p.pca {PLORN}

Visualize predictors using principal coordinate analysis

Description

Visualize predictors using principal coordinate analysis

Usage

p.pca(x, y, method = "linear", lower.thr = 0, n.pred = ncol(x), size = 1)

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 indicated in a PCA plot (default: 0).

n.pred

The number of candidate predictors for PLORN model to be indicated in a PCA plot (default: ncol(x)).

size

The size of symbols in a PCA plot (default: 1).

Value

A PCA plot

Author(s)

Takahiko Koizumi

Examples

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