plorn: Construct and apply the PLORN model with your own data

In PLORN: Prediction with Less Overfitting and Robust to Noise

Construct and apply the PLORN model with your own data

Description

Construct and apply the PLORN model with your own data

Usage

plorn(x, y, newx = x, method = "linear", lower.thr = 0, n.pred = 0)

Arguments

x	A data matrix (row: samples, col: predictors).
y	A vector of an environment in which the samples were collected.
newx	A data matrix (row: samples, col: predictors).
method	A string to specify the method of regression for calculating R-squared values. "linear" (default), "quadratic" or "cubic" regression model can be specified.
lower.thr	The lower threshold of R-squared value to be used in PLORN model (default: 0).
n.pred	The number of candidate predictors to be used in PLORN model (default: 30).

Value

A vector of the environment in which the samples of newx were collected

Author(s)

Takahiko Koizumi

Examples

data(Pinus)
train <- p.clean(Pinus$train)
test <- Pinus$test
test <- test[, colnames(train)]
target <- Pinus$target
cor(target, plorn(train, target, newx = test, method = "cubic"))

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