Rfast2: pcr – R documentation

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pcr

Principal components regression

Principal components regression.

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

`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.

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

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).

Michail Tsagris

R implementation and documentation: Michail Tsagris mtsagris@uoc.gr.

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

y <- as.vector(iris[, 1])
x <- as.matrix(iris[, 2:4])
mod1 <- pcr(y, x, 1)
mod2 <- pcr(y, x, 2)
mod <- pcr(y, x, k = 1:3)  ## all results at once

A Collection of Efficient and Extremely Fast R Functions II

v0.0.9

GPL (>= 2.0)

Authors

Manos Papadakis, Michail Tsagris, Stefanos Fafalios and Marios Dimitriadis.

Initial release

2021-03-21