ggcalibration: Model Calibration Plot

In ddsjoberg/bstfun: Functions for the MSK Biostatistics Department

Model Calibration Plot

Description

Assess a model's calibration via a calibration plot.

Usage

ggcalibration(
  data,
  y,
  x,
  n.groups = 10,
  conf.level = 0.95,
  ci.method = c("exact", "ac", "asymptotic", "wilson", "prop.test", "bayes", "logit",
    "cloglog", "probit"),
  geom_smooth.args = list(method = "loess", se = FALSE, formula = y ~ x, color = "black"),
  geom_errorbar.args = list(width = 0),
  geom_point.args = list(),
  geom_function.args = list(colour = "gray", linetype = "dashed")
)

Arguments

data	a data frame
y	variable name of the outcome coded as 0/1
x	variable name of the risk predictions
n.groups	number of groups
conf.level	level of confidence to be used in the confidence interval
ci.method	method to use to construct the interval. See binom::binom.confint() for details
geom_smooth.args	named list of arguments that will be passed to ggplot2::geom_smooth(). Default is list(method = "loess", se = FALSE, formula = y ~ x, color = "black")
geom_errorbar.args	named list of arguments that will be passed to ggplot2::geom_errorbar(). Default is list(width = 0)
geom_point.args	named list of arguments that will be passed to ggplot2::geom_point(). Default is list()
geom_function.args	named list of arguments that will be passed to ggplot2::geom_function() and is the function that adds the 45 degree guideline. Default is list(colour = "gray", linetype = "dashed")

Value

ggplot

Examples

glm(response ~ age + marker + grade, trial, family = binomial) %>%
  broom::augment(type.predict = "response") %>%
  ggcalibration(y = response, x = .fitted, n.groups = 6) +
  ggplot2::xlim(0, 1) +
  ggplot2::labs(x = "Model Risk")

ddsjoberg/bstfun documentation built on July 4, 2023, 10:59 a.m.