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)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 |
"Eric Bair, Ph.D."
"Jean-Eudes Dazard, Ph.D."
"Rob Tibshirani, Ph.D."
Maintainer: "Jean-Eudes Dazard, Ph.D."
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.