superpc.predictionplot: Plot outcome predictions from superpc

In superpc: Supervised Principal Components

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)

superpc documentation built on Oct. 24, 2020, 1:07 a.m.