var_select

formula object of the estimating equation

e_equ

dataframe containing partitioning and estimating variables
as well as any used sample design variables

data

e_fn

the sample weights for each observation

weights

lable of the strata containing the observation

strata

lable of the clusters containing the observation

clusters

string vector containing the names of pontential variables

integer specifying the number of permuations

perm_reps

numeric p-value used to reject null hypothesis in permutation 
test

pval

selects variables likely to result in the best split

Fits a linear model to survey data in each node obtained by
recursively partitioning the data.  The splitting variables and splits
selected are obtained using a procedure which adjusts for complex sample
design features used to obtain the data. Likewise the model fitting
algorithm produces design-consistent coefficients to the least squares
linear model between the dependent and independent variables.
The first stage of the design is accounted for in the provided variance
estimates. The main function returns the resulting binary tree with the
linear model fit at every end-node. The package provides a number of
functions and methods for these trees.

var_select: var_select

Description

Usage

Arguments

Value