box_ind

<p>dataframe containing the variables used for the recursive 
partitioning.</p>

newdata

<p>For each row of data, returns a vector indicators whether
              observation is in that box or not</p>

Functions to allow users to build and analyze design consistent
tree and random forest models using survey data from a complex sample
design.  The tree model algorithm can fit a linear model to survey data
in each node obtained by recursively partitioning the data.  The splitting
variables and selected splits are obtained using a randomized permutation
test procedure which adjusted for complex sample design features used to
obtain the data. Likewise the model fitting algorithm produces
design-consistent coefficients to any specified least squares linear model
between the dependent and independent variables used in the end nodes.
The main functions return the resulting binary tree or random forest as
an object of "rpms" or "rpms_forest" type. The package also provides methods
modeling a "boosted" tree or forest model and a tree model for zero-inflated
data as well as a number of functions and methods available for use with
these object types.

box_ind: box_ind

Description

Usage

Arguments

Value