Checks the covariate balance of original population or pseudo population.
Usage
check_covar_balance(
w,
c,
ci_appr,
counter_weight = NULL,
covar_bl_method = "absolute",
covar_bl_trs = 0.1,
covar_bl_trs_type = "mean"
)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.
- 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).
Examples
# \donttest{
set.seed(422)
n <- 100
mydata <- generate_syn_data(sample_size=n)
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)
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 = mydata,
.formula = w ~ cf1 + cf2 + cf3 + cf4 + cf5 +
cf6 + year + region,
sl_lib = c("m_xgboost"),
gps_density = "kernel")
#> Loading required package: nnls
#> Error in get(library$library$predAlgorithm[s], envir = env): object 'm_xgboost' not found
cw_object_matching <- compute_counter_weight(gps_obj = data_with_gps,
ci_appr = "matching",
bin_seq = NULL,
nthread = 1,
delta_n = 0.1,
dist_measure = "l1",
scale = 0.5)
#> Error in compute_counter_weight(gps_obj = data_with_gps, ci_appr = "matching", bin_seq = NULL, nthread = 1, delta_n = 0.1, dist_measure = "l1", scale = 0.5): object 'data_with_gps' not found
pseudo_pop <- generate_pseudo_pop(.data = mydata,
cw_obj = cw_object_matching,
covariate_col_names = c("cf1", "cf2", "cf3",
"cf4", "cf5", "cf6",
"year", "region"),
covar_bl_trs = 0.1,
covar_bl_trs_type = "maximal",
covar_bl_method = "absolute")
#> Error in generate_pseudo_pop(.data = mydata, cw_obj = cw_object_matching, covariate_col_names = c("cf1", "cf2", "cf3", "cf4", "cf5", "cf6", "year", "region"), covar_bl_trs = 0.1, covar_bl_trs_type = "maximal", covar_bl_method = "absolute"): object 'cw_object_matching' not found
adjusted_corr_obj <- check_covar_balance(w = pseudo_pop$.data[, c("w")],
c = pseudo_pop$.data[ ,
pseudo_pop$params$covariate_col_names],
counter = pseudo_pop$.data[,
c("counter_weight")],
ci_appr = "matching",
covar_bl_method = "absolute",
covar_bl_trs = 0.1,
covar_bl_trs_type = "mean")
#> Error in ci_appr %in% c("matching", "weighting"): object 'pseudo_pop' not found
# }