set.seed(332)

#generate some data
x <- matrix(rnorm(50*30), ncol=30)
y <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
ytest <- 10 + svd(x[1:50,])$v[,1] + .1*rnorm(30)
censoring.status <- sample(c(rep(1,20), rep(0,10)))
censoring.status.test <- sample(c(rep(1,20), rep(0,10)))

featurenames <- paste("feature", as.character(1:50), 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")
superpc.predictionplot(a, 
                       data, 
                       data.test, 
                       threshold=1)