rpms_zinf

formula containing all variables for partitioning

rp_equ

data.frame that includes variables used in rp_equ, e_equ, 
and design information

data

formula or vector of sample weights for each observation

weights

formula or vector of strata labels

strata

formula or vector of cluster labels

clusters

formula for modeling data in each node

e_equ

string name of function to use for modeling 
(only "survLm" is operational)

e_fn

l_fn

numeric minimum number of observations in each node

bin_size

integer number of middle points to do in search

gridpts

integer specifying the number of thousands of permuation 
replications to use to estimate p-value

perm_reps

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

pval

main function producing a regression tree using variables
 from rp_equ to partition the data and fit the model e_equ on each node.
 Currently only uses data with complete cases.

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.

rpms_zinf: rpms_zinf

Description

Usage

Arguments

Value