Calculate quantile-quantile data comparing the distribution of a variable between treatment groups for a single weighting scheme (or unweighted). This function computes the quantiles for both groups and returns a data frame suitable for plotting or further analysis.
Usage
bal_qq(
.data,
.var,
.exposure,
.weights = NULL,
quantiles = seq(0.01, 0.99, 0.01),
.reference_level = NULL,
na.rm = FALSE
)Arguments
- .data
A data frame containing the variables.
- .var
Variable to compute quantiles for (unquoted).
- .exposure
Column name of treatment/group variable (unquoted).
- .weights
Optional single weight variable (unquoted). If NULL, computes unweighted quantiles.
- quantiles
Numeric vector of quantiles to compute. Default is
seq(0.01, 0.99, 0.01)for 99 quantiles.- .reference_level
The reference group level for comparisons. Can be either a group level value or a numeric index. If
NULL(default), uses the first level.- na.rm
A logical value indicating whether to remove missing values before computation. If
FALSE(default), missing values in the input will produceNAin the output.
Value
A tibble with columns:
- quantile
Numeric. The quantile probability (0-1).
- exposed_quantiles
Numeric. The quantile value for the exposed group.
- unexposed_quantiles
Numeric. The quantile value for the unexposed group.
Details
This function computes the data needed for quantile-quantile plots by calculating corresponding quantiles from two distributions. The computation uses the inverse of the empirical cumulative distribution function (ECDF). For weighted data, it first computes the weighted ECDF and then inverts it to obtain quantiles.
When the distributions of a variable are similar between treatment groups (indicating good balance), the QQ plot points will lie close to the diagonal line y = x.
See also
check_qq() for computing QQ data across multiple weights,
plot_qq() for visualization
Other balance functions:
bal_corr(),
bal_ess(),
bal_ks(),
bal_model_auc(),
bal_model_roc_curve(),
bal_smd(),
bal_vr(),
check_balance(),
check_ess(),
check_model_auc(),
check_model_roc_curve(),
check_qq(),
plot_balance()
Examples
# Unweighted QQ data
bal_qq(nhefs_weights, age, qsmk)
#> # A tibble: 99 × 3
#> quantile exposed_quantiles unexposed_quantiles
#> <dbl> <dbl> <dbl>
#> 1 0.01 25 25
#> 2 0.02 25 25
#> 3 0.03 26 25
#> 4 0.04 26 25.5
#> 5 0.05 27 26
#> 6 0.06 27 26
#> 7 0.07 28 26
#> 8 0.08 28 27
#> 9 0.09 29 27
#> 10 0.1 29 28
#> # ℹ 89 more rows
# Weighted QQ data
bal_qq(nhefs_weights, age, qsmk, .weights = w_ate)
#> # A tibble: 99 × 3
#> quantile exposed_quantiles unexposed_quantiles
#> <dbl> <dbl> <dbl>
#> 1 0.01 25 25
#> 2 0.02 25 25
#> 3 0.03 25.3 25
#> 4 0.04 26 26
#> 5 0.05 26 26
#> 6 0.06 26 26
#> 7 0.07 27 27
#> 8 0.08 27 27
#> 9 0.09 28 27
#> 10 0.1 28 28
#> # ℹ 89 more rows
# Custom quantiles
bal_qq(nhefs_weights, age, qsmk, .weights = w_ate,
quantiles = seq(0.1, 0.9, 0.1))
#> # A tibble: 9 × 3
#> quantile exposed_quantiles unexposed_quantiles
#> <dbl> <dbl> <dbl>
#> 1 0.1 28 28
#> 2 0.2 32 31
#> 3 0.3 34.1 35
#> 4 0.4 39 39
#> 5 0.5 43 43
#> 6 0.6 47 47
#> 7 0.7 51 50
#> 8 0.8 55 54
#> 9 0.9 60 60