This function computes the Principal Components Regression (PCR) fit.
pcr( X, y, scale = TRUE, m = min(ncol(X), nrow(X) - 1), eps = 1e-06, supervised = FALSE )
X | matrix of predictor observations. |
---|---|
y | vector of response observations. The length of |
scale | Should the predictor variables be scaled to unit variance?
Default is |
m | maximal number of principal components. Default is
|
eps | precision. Eigenvalues of the correlation matrix of |
supervised | Should the principal components be sorted by decreasing squared correlation to the response? Default is FALSE. |
matrix of regression coefficients, including the
coefficients of the null model, i.e. the constant model mean(y)
.
vector of intercepts, including the intercept of the null
model, i.e. the constant model mean(y)
.
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.
Nicole Kraemer