p.opt {PLORN}

Estimate the optimal number of predictors to construct PLORN model

Description

Estimate the optimal number of predictors to construct PLORN model

Usage

p.opt(x, y, range = 5:50, method = "linear", rep = 1)

Arguments

x

A data matrix (row: samples, col: predictors).

y

A vector of an environment in which the samples were collected.

range

A sequence of numbers of predictors to be tested for MAE calculation (default: 5:50).

method

A string to specify the method of regression for calculating R-squared values. "linear" (default), "quadratic" or "cubic" regression model can be specified.

rep

The number of replications for each case set by range (default: 1).

Value

A sample-MAE curve

Author(s)

Takahiko Koizumi

Examples

data(Pinus)
train <- p.clean(Pinus$train)
target <- Pinus$target
p.opt(train[1:10, ], target[1:10], range = 5:15)