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superpc

Supervised Principal Components

Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. 'Superpc' is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.

Functions (15)

superpc.cv

Cross-validation for supervised principal components

superpc.decorrelate

Decorrelate features with respect to competing predictors

superpc.fit.to.outcome

Fit predictive model using outcome of supervised principal components

superpc.listfeatures

Return a list of the important predictors

superpc.lrtest.curv

Compute values of likelihood ratio test from supervised principal components fit

superpc.news

Display the superpc Package News

superpc.plot.lrtest

Plot likelhiood ratio test statistics

superpc.plotcv

Plot output from superpc.cv

superpc.plotred.lrtest

Plot likelihood ratio test statistics from supervised principal components predictor

superpc.predict

Form principal components predictor from a trained superpc object

superpc.predict.red

Feature selection for supervised principal components

superpc.predict.red.cv

Cross-validation of feature selection for supervised principal components

superpc.predictionplot

Plot outcome predictions from superpc

superpc.rainbowplot

Make rainbow plot of superpc and compeiting predictors

superpc.train

Prediction by supervised principal components

superpc

Supervised Principal Components

v1.12
GPL (>= 3) | file LICENSE
Authors
Eric Bair [aut], Jean-Eudes Dazard [cre, ctb], Rob Tibshirani [ctb]
Initial release
2020-10-19

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