raptools (version 1.23.0)

ggcalibrate_original: The Original Calibration plot

Description

ggcalibrate_original plots the stats::predicted events against the actual event rate using the "old" form.

Usage

ggcalibrate_original(
  x1,
  x2 = NULL,
  y = NULL,
  n_cut = 5,
  cut_type = c("interval", "number", "width"),
  include_margin = FALSE
)

Value

a list of one or two ggplots

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_cut

An integer indicating either the number of intervals of the same width, the number of intervals of the same number of subjects, or the width (as a percentage) of the intervals.

cut_type

One of three strings: "interval", "number", or "width". - "interval": uses cut_interval() to get n_cut intervals of approximately equal width. - "number": uses cut_number() to get n_cut intervals with approximately equal counts. - "width": uses cut_width() to get intervals of a fixed width (approximately 100/n_cut).

include_margin

TRUE for including producing a bar plot of the counts of in each of the intervals. Default is FALSE. Note if the output is saved to my_graphs then using the library gridExtra the function grid.arrange(graphs$g, graphs$g_marg , nrow = 2, heights = c(2,1)) will produce a plot with both the calibration plot and the marginal plot.

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_original(
  x1, x2, y,
  n_cut = 3, cut_type = "interval",
  include_margin = FALSE
)

# \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_original(
  x1, x2, y,
  n_cut = 5, cut_type = "interval",
  include_margin = FALSE
)
# }