| rpms | R Documentation |
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)
}