The Calibration plot

ggcalibrate plots the stats::predicted events against the actual event rate

Usage

ggcalibrate(
  x1,
  x2 = NULL,
  y = NULL,
  n_knots = 5,
  ci_level = 0.95,
  alpha_level = 0.25,
  actuals = FALSE,
  smooth_method = NULL,
  smooth_span = NULL
)

Arguments

x1

Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the baseline model. Must be between 0 & 1

x2

Either a logistic regression fitted using glm (base package) or lrm (rms package) or calculated probabilities (eg through a logistic regression model) of the new (alternative) model. Must be between 0 & 1

y

Binary of outcome of interest. Must be 0 or 1 (if fitted models are provided this is extracted from the fit which for an rms fit must have x = TRUE, y = TRUE).

n_knots

The curves are made by fitting a restricted cubic spline (rms package). The default 5-knots is usually enough.

ci_level

Confidence interval of the curve (default = 0.95).

alpha_level

Transparency (alpha) of the shaded confidence interval (default = 0.25).

actuals

Logical, whether to also plot the actual events (0 or 1) against the predictions as short vertical marks, like a rug plot, at 0 and 1 (default = FALSE).

smooth_method

Deprecated and ignored. The curves are no longer drawn with geom_smooth().

smooth_span

Deprecated and ignored. The curves are no longer drawn with geom_smooth().

Value

a ggplot

Details

The calibration curve for each model is a logistic regression of the outcome on a restricted cubic spline of the predicted probability. The confidence interval is calculated from the standard error of that fit on the log-odds scale and back-transformed, so it is bounded by 0 and 1.

See also

[ggcalibrate_BA()] for the same curves plotted as deviations from perfect calibration.

Examples

# Quick example with subset of data
data(data_risk)
data_subset <- data_risk[1:100, ]  # Use first 100 rows for speed
complete_cases <- complete.cases(data_subset)
data_clean <- data_subset[complete_cases, ]
y <- data_clean$outcome
x1 <- data_clean$baseline
x2 <- data_clean$new
output <- ggcalibrate(x1, x2, y, n_knots = 3, ci_level = 0.95)

# \donttest{
# Full dataset example
data(data_risk)
complete_cases <- complete.cases(data_risk)
data_clean <- data_risk[complete_cases, ]
y <- data_clean$outcome
x1 <- data_clean$baseline
x2 <- data_clean$new
output <- ggcalibrate(x1, x2, y, n_knots = 5, ci_level = 0.95)

# Show the actual events and a darker confidence interval
output <- ggcalibrate(x1, x2, y, alpha_level = 0.5, actuals = TRUE)
# }