superpc.predictionplot function

Plot outcome predictions from superpc

Plots outcome predictions from superpc

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

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.

Author(s)

• "Eric Bair, Ph.D."
• "Jean-Eudes Dazard, Ph.D."
• "Rob Tibshirani, Ph.D."

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

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)