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Nesting the vignette study in a diabetes population to reduce size. S…
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…tarted work on evaluation routines for surivival models.
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schuemie committed Apr 6, 2015
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143 changes: 143 additions & 0 deletions .cproject
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11 changes: 11 additions & 0 deletions .gitignore
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Expand Up @@ -10,3 +10,14 @@
# RStudio files
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# SqlRender
statement_*.sql
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standalone/build/*
27 changes: 27 additions & 0 deletions .project
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1 change: 1 addition & 0 deletions NAMESPACE
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Expand Up @@ -10,6 +10,7 @@ S3method(summary,cohortData)
S3method(summary,covariateData)
S3method(summary,outcomeData)
export(computeAuc)
export(computeAucFromDataFrames)
export(createCovariateSettings)
export(fitPredictiveModel)
export(getDbCohortData)
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35 changes: 35 additions & 0 deletions R/Evaluate.R
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Expand Up @@ -47,6 +47,41 @@ computeAuc <- function(prediction,
}
}

#' Compute the area under the ROC curve
#'
#' @details Computes the area under the ROC curve for the predicted probabilities, given the
#' true observed outcomes.
#'
#' @param prediction A vector with the predicted hazard rate.
#' @param status A vector with the status of 1 (event) or 0 (no event).
#' @param time Only for survival models: a vector with the time to event or censor (which ever comes first).
#' @param confidenceInterval Should 95 percebt confidence intervals be computed?
#' @param timePoint Only for survival models: time point when the AUC should be evaluated
#' @param modelType Type of model. Currently supported are "logistic" and "survival".
#'
#' @export
computeAucFromDataFrames <- function(prediction, status, time = NULL, confidenceInterval = FALSE, timePoint, modelType = "logistic"){
if (modelType == "survival" & confidenceInterval)
stop("Currently not supporting confidence intervals for survival models")

if (modelType == "survival"){
Surv.rsp <- Surv(time, status)
Surv.rsp.new <- Surv.rsp
if (missing(timePoint))
timePoint <- max(time[status == 1])
auc <- AUC.uno(Surv.rsp, Surv.rsp.new, prediction, timePoint)$auc
return(auc*auc)
} else {
if (confidenceInterval){
auc <- .Call('PatientLevelPrediction_aucWithCi', PACKAGE = 'PatientLevelPrediction', prediction, status)
return(data.frame(auc=auc[1],auc_lb95ci=auc[2],auc_lb95ci=auc[3]))
} else {
auc <- .Call('PatientLevelPrediction_auc', PACKAGE = 'PatientLevelPrediction', prediction, status)
return(auc)
}
}
}

#' Plot the calibration
#'
#' @details Create a plot showing the predicted probabilities and the observed fractions. Predictions
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