pcreg {bestglm} | R Documentation |
Principal Component and Partial Least Squares Regression
Description
Regression using the principal components or latent variables as inputs. The best model is selected using components 1, 2, ..., r, where r, the number of components to use is determined by the AIC or BIC.
Usage
pcreg(Xy, scale = TRUE, method = c("PC", "LV"), ic = c("BIC", "AIC"))
Arguments
Xy |
dataframe with variable names in columns |
scale |
Whether or not to scale. Default is TRUE. |
method |
either principal components, "PC", or partial least squares latent variables, "LV" |
ic |
"BIC" or "AIC" |
Value
An S3 class list "pcreg" with components
lmfit |
lm model |
PLSFit |
column sd |
Z |
matrix of principal components or latent vector |
method |
'pcr' or 'pls' |
Author(s)
A. I. McLeod
See Also
predict.pcreg
,
summary.pcreg
,
plot.pcreg
,
fitted.pcreg
,
residuals.pcreg
Examples
pcreg(mcdonald, scale=TRUE, method="PC")
pcreg(mcdonald, scale=TRUE, method="LV")
[Package bestglm version 0.37.3 Index]