compile_pseudo_pop function

Compile pseudo population

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

compile_pseudo_pop(
  data_obj,
  ci_appr,
  gps_density,
  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.

• 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.

Returns

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

Details

For matching approach, use an extra parameter, bin_seq, which is 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).

Examples

set.seed(112)
m_d <- generate_syn_data(sample_size = 100)

m_xgboost <- function(nthread = 1,
                      ntrees = 35,
                      shrinkage = 0.3,
                      max_depth = 5,
                      ...) {SuperLearner::SL.xgboost(
                        nthread = nthread,
                        ntrees = ntrees,
                        shrinkage=shrinkage,
                        max_depth=max_depth,
                        ...)}

data_with_gps <- estimate_gps(.data = m_d,
                              .formula = w ~ cf1 + cf2 + cf3 +
                                             cf4 + cf5 + cf6,
                              gps_density = "normal",
                              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)