Checks the covariate balance of original population or pseudo population.
Arguments
- w
A vector of observed continuous exposure variable.
- c
A data.frame of observed covariates variable.
- ci_appr
The causal inference 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.
Examples
# \donttest{
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[, c("id", "w")],
mydata[, c("id", "cf1", "cf2", "cf3",
"cf4","cf5", "cf6", "year",
"region")],
ci_appr = "matching",
gps_density = "kernel",
exposure_trim_qtls = c(0.01,0.99),
sl_lib = c("m_xgboost"),
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,
nthread = 1)
#> mean absolute correlation: 0.146209278430226| Covariate balance threshold: 0.1
#> Loading required package: nnls
#> mean absolute correlation: 0.329695962727751| Covariate balance threshold: 0.1
#> Covariate balance condition has not been met.
#> Best mean absolute correlation: 0.329695962727751| Covariate balance threshold: 0.1
adjusted_corr_obj <- check_covar_balance(w = pseudo_pop$pseudo_pop[, c("w")],
c = pseudo_pop$pseudo_pop[ ,
pseudo_pop$covariate_cols_name],
counter = pseudo_pop$pseudo_pop[,
c("counter_weight")],
ci_appr = "matching",
nthread = 1,
covar_bl_method = "absolute",
covar_bl_trs = 0.1,
covar_bl_trs_type = "mean")
#> mean absolute correlation: 0.329695962727751| Covariate balance threshold: 0.1
# }