Extract plot data from pmcalibration object — get

Extract plot data from pmcalibration object

Usage

get_curve(x, conf_level = 0.95)

Arguments

x: pmcalibration object
conf_level: width of the confidence interval (0.95 gives 95% CI). Ignored if call to pmcalibration didn't request confidence intervals

Value

data frame for plotting with 4 columns

p - values for the x-axis (predicted probabilities - note these are *not* from your data and are only used for plotting)
p_c - probability implied by the calibration curve given p
lower and upper - bounds of the confidence interval

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 -0.8168944 -0.4201007 1 -0.6683596
#> 2  0.3836505  1.0472054 1  2.0112081
#> 3 -0.4586323 -1.5465763 0 -1.3633466
#> 4 -0.7157040 -1.0460057 1 -1.1119836
#> 5  0.4295485 -2.2726698 0 -1.5383656
#> 6 -1.1698527 -0.3166519 1 -0.9124175
# 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")


cplot <- get_curve(cal, conf_level = .95)
head(cplot)
#>            p        p_c       lower      upper
#> 1 0.02123300 0.03373625 0.006595718 0.06087678
#> 2 0.03106377 0.04700997 0.013016223 0.08100371
#> 3 0.04089454 0.05969715 0.020108363 0.09928593
#> 4 0.05072532 0.07194617 0.027667787 0.11622456
#> 5 0.06055609 0.08384568 0.035577422 0.13211394
#> 6 0.07038687 0.09545483 0.043761841 0.14714781

if (requireNamespace("ggplot2", quietly = TRUE)){
library(ggplot2)
ggplot(cplot, aes(x = p, y = p_c, ymin=lower, ymax=upper)) +
  geom_abline(intercept = 0, slope = 1, lty=2) +
  geom_line() +
  geom_ribbon(alpha = 1/4) +
  lims(x=c(0,1), y=c(0,1))
}