compile_pseudo_pop {CausalGPS} | R Documentation |
Compile pseudo population
Description
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:
|
ci_appr |
Causal inference approach. |
gps_density |
Model type which is used for estimating GPS value,
including |
bin_seq |
Sequence of w (treatment) to generate pseudo population. If
NULL is passed the default value will be used, which is
|
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
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)