generate_pseudo_pop {CausalGPS}  R Documentation 
Generates pseudo population data set based on userdefined causal inference approach. The function uses an adaptive approach to satisfies covariate balance requirements. The function terminates either by satisfying covariate balance or completing the requested number of iteration, whichever comes first.
generate_pseudo_pop(
w,
c,
ci_appr,
gps_density = "normal",
use_cov_transform = FALSE,
transformers = list("pow2", "pow3"),
bin_seq = NULL,
exposure_trim_qtls = c(0.01, 0.99),
gps_trim_qtls = c(0, 1),
params = list(),
sl_lib = c("m_xgboost"),
nthread = 1,
include_original_data = FALSE,
gps_obj = NULL,
...
)
w 
A data.frame comprised of two columns: one contains the observed exposure variable, and the other is labeled as 'id'. The column for the outcome variable can be assigned any name as per your requirements. 
c 
A data.frame of includes observed covariate variables. It should also consist of a column named 'id'. 
ci_appr 
The causal inference approach. Possible values are:

gps_density 
Model type which is used for estimating GPS value,
including 
use_cov_transform 
If TRUE, the function uses transformer to meet the covariate balance. 
transformers 
A list of transformers. Each transformer should be a unary function. You can pass name of customized function in the quotes. Available transformers:

bin_seq 
Sequence of w (treatment) to generate pseudo population. If
NULL is passed the default value will be used, which is

exposure_trim_qtls 
A numerical vector of two. Represents the trim quantile level for exposure values. Both numbers should be in the range of [0,1] and in increasing order (default: c(0.01, 0.99)). 
gps_trim_qtls 
A numerical vector of two. Represents the trim quantile level for the gps values. Both numbers should be in the range of [0,1] and in increasing order (default: c(0.0, 1.0)). 
params 
Includes list of params that is used internally. Unrelated parameters will be ignored. 
sl_lib 
A vector of prediction algorithms. 
nthread 
An integer value that represents the number of threads to be used by internal packages. 
include_original_data 
If TRUE, includes the original data in the outcome. 
gps_obj 
A gps object that is generated with 
... 
Additional arguments passed to different models. 
if ci_appr = 'matching':
dist_measure: Matching function. Available options:
l1: Manhattan distance matching
delta_n: caliper parameter.
scale: a specified scale parameter to control the relative weight that is attributed to the distance measures of the exposure versus the GPS.
covar_bl_method: covariate balance method. Available options:
'absolute'
covar_bl_trs: covariate balance threshold
covar_bl_trs_type: covariate balance type (mean, median, maximal)
max_attempt: maximum number of attempt to satisfy covariate balance.
See create_matching()
for more details about the parameters and default
values.
if ci_appr = 'weighting':
covar_bl_method: Covariate balance method.
covar_bl_trs: Covariate balance threshold
max_attempt: Maximum number of attempt to satisfy covariate balance.
Returns a pseudo population (gpsm_pspop) object that is generated or augmented based on the selected causal inference approach (ci_appr). The object includes the following objects:
params
ci_appr
params
pseudo_pop
adjusted_corr_results
original_corr_results
best_gps_used_params
effect size of generated pseudo population
m_d < generate_syn_data(sample_size = 100)
pseuoo_pop < generate_pseudo_pop(m_d[, c("id", "w")],
m_d[, c("id", "cf1","cf2","cf3","cf4","cf5","cf6")],
ci_appr = "matching",
gps_density = "normal",
bin_seq = NULL,
expos_trim_qlts = c(0.01,0.99),
gps_trim_qlts = c(0.01,0.99),
use_cov_transform = FALSE,
transformers = list(),
params = list(xgb_nrounds=c(10,20,30),
xgb_eta=c(0.1,0.2,0.3)),
sl_lib = c("m_xgboost"),
nthread = 1,
covar_bl_method = "absolute",
covar_bl_trs = 0.1,
covar_bl_trs_type= "mean",
max_attempt = 1,
dist_measure = "l1",
delta_n = 1,
scale = 0.5)