Calculate quantile-quantile data comparing the distribution of a variable between treatment groups. This function computes the quantiles for both groups and returns a tidy data frame suitable for plotting or further analysis.
Usage
check_qq(
.data,
.var,
.exposure,
.weights = NULL,
quantiles = seq(0.01, 0.99, 0.01),
include_observed = TRUE,
.reference_level = NULL,
na.rm = FALSE
)Arguments
- .data
A data frame containing the variables.
- .var
Variable to compute quantiles for. Supports tidyselect syntax.
- .exposure
Column name of treatment/group variable. Supports tidyselect syntax.
- .weights
Optional weighting variable(s). Can be unquoted variable names (supports tidyselect syntax), a character vector, or NULL. Multiple weights can be provided to compare different weighting schemes. Default is NULL (unweighted).
- quantiles
Numeric vector of quantiles to compute. Default is
seq(0.01, 0.99, 0.01)for 99 quantiles.- include_observed
Logical. If using
.weights, also compute observed (unweighted) quantiles? Defaults to TRUE.- .reference_level
The reference treatment level to use for comparisons. If
NULL(default), uses the last level for factors or the maximum value for numeric variables.- na.rm
Logical; if TRUE, drop NA values before computation.
Value
A tibble with class "halfmoon_qq" containing columns:
- method
Character. The weighting method ("observed" or weight variable name).
- 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.
See also
bal_qq() for single weight QQ data, plot_qq() for visualization
Other balance functions:
bal_corr(),
bal_ess(),
bal_ks(),
bal_model_auc(),
bal_model_roc_curve(),
bal_qq(),
bal_smd(),
bal_vr(),
check_balance(),
check_ess(),
check_model_auc(),
check_model_roc_curve(),
plot_balance()
Examples
# Basic QQ data (observed only)
check_qq(nhefs_weights, age, qsmk)
#> # A tibble: 99 × 4
#> method quantile exposed_quantiles unexposed_quantiles
#> <fct> <dbl> <dbl> <dbl>
#> 1 observed 0.01 25 25
#> 2 observed 0.02 25 25
#> 3 observed 0.03 26 25
#> 4 observed 0.04 26 25.5
#> 5 observed 0.05 27 26
#> 6 observed 0.06 27 26
#> 7 observed 0.07 28 26
#> 8 observed 0.08 28 27
#> 9 observed 0.09 29 27
#> 10 observed 0.1 29 28
#> # ℹ 89 more rows
# With weighting
check_qq(nhefs_weights, age, qsmk, .weights = w_ate)
#> # A tibble: 198 × 4
#> method quantile exposed_quantiles unexposed_quantiles
#> <fct> <dbl> <dbl> <dbl>
#> 1 observed 0.01 25 25
#> 2 observed 0.02 25 25
#> 3 observed 0.03 26 25
#> 4 observed 0.04 26 25.5
#> 5 observed 0.05 27 26
#> 6 observed 0.06 27 26
#> 7 observed 0.07 28 26
#> 8 observed 0.08 28 27
#> 9 observed 0.09 29 27
#> 10 observed 0.1 29 28
#> # ℹ 188 more rows
# Compare multiple weighting schemes
check_qq(nhefs_weights, age, qsmk, .weights = c(w_ate, w_att))
#> # A tibble: 297 × 4
#> method quantile exposed_quantiles unexposed_quantiles
#> <fct> <dbl> <dbl> <dbl>
#> 1 observed 0.01 25 25
#> 2 observed 0.02 25 25
#> 3 observed 0.03 26 25
#> 4 observed 0.04 26 25.5
#> 5 observed 0.05 27 26
#> 6 observed 0.06 27 26
#> 7 observed 0.07 28 26
#> 8 observed 0.08 28 27
#> 9 observed 0.09 29 27
#> 10 observed 0.1 29 28
#> # ℹ 287 more rows