rpms: rpms
In rpms: Recursive Partitioning for Modeling Survey Data

Description Usage Arguments Value Examples

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 of continuous variables.

rpms(
  rp_equ,
  data,
  weights = ~1,
  strata = ~1,
  clusters = ~1,
  e_equ = ~1,
  e_fn = "survLm",
  l_fn = NULL,
  bin_size = NULL,
  gridpts = 3,
  perm_reps = 1000L,
  pval = 0.05
)

`rp_equ`	formula containing all variables for partitioning
`data`	data.frame that includes variables used in rp_equ, e_equ, and design information
`weights`	formula or vector of sample weights for each observation
`strata`	formula or vector of strata labels
`clusters`	formula or vector of cluster labels
`e_equ`	formula for modeling data in each node
`e_fn`	string name of function to use for modeling (only "survLm" is operational)
`l_fn`	loss function (ignored)
`bin_size`	integer specifying minimum number of observations in each node
`gridpts`	integer number of middle points to do in search; set to n for categorical variables when e_equ is used.
`perm_reps`	integer specifying the number of thousands of permutation replications to use to estimate p-value
`pval`	numeric p-value used to reject null hypothesis in permutation test

object of class "rpms"

{
# model mean of retirement account value for households with reported 
# retirment account values > 0 using a binary tree while accounting for 
# clusterd data and sample weights.

s1<- which(CE$IRAX > 0)
rpms(IRAX~EDUCA+AGE+BLS_URBN, data=CE[s1,], weights=~FINLWT21, clusters=~CID)

                 
# model linear fit between retirement account value and amount of income
# conditioning on education and accounting for clusterd data for households 
# with reported retirment account values > 0

rpms(IRAX~EDUCA, e_equ=IRAX~FINCBTAX, data=CE[s1,], weights=~FINLWT21, clusters=~CID)    

}