Return a list of the important predictors
Return a list of the important predictor
superpc.listfeatures(data,
train.obj,
fit.red,
fitred.cv=NULL,
num.features=NULL,
component.number=1)data |
Data object |
train.obj |
Object returned by superpc.train |
fit.red |
Object returned by superpc.predict.red, applied to training set |
fitred.cv |
(Optional) object returned by superpc.predict.red.cv |
num.features |
Number of features to list. Default is all features. |
component.number |
Number of principal component (1,2, or 3) used to determine feature importance scores |
Returns matrix of features and their importance scores, in order of decreasing absolute value of importance score. The importance score is the correlation of the reduced predictor and the full supervised PC predictor. It also lists the raw score- for survival data, this is the Cox score for that feature; for regression, it is the standardized regression coefficient. If fitred.cv is supplied, the function also reports the average rank of the gene in the cross-validation folds, and the proportion of times that the gene is chosen (at the given threshold) in the cross-validation folds.
"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")
fit.red <- superpc.predict.red(a,
data,
data.test,
.6)
superpc.listfeatures(data,
a,
fit.red,
num.features=10)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.