# Calibration Plots Calibration plot based on predictions. Overall root mean squared error (RMSE) of predicted and observed discrete hazards is calculated. ```r calibrationPlot( testPreds, testDataLong, weights = NULL, K = 10, event = "e1", ... ) ``` ## Arguments - `testPreds`: Predictions on the validation data with model fitted on training data ("numeric vector"). - `testDataLong`: Validation data set in long format ("class data.frame"). - `weights`: optional vector of weights ("numeric vector"). The length of weights must be equal to the number of observations of the validation data set. - `K`: Number of subsets for plotting ("integer vector"). - `event`: Column names of the event to be considered for plotting (only in case of cause-specific hazards) ("character vector"). - `...`: Additional arguments passed to `plot`. ## Returns Calibration plot ## Examples ```r #################### # Data preprocessing # Example unemployment data library(Ecdat) data(UnempDur) # Select subsample selectInd1 <- 1:100 selectInd2 <- 101:200 trainSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd1], ] valSet <- UnempDur[which(UnempDur$spell %in% (1:10))[selectInd2], ] #################### # One event # Convert to long format trainSet_long <- dataLong(dataShort = trainSet, timeColumn = "spell", eventColumn = "censor1") valSet_long <- dataLong(dataShort = valSet, timeColumn = "spell", eventColumn = "censor1") # Estimate continuation ratio model with logit link glmFit <- glm(formula = y ~ timeInt + age + logwage, data = trainSet_long, family = binomial()) # Calculate predicted hazards predHazards <- predict(glmFit, newdata = valSet_long, type = "response") # Calibration plot calibrationPlot(predHazards, testDataLong = valSet_long) ############################ # Two cause specific hazards # Convert to long format trainSet_long <- dataLongCompRisks(dataShort = trainSet, timeColumn = "spell", eventColumns = c("censor1", "censor4")) valSet_long <- dataLongCompRisks(dataShort = valSet, timeColumn = "spell", eventColumns = c("censor1", "censor4")) # Estimate continuation ratio model with logit link vglmFit <- VGAM::vglm(formula = cbind(e0, e1, e2) ~ timeInt + age + logwage, data = trainSet_long, family = VGAM::multinomial(refLevel = "e0")) # Calculate predicted hazards predHazards <- VGAM::predictvglm(vglmFit, newdata = valSet_long, type = "response") # Calibration plots calibrationPlot(predHazards, testDataLong = valSet_long) calibrationPlot(predHazards, testDataLong = valSet_long, event = "e2") ############################### # Subdistribution hazards model # Convert to long format trainSet_long <- dataLongSubDist(dataShort = trainSet, timeColumn = "spell", eventColumns = c("censor1", "censor4"), eventFocus = "censor1") valSet_long <- dataLongSubDist(dataShort = valSet, timeColumn = "spell", eventColumns = c("censor1", "censor4"), eventFocus = "censor1") # Estimate continuation ratio model with logit link glmFit <- glm(formula = y ~ timeInt + age + logwage, data = trainSet_long, family = binomial(), weights = trainSet_long$subDistWeights) # Calculate predicted hazards predHazards <- predict(glmFit, newdata = valSet_long, type = "response") # Calibration plot calibrationPlot(predHazards, testDataLong = valSet_long, weights = valSet_long$subDistWeights) ``` ## References Rdpack::insert_ref(key="bergerTutorial",package="discSurv") Rdpack::insert_ref(key="heyardValCompRisks",package="discSurv") Rdpack::insert_ref(key="bergerAssessing",package="discSurv") ## See Also `estRecal`, `dataLong`, `dataLongCompRisks`, `dataLongSubDist` ## Author(s) Moritz Berger moritz.berger@imbie.uni-bonn.de [https://www.imbie.uni-bonn.de/personen/dr-moritz-berger/](https://www.imbie.uni-bonn.de/personen/dr-moritz-berger/)

calibrationPlot function