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Create 2-dimensional QQ geometries

Source: R/geom_qq2.R
geom_qq2.Rd

geom_qq2() is a geom for creating quantile-quantile plots with support for weighted comparisons. QQ plots compare the quantiles of two distributions, making them useful for assessing distributional balance in causal inference. As opposed to geom_qq(), this geom does not compare a variable against a theoretical distribution, but rather against two group's distributions, e.g., treatment vs. control.

Usage

geom_qq2(
  mapping = NULL,
  data = NULL,
  stat = "qq2",
  position = "identity",
  na.rm = TRUE,
  show.legend = NA,
  inherit.aes = TRUE,
  quantiles = seq(0.01, 0.99, 0.01),
  treatment_level = NULL,
  ...
)

Arguments

mapping

Set of aesthetic mappings. Required aesthetics are sample (variable) and treatment (group). The treatment aesthetic can be a factor, character, or numeric. Optional aesthetics include weight for weighting.

data

Data frame to use. If not specified, inherits from the plot.

stat

Statistical transformation to use. Default is "qq2".

position

Position adjustment. Default is "identity".

na.rm

If FALSE, the default, missing values are removed with a warning. If TRUE, missing values are silently removed.

show.legend

Logical. Should this layer be included in the legends? NA, the default, includes if any aesthetics are mapped.

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them.

quantiles

Numeric vector of quantiles to compute. Default is seq(0.01, 0.99, 0.01) for 99 quantiles.

treatment_level

The level of the outcome variable to consider as the treatment/event. If NULL (default), uses the last level for factors or the maximum value for numeric variables.

...

Other arguments passed on to layer().

Value

A ggplot2 layer.

Details

Quantile-quantile (QQ) plots visualize how the distributions of a variable differ between treatment groups by plotting corresponding quantiles against each other. If the distributions are identical, points fall on the 45-degree line (y = x). Deviations from this line indicate differences in the distributions.

QQ plots are closely related to empirical cumulative distribution function (ECDF) plots (see geom_ecdf()). While ECDF plots show \(F(x) = P(X \leq x)\) for each group, QQ plots show \(F_1^{-1}(p)\) vs \(F_2^{-1}(p)\), essentially the inverse relationship. Both approaches visualize the same information about distributional differences, but QQ plots make it easier to spot deviations through a 45-degree reference line.

See also

  • geom_ecdf() for an alternative visualization of distributional differences

  • plot_qq() for a complete plotting function with reference line and labels

  • qq() for the underlying data computation

Other ggplot2 functions: geom_calibration(), geom_ecdf(), geom_mirror_density(), geom_mirror_histogram(), geom_roc()

Examples

library(ggplot2)

# Basic QQ plot
ggplot(nhefs_weights, aes(sample = age, treatment = qsmk)) +
  geom_qq2() +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed")


# With weighting
ggplot(nhefs_weights, aes(sample = age, treatment = qsmk, weight = w_ate)) +
  geom_qq2() +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed")


# Compare multiple weights using long format
long_data <- tidyr::pivot_longer(
  nhefs_weights,
  cols = c(w_ate, w_att),
  names_to = "weight_type",
  values_to = "weight"
)

ggplot(long_data, aes(color = weight_type)) +
  geom_qq2(aes(sample = age, treatment = qsmk, weight = weight)) +
  geom_abline(intercept = 0, slope = 1, linetype = "dashed")