Plot a calibration curve — plot.pmcalibration • pmcalibration

Plot a pmcalibration object. For binary outcomes, also plot the distribution of predicted risks by outcome. Alternatively you can use get_curve() to get the data required to plot the calibration curve.

Usage

# S3 method for class 'pmcalibration'
plot(
  x,
  conf_level = 0.95,
  riskdist = TRUE,
  linecol = "black",
  fillcol = "grey",
  ideallty = 2,
  idealcol = "red",
  ...
)

Arguments

x: a pmcalibration calibration curve
conf_level: width of the confidence interval (0.95 gives 95% CI). Ignored if call to pmcalibration didn't request confidence intervals
riskdist: add risk distribution plot under calibration curve (TRUE) or not (FALSE)
linecol: color of the calibration curve line
fillcol: color of the confidence interval
ideallty: line type of the ideal unit slope line
idealcol: color of the ideal unit slope line
...: other args for plot() (currently only lims and labs can be specified)

Value

No return value, called for side effects

Examples

library(pmcalibration)
# simulate some data with a binary outcome
n <- 500
dat <- sim_dat(N = n, a1 = .5, a3 = .2)
head(dat)
#>           x1         x2 y         LP
#> 1 -0.9802799  0.0734722 0 -0.4212124
#> 2 -1.4877218 -1.3790974 1 -1.9564765
#> 3  0.1761391  0.3903213 1  1.0802105
#> 4  0.8570410 -0.1267395 1  1.2085773
#> 5  0.0808151 -1.5949875 0 -1.0399522
#> 6 -0.4012543 -1.4866294 1 -1.2685804
# predictions
p <- with(dat, invlogit(.5 + x1 + x2 + x1*x2*.1))

# fit calibration curve
cal <- pmcalibration(y = dat$y, p = p, smooth = "gam", k = 20, ci = "pw", plot = FALSE)

plot(cal, xlab = "Predicted Risk of Outcome") # customize plot