est_gspcr: Estimate Generalized Principal Component Regression

In gspcr: Generalized Supervised Principal Component Regression

Estimate Generalized Principal Component Regression

Description

Estimate SPCA on the data given chosen parameter values

Usage

est_gspcr(object = NULL, dv, ivs, fam, active_set, ndim)

Arguments

object	gspcrcv object resulting from the call of cv_gspcr(). If this is specified, then every other argument can be left blank.
dv	numeric vector or factor of dependent variable values
ivs	n \times p data.frame of independent variables (factors allowed)
fam	character vector of length 1 storing the description of the error distribution and link function to be used in the model
active_set	names of the columns of ivs to be used as predictors
ndim	numeric vector defining the number of principal components to be used (2 or more)

Details

After deciding on the number of components and the active set, this estimates the GSPCR model. This function can be used by specifying the object argument or by filling in custom values for every argument. If both the object and any other argument are specified, then the argument values will be prioritized.

Value

Description of function output

Author(s)

Edoardo Costantini, 2023

References

Bair, E., Hastie, T., Paul, D., & Tibshirani, R. (2006). Prediction by supervised principal components. Journal of the American Statistical Association, 101(473), 119-137.

gspcr documentation built on May 29, 2024, 2:44 a.m.