pcr: Principal components regression

In Rfast2: A Collection of Efficient and Extremely Fast R Functions II

Principal components regression

Description

Principal components regression.

Usage

pcr(y, x, k = 1, xnew = NULL)

Arguments

y	A real values vector.
x	A matrix with the predictor variable(s), they have to be continuous.
k	The number of principal components to use. This can be a single number or a vector starting from 1. In the second case you get results for the sequence of principal components.
xnew	If you have new data use it, otherwise leave it NULL.

Details

The principal components of the cross product of the independent variables are obtained and classical regression is performed.

Value

A list including:

be	The beta coefficients of the predictor variables computed via the principcal components.
per	The percentage of variance of the cross product of the independent variables explained by the k components.
vec	The principal components, the loadings.
est	The fitted or the predicted values (if xnew is not NULL).

Author(s)

Michail Tsagris.

R implementation and documentation: Michail Tsagris [e-mail].

References

Jolliffe I.T. (2002). Principal Component Analysis.

See Also

pca

Examples

x <- as.matrix(iris[, 2:4])
y <- as.vector(iris[, 1])
mod <- pcr(y, x, k = 1:3) 

Rfast2 documentation built on June 8, 2025, 11:46 a.m.