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,
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 |
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. |
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)
.
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)
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)