model_list_pca: A list of models for the best subset selection with PCA.

In autoMrP: Improving MrP with Ensemble Learning

A list of models for the best subset selection with PCA.

Description

model_list_pca() generates an exhaustive list of lme4 model formulas from the individual level and context level principal components as well as geographic unit variables to be iterated over in best subset selection with principal components.

Usage

model_list_pca(y, L1.x, L2.x, L2.unit, L2.reg = NULL)

Arguments

y	Outcome variable. A character vector containing the column names of the outcome variable.
L1.x	Individual-level covariates. A character vector containing the column names of the individual-level variables in survey and census used to predict outcome y. Note that geographic unit is specified in argument L2.unit.
L2.x	Context-level covariates. A character vector containing the column names of the context-level variables in survey and census used to predict outcome y.
L2.unit	Geographic unit. A character scalar containing the column name of the geographic unit in survey and census at which outcomes should be aggregated.
L2.reg	Geographic region. A character scalar containing the column name of the geographic region in survey and census by which geographic units are grouped (L2.unit must be nested within L2.reg). Default is NULL.

Value

Returns a list with the number of elements k+1 where k is the number of context-level variables. Each element is of class formula. The first element is a model with context-level variables and the following models iteratively add the principal components as context-level variables.

autoMrP documentation built on May 29, 2024, 6:40 a.m.