superpc: superpc.rainbowplot – R documentation

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superpc.rainbowplot

Make rainbow plot of superpc and compeiting predictors

Description

Makes a heatmap display of outcome predictions from superpc, along with expected survival time, and values of competing predictors.

Usage

superpc.rainbowplot(data, 
                        pred, 
                        sample.labels,
                        competing.predictors,
                        call.win.metafile=FALSE)

Arguments

`data`	List of (test) data, of form described in superpc.train documentation
`pred`	Superpc score from superpc.predict or superpc.predict.red
`sample.labels`	Vector of sample labels of test data
`competing.predictors`	List of competing predictors to be plotted
`call.win.metafile`	Used only by Excel interface call to function

Details

Any censored survival times are estimated by E(T|T > C), where $C$ is the observed censoring time and the Kaplan-Meier estimate from the training set is used to estimate the expectation.

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="")
competing.predictors.test <- list(pred1=rnorm(30), 
                                  pred2=as.factor(sample(c(1,2),
                                                  replace=TRUE,
                                                  size=30)))
                                                  
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)
sample.labels <- paste("te", as.character(1:20), sep="")

a <- superpc.train(data, type="survival")
pred <- superpc.predict(a, 
                        data, 
                        data.test, 
                        threshold=.25, 
                        n.components=1)$v.pred
superpc.rainbowplot(data, 
                    pred, 
                    sample.labels, 
                    competing.predictors=competing.predictors.test)

superpc

Supervised Principal Components

v1.12

GPL (>= 3) | file LICENSE

Authors

Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]

Initial release

2020-10-19