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POCRE (version 0.6.0)

Penalized Orthogonal-Components Regression

Description

Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.

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Version

Install

install.packages('POCRE')

Monthly Downloads

232

Version

0.6.0

License

GPL-2

Maintainer

Dabao Zhang

Last Published

March 16th, 2022

Functions in POCRE (0.6.0)

selectmodel

Select the Optimal Model
sipocre

Penalized Orthogonal-Components Regression (POCRE) with Significance Inference
pocrepath

Build a POCRE Path for Different Values of Tuning Parameters
simpoi

A Set of Simulated Poisson Data.
simdata

A Set of Simulated Data with Single Gaussian Response Variable
plot.pocre

Visualization of a pocre Object
pocrescreen

Screen Variables Using Penalized Orthogonal-Components Regression (POCRE)
cvpocre

Use k-Fold Cross-Validation to Choose the Tuning Parameter for POCRE
pocre

Penalized Orthogonal-Components Regression (POCRE)
sim5ydata

A Set of Simulated Data with Multiple Response Variables
gps

Screen Variables for Generalized Linear Models via Generalized POCRE
plot.pocrepath

Visulaization of a POCRE Path
simbin

A Set of Simulated Binomial Data.