Model Calibration Plot

Assess a model's calibration via a calibration plot.

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")