superpc.fit.to.outcome function

Fit predictive model using outcome of supervised principal components

Fit predictive model using outcome of supervised principal components, via either coxph (for surival data) or lm (for regression data)

superpc.fit.to.outcome(fit, 
                           data.test, 
                           score, 
                           competing.predictors=NULL, 
                           print=TRUE, 
                           iter.max=5)

Arguments

• fit: Object returned by superpc.train.
• data.test: Data object for prediction. Same form as data object documented in superpc.train.
• score: Supervised principal component score, from superpc.predict.
• competing.predictors: Optional - a list of competing predictors to be included in the model.
• print: Should a summary of the fit be printed? Default TRUE.
• iter.max: Max number of iterations used in predictive model fit. Default 5. Currently only relevant for Cox PH model.

Returns

Returns summary of coxph or lm fit.

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, 
                       prediction.type="continuous")
superpc.fit.to.outcome(a, 
                       data, 
                       fit$v.pred)