# Plot the smooth calibration as detailed in Calster et al. "A calibration heirarchy for risk models was defined: from utopia to empirical data" (2016) ```r 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. ## Returns A ggplot object. ## Details Create a plot showing the smoothed calibration ## Examples ```r # 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) ```

plotSmoothCalibration function