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

Description Usage Arguments Value Examples

function for producing boosted rpms models (trees or random forests)

rpms_boost(
  rp_equ,
  data,
  weights = ~1,
  strata = ~1,
  clusters = ~1,
  e_equ = ~1,
  bin_size = NULL,
  gridpts = 3,
  perm_reps = 100L,
  pval = 0.05,
  f_size = 200L,
  model_type = "tree",
  times = 2L
)

`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
`bin_size`	numeric minimum number of observations in each node
`gridpts`	integer number of middle points to do in search
`perm_reps`	integer specifying the number of thousands of permuation replications to use to estimate p-value
`pval`	numeric p-value used to reject null hypothesis in permutation test
`f_size`	integer specifying the number of trees in the forest (only used if model_type is "forest")
`model_type`	string: one of "tree" or "forest"
`times`	integer specifying number of boosting levels to try.

object of class "rpms_boost"

{
# model mean of retirement contributions with a binary tree while accounting 
# for clusterd data and sample weights.

rpms_boost(IRAX~EDUCA+AGE+BLS_URBN, data = CE,  weights=~FINLWT21, clusters=~CID, pval=.01)


}