superpc.predict function

Form principal components predictor from a trained superpc object

Computes supervised principal components, using scores from "object"

superpc.predict(object, 
                    data, 
                    newdata, 
                    threshold, 
                    n.components=3, 
                    prediction.type=c("continuous","discrete","nonzero"), 
                    n.class=2)

Arguments

• object: Obect returned by superpc.train
• data: List of training data, of form described in superpc.train documentation,
• newdata: 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.
• prediction.type: "continuous" for raw principal component(s); "discrete" for principal component categorized in equal bins; "nonzero" for indices of features that pass the threshold
• n.class: Number of classes into which predictor is binned (for prediction.type="discrete"

Returns

• v.pred: Supervised principal componients predictor

• u: U matrix from svd of feature matrix x

• d: singual values from svd of feature matrix x

• which.features: Indices of features exceeding threshold

• n.components: Number of supervised principal components requested

• call: calling sequence

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")
fit <- superpc.predict(a, 
                       data, 
                       data.test, 
                       threshold=1.0, 
                       n.components=1)
plot(fit$v.pred, ytest)