Compile pseudo population

Compiles pseudo population based on the original population and estimated GPS value.

Usage

compile_pseudo_pop(
  data_obj,
  ci_appr,
  gps_density,
  bin_seq,
  exposure_col_name,
  nthread,
  ...
)

Arguments

data_obj

A S3 object including the following:

• Original data set + GPS values

• e_gps_pred

• e_gps_std_pred

• w_resid

• gps_mx (min and max of gps)

• w_mx (min and max of w).

ci_appr

Causal inference approach.

gps_density

Model type which is used for estimating GPS value, including normal and kernel.

bin_seq

Sequence of w (treatment) to generate pseudo population. If NULL is passed the default value will be used, which is seq(min(w)+delta_n/2,max(w), by=delta_n).

exposure_col_name

Exposure data column name.

nthread

An integer value that represents the number of threads to be used by internal packages.

...

Additional parameters.

Value

compile_pseudo_pop returns the pseudo population data that is compiled based on the selected causal inference approach.

Examples

# \donttest{
set.seed(112)
m_d <- generate_syn_data(sample_size = 100)
data_with_gps <- estimate_gps(m_d[, c("id", "w")],
                              m_d[, c("id", "cf1","cf2","cf3","cf4","cf5","cf6")],
                              gps_density = "normal",
                              params = list(xgb_max_depth = c(3,4,5),
                                       xgb_nrounds=c(10,20,30,40,50,60)),
                              nthread = 1,
                              sl_lib = c("m_xgboost")
                             )


pd <- compile_pseudo_pop(data_obj = data_with_gps,
                         ci_appr = "matching",
                         gps_density = "normal",
                         bin_seq = NULL,
                         exposure_col_name = c("w"),
                         nthread = 1,
                         dist_measure = "l1",
                         covar_bl_method = 'absolute',
                         covar_bl_trs = 0.1,
                         covar_bl_trs_type= "mean",
                         delta_n = 0.5,
                         scale = 1)
# }