pcr: Principal Components Regression

In plsdof: Degrees of Freedom and Statistical Inference for Partial Least Squares Regression

Principal Components Regression

Description

This function computes the Principal Components Regression (PCR) fit.

Usage

pcr(
  X,
  y,
  scale = TRUE,
  m = min(ncol(X), nrow(X) - 1),
  eps = 1e-06,
  supervised = FALSE
)

Arguments

X	matrix of predictor observations.
y	vector of response observations. The length of y is the same as the number of rows of X.
scale	Should the predictor variables be scaled to unit variance? Default is TRUE.
m	maximal number of principal components. Default is m=min(ncol(X),nrow(X)-1).
eps	precision. Eigenvalues of the correlation matrix of X that are smaller than eps are set to 0. The default value is eps=10^{-6}.
supervised	Should the principal components be sorted by decreasing squared correlation to the response? Default is FALSE.

Details

The function first scales all predictor variables to unit variance, and then computes the PCR fit for all components. Is supervised=TRUE, we sort the principal correlation according to the squared correlation to the response.

Value

coefficients	matrix of regression coefficients, including the coefficients of the null model, i.e. the constant model mean(y).
intercept	vector of intercepts, including the intercept of the null model, i.e. the constant model mean(y).

Author(s)

Nicole Kraemer

See Also

pcr.cv, pls.cv

Examples

n<-50 # number of observations
p<-15 # number of variables
X<-matrix(rnorm(n*p),ncol=p)
y<-rnorm(n)

my.pcr<-pcr(X,y,m=10)

plsdof documentation built on Dec. 1, 2022, 1:13 a.m.