Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016)

Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016)

Usage

plotSmoothCalibration(
  plpResult,
  smooth = "loess",
  span = 0.75,
  nKnots = 5,
  scatter = FALSE,
  bins = 20,
  sample = TRUE,
  typeColumn = "evaluation",
  saveLocation = NULL,
  fileName = "smoothCalibration.pdf"
)

Arguments

plpResult

The result of running runPlp function. An object containing the model or location where the model is save, the data selection settings, the preprocessing and training settings as well as various performance measures obtained by the model.

smooth

options: 'loess' or 'rcs'

span

This specifies the width of span used for loess. This will allow for faster computing and lower memory usage.

nKnots

The number of knots to be used by the rcs evaluation. Default is 5

scatter

plot the decile calibrations as points on the graph. Default is False

bins

The number of bins for the histogram. Default is 20.

sample

If using loess then by default 20,000 patients will be sampled to save time

typeColumn

The name of the column specifying the evaluation type

saveLocation

Directory to save plot (if NULL plot is not saved)

fileName

Name of the file to save to plot, for example 'plot.png'. See the function ggsave in the ggplot2 package for supported file formats.

Value

A ggplot object.

Details

Create a plot showing the smoothed calibration

Examples

# generate prediction dataaframe with 1000 patients
predictedRisk <- stats::runif(1000)
# overconfident for high risk patients
actualRisk <- ifelse(predictedRisk < 0.5, predictedRisk, 0.5 + 0.5 * (predictedRisk - 0.5))
outcomeCount <- stats::rbinom(1000, 1, actualRisk)
# mock data frame
prediction <- data.frame(rowId = 1:1000,
                         value = predictedRisk, 
                         outcomeCount = outcomeCount,
                         evaluationType = "Test")
attr(prediction, "modelType") <- "binary"
calibrationSummary <- getCalibrationSummary(prediction, "binary",
                                            numberOfStrata = 10,
                                            typeColumn = "evaluationType")
plpResults <- list()
plpResults$performanceEvaluation$calibrationSummary <- calibrationSummary
plpResults$prediction <- prediction
plotSmoothCalibration(plpResults)
#> Smooth calibration plot for Test
#> $test
#> $test$smoothPlot

#> 
#> $test$histPlot

#> 
#>