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Plot calibration curve for an ip_score object

Source: R/print.R
plot.ip_score.Rd

Produces a calibration plot comparing predicted and observed outcomes under the intervention of interest.

Usage

# S3 method for class 'ip_score'
plot(
  x,
  xlim = c(0, 1),
  ylim = c(0, 1),
  pty = "s",
  asp = NA,
  main,
  xlab = "Predicted",
  ylab = "Observed",
  cex.main = 0.8,
  legend = "topleft",
  ...
)

Arguments

x

The `ip_score` object returned by ip_score

xlim

The x limits of the plot, c(x1, x2)

ylim

The y limits of the plot, c(y1, y2)

pty

A character specifying the type of plot region to be used; "s" generates a square plotting region and "m" generates the maximal plotting region.

asp

The y/x aspect ratio

main

Character string giving the main title of the plot.

xlab

Character string specifying the x-axis label.

ylab

Character string specifying the y-axis label.

cex.main

Numeric value controlling the size of the main title.

legend

Keyword denoting the positioning of the legend. Can be "bottomright", "bottom", "bottomleft", "left", "topleft", "top", "topright", "right" and "center", or alternatively, "none" to hide the legend.

...

Currently ignored.

Value

Invisibly returns `x`.

Details

Subjects are grouped into subgroups according to percentiles of the estimated risks. For each subgroup, the x-coordinate is the mean estimated risk of the subgroup. The y-coordinate is the inverse-probability-weighted proportion of 'observed' events.

The observed and predicted calibration subgroup coordinates are computed by the ip_score function and are stored in `x$score$calplot`, where `x` is the `ip_score` object. These raw values can be used to create custom calibration plots when additional control is needed.

If ip_score was run with bootstrap resampling (`bootstrap > 0`), additional panels are produced for every evaluated model showing the calibration curves from all bootstrap replicate in grey.

This method is available only when "calplot" was included in the `metrics` argument of ip_score.

Examples

n <- 1000

data <- data.frame(L = rnorm(n), P = rnorm(n))
data$A <- rbinom(n, 1, plogis(data$L))
data$Y <- rbinom(n, 1, plogis(0.1 + 0.5*data$L + 0.7*data$P - 2*data$A))

random <- runif(n, 0, 1)
model <- glm(Y ~ A + P, data = data, family = "binomial")

score <- ip_score(
  object = list(random, model),
  data = data,
  outcome = Y,
  treatment_formula = A ~ L,
  treatment_of_interest = 0,
  bootstrap = 20,
  bootstrap_progress = FALSE,
  metrics = "calplot"
)

plot(score)