plotGeneralizability: Plot the train/test generalizability diagnostic

In OHDSI/PatientLevelPrediction: Develop Clinical Prediction Models Using the Common Data Model

Plot the train/test generalizability diagnostic

Description

Plot the train/test generalizability diagnostic

Usage

plotGeneralizability(
  covariateSummary,
  saveLocation = NULL,
  fileName = "Generalizability.png"
)

Arguments

covariateSummary	A prediction object as generated using the runPlp function.
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.

Details

Create a plot showing the train/test generalizability diagnostic #'

Value

A ggplot object. Use the ggsave function to save to file in a different format.

Examples

 
data("simulationProfile")
plpData <- simulatePlpData(simulationProfile, n=1000)
population <- createStudyPopulation(plpData, outcomeId = 3)
data <- splitData(plpData, population = population)
strata <- data.frame(
 rowId = c(data$Train$labels$rowId, data$Test$labels$rowId),
 strataName = c(rep("Train", nrow(data$Train$labels)),
                rep("Test", nrow(data$Test$labels))))
covariateSummary <- covariateSummary(plpData$covariateData, 
                                     cohort = dplyr::select(population, "rowId"),
 strata = strata, labels = population)
plotGeneralizability(covariateSummary)

OHDSI/PatientLevelPrediction documentation built on Feb. 14, 2025, 9:44 a.m.