os.pca: Visualize predictors using principal coordinate analysis

In OmicSense: Biosensor Development using Omics Data

Visualize predictors using principal coordinate analysis

Description

Visualize predictors using principal coordinate analysis

Usage

os.pca(x, y, method = "linear", thresh = 0, n.pred = ncol(x), size = 1)

Arguments

x	A data matrix (row: samples, col: predictors).
y	A vector of target value.
method	A string to specify the regression function for calculating R-squared values. "linear" (default), "quadratic" or "cubic" function can be specified.
thresh	The lower threshold of R-squared value to be indicated in a PCA plot (default: 0).
n.pred	The number of candidate predictors for prediction 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 <- os.clean(Pinus$train)
target <- Pinus$target
os.pca(train, target)

OmicSense documentation built on Sept. 23, 2023, 1:06 a.m.