View source: R/check_covar_balance.R
check_covar_balance | R Documentation |
Checks the covariate balance of original population or pseudo population.
check_covar_balance( w, c, ci_appr, optimized_compile, counter_weight = NULL, nthread = 1, ... )
w |
A vector of observed continuous exposure variable. |
c |
A data.frame of observed covariates variable. |
ci_appr |
The causal inference approach. |
optimized_compile |
If TRUE, use optimized compile approach. |
counter_weight |
A weight vector in different situations. If the matching approach is selected, it is an integer data.table of counters. In the case of the weighting approach, it is weight data.table. |
nthread |
The number of available threads. |
... |
Additional arguments passed to different models. |
For ci_appr == matching:
covar_bl_method
covar_bl_trs
output object:
corr_results
absolute_corr
mean_absolute_corr
pass (TRUE,FALSE)
set.seed(422) n <- 100 mydata <- generate_syn_data(sample_size=100) year <- sample(x=c("2001","2002","2003","2004","2005"),size = n, replace = TRUE) region <- sample(x=c("North", "South", "East", "West"),size = n, replace = TRUE) mydata$year <- as.factor(year) mydata$region <- as.factor(region) mydata$cf5 <- as.factor(mydata$cf5) pseudo_pop <- generate_pseudo_pop(mydata$Y, mydata$treat, mydata[c("cf1","cf2","cf3","cf4","cf5","cf6","year","region")], ci_appr = "matching", pred_model = "sl", gps_model = "non-parametric", trim_quantiles = c(0.01,0.99), optimized_compile = TRUE, sl_lib = c("m_xgboost"), covar_bl_method = "absolute", covar_bl_trs = 0.1, covar_bl_trs_type = "mean", max_attempt = 1, matching_fun = "matching_l1", delta_n = 1, scale = 0.5, nthread = 1) adjusted_corr_obj <- check_covar_balance(w = pseudo_pop$pseudo_pop[, c("w")], c = pseudo_pop$pseudo_pop[ , pseudo_pop$covariate_cols_name, with=FALSE], counter = pseudo_pop$pseudo_pop[, c("counter_weight")], ci_appr="matching", nthread=1, covar_bl_method = "absolute", covar_bl_trs = 0.1, covar_bl_trs_type = "mean", optimized_compile=TRUE)
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