p.sort: Sort and truncate predictors according to the strength of...

In PLORN: Prediction with Less Overfitting and Robust to Noise

Sort and truncate predictors according to the strength of predictor-environment interaction

Description

Sort and truncate predictors according to the strength of predictor-environment interaction

Usage

p.sort(x, y, method = "linear", n.pred = ncol(x), trunc = 1)

Arguments

x	A data matrix (raw: samples, col: predictors).
y	A vector of an environment in which the samples were collected.
method	A string to specify the method of regression for calculating R-squared values. "linear" (default), "quadratic" or "cubic" regression model can be specified.
n.pred	The number of predictors to be included in PLORN model (default: ncol(x)).
trunc	a threshold to be truncated (default: 1).

Value

A data matrix (raw: samples, col: sorted predictors)

Author(s)

Takahiko Koizumi

Examples

data(Pinus)
train <- p.clean(Pinus$train)
target <- Pinus$target
cor(target, train[, 1])

train <- p.sort(train, target, trunc = 0.5)
cor(target, train[, 1])

PLORN documentation built on March 21, 2022, 9:11 a.m.