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 level of
.exposureto treat as the reference, the unexposed group whose quantiles are returned inunexposed_quantiles. Either a level of.exposureor its position among the observed levels. IfNULL(default), the first observed level is used.- na.rm
Logical. If
FALSE(default), missing values in.var,.exposure, or any weight raise an error. IfTRUE, rows with missing values are dropped 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, the level of
.exposurethat is not the reference level.- unexposed_quantiles
Numeric. The quantile value for the unexposed group, the reference level of
.exposure.
Details
This function computes the data needed for quantile-quantile plots by
calculating corresponding quantiles from two distributions. Unweighted
quantiles come from stats::quantile(); weighted quantiles come from
weighted_quantile(), which uses the same definition, so a constant weight
reproduces the observed 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