Principal components regression

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Description

Principal components regression.

Usage

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

Arguments

y

A real values vector.

x

The predictor variable(s), they have to be continuous.

k

The number of principal components to use.

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. This is used in the function alfa.pcr.

Value

A list including:

beta

The beta coefficients.

parameters

The beta coefficients and their standard eror.

mse

The MSE of the linear regression, if xnew is NULL, becuase it needs the fitted values.

adj.rsq

The value of the adusted R^2 if xnew is NULL.

per

The percentage of variance of the cross product of the independent variables explained by the k components.

est

The fitted or the predicted values (if xnew is not NULL).

Author(s)

Michail Tsagris

R implementation and documentation: Michail Tsagris <mtsagris@yahoo.gr> and Giorgos Athineou <athineou@csd.uoc.gr>

References

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

See Also

pcr.tune, alfa.pcr, glm.pcr

Examples

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library(MASS)
x <- fgl[, 2:9]
y <- fgl[, 1]
mod1 <- pcr(y, x, 1)
mod2 <- pcr(y, x, 2)

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