Recursive Partitioning for Modeling Survey Data
Description
Functions to allow users to build and analyze design consistent
tree and random forest models using survey data from a complex sample
design. The 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 a number
of functions and methods available for use with these object types.