CGC: Internal function for the Clustered Grouped Calibration Curve...

In CalibrationCurves: Calibration Performance

Internal function for the Clustered Grouped Calibration Curve (CGC)

Description

Estimates the calibration curves using the CGC approach. The function supports two grouping methods: equal-sized groups ("grouped") or interval-based groups ("interval"). Optionally, a calibration plot can be produced with cluster-specific curves.

Usage

CGC(
  data = NULL,
  p,
  y,
  cluster,
  cl.level = 0.95,
  ntiles = 10,
  cluster_curves = FALSE,
  plot = TRUE,
  size = 1,
  linewidth = 0.4,
  univariate = FALSE,
  method = c("grouped", "interval")
)

Arguments

data	optional data frame containing the variables p, y, and cluster. If supplied, variable names should be given without quotation marks.
p	predicted probabilities (numeric vector) or name of the column in data.
y	binary outcome variable or the name of the column in data.
cluster	cluster identifier (factor, character, or integer) or name of the column in data.
cl.level	the confidence level for the calculation of the confidence interval. Default is 0.95.
ntiles	integer, number of groups (tiles) for calibration. Default is 10.
cluster_curves	logical, whether to include cluster-specific calibration curves in the plot. Default is FALSE.
plot	logical, whether to return a calibration plot. Default is TRUE.
size	numeric, point size for plotted curves. Default is 1.
linewidth	numeric, line width for plotted curves. Default is 0.4.
univariate	logical, whether to use univariate meta-analysis. Default is FALSE.
method	character, grouping method: "grouped" (equal-sized groups) or "interval" (interval-based). Default is "grouped".

Details

When method = "grouped", the predictions are divided into equal-sized bins using quantiles. Conversely, if method ="interval", the predictions are divided into fixed-width bins across [0, 1].

The function performs a meta-analysis within each group. This can be either a univariate or bivariate analysis, which is specified in the univariate argument. The univariate analysis is performed using the metaprop function and the bivariate analysis employs the rma.mv function. Hereafter, the results are aggregated and plotted as calibration curves.

Value

A list containing:

plot_data

Data frame of meta-analysis calibration estimates.

trad_grouped

Data frame with traditional grouped calibration results.

observed_data

Data frame with per-observation calibration data.

cluster_data

Data frame with cluster-specific calibration summaries.

plot

A ggplot2 object if plot = TRUE, otherwise NULL.

CalibrationCurves documentation built on Dec. 9, 2025, 5:08 p.m.