Description Usage Arguments Value Author(s) References Examples
View source: R/superpc.fit.to.outcome.R
Fit predictive model using outcome of supervised principal components, via either coxph (for surival data) or lm (for regression data)
1 2 3 4 5 6 | superpc.fit.to.outcome(fit,
data.test,
score,
competing.predictors=NULL,
print=TRUE,
iter.max=5)
|
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 summary of coxph or lm fit.
"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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | 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)
|
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