p.rank: Visualize R-squared value distribution in...

In PLORN: Prediction with Less Overfitting and Robust to Noise

Visualize R-squared value distribution in predictor-environment interaction

Description

Visualize R-squared value distribution in predictor-environment interaction

Usage

p.rank(
  x,
  y,
  method = "linear",
  lower.thr = 0,
  n.pred = ncol(x),
  upper.xlim = ncol(x)
)

Arguments

x	A data matrix (row: 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.
lower.thr	The lower threshold of R-squared value to be included in PLORN model (default: 0).
n.pred	The number of predictors to be included in PLORN model (default: ncol(x)).
upper.xlim	The upper limitation of x axis (i.e., the number of predictors) in the resulted figure (default: ncol(x)).

Value

A rank order plot

Author(s)

Takahiko Koizumi

Examples

data(Pinus)
train <- p.clean(Pinus$train)
target <- Pinus$target
train <- p.sort(train, target)
p.rank(train, target)

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