Compositional (version 1.0)

pcr: Principal components regression

Description

Principal components regression.

Usage

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.

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).

Details

The principal components of the cross product of the independent variables are obtained and classical regression is performed. This is use din the function alfa.pcr.

References

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

See Also

pcr.tune, alfa.pcr, glm.pcr

Examples

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