83 lines
No EOL
3.7 KiB
R
83 lines
No EOL
3.7 KiB
R
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#' Integrated Brier Score
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#'
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#' Used to calculate the Integrated Brier Score, which for the competing risks
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#' setting is the integral of the squared difference between each observed
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#' cumulative incidence function (CIF) for each observation and the
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#' corresponding predicted CIF. If the survivor function (1 - CDF) of the
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#' censoring distribution is provided, weights can be calculated to account for
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#' the censoring.
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#'
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#' @return A numeric vector of the Integrated Brier Score for each prediction.
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#' @param responses A list of responses corresponding to the provided
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#' mortalities; use \code{\link{CR_Response}}.
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#' @param predictions The predictions to be tested against.
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#' @param event The event type for the error to be calculated on.
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#' @param time \code{time} specifies the upper bound of the integral.
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#' @param censoringDistribution Optional; if provided then weights are
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#' calculated on the errors. There are three ways to provide it - \itemize{
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#' \item{If you have all the censor times and just want to use a simple
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#' empirical estimate of the distribution, just provide a numeric vector of
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#' all of the censor times and it will be automatically calculated.} \item{You
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#' can directly specify the survivor function by providing a list with two
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#' numeric vectors called \code{x} and \code{y}. They should be of the same
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#' length and correspond to each point. It is assumed that previous to the
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#' first value in \code{y} the \code{y} value is 1.0; and that the function
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#' you provide is a right-continuous step function.} \item{You can provide a
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#' function from \code{\link[stats]{stepfun}}. Note that this only supports
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#' functions where \code{right = FALSE} (default), and that the first y value
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#' (corresponding to y before the first x value) will be to set to 1.0
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#' regardless of what is specified.}
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#'
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#' }
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#' @param parallel A logical indicating whether multiple cores should be
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#' utilized when calculating the error. Available as an option because it's
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#' been observed that using Java's \code{parallelStream} can be unstable on
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#' some systems. Default value is \code{TRUE}; only set to \code{FALSE} if you
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#' get strange errors while predicting.
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#'
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#' @export
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#' @references Section 4.2 of Ishwaran H, Gerds TA, Kogalur UB, Moore RD, Gange
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#' SJ, Lau BM (2014). “Random Survival Forests for Competing Risks.”
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#' Biostatistics, 15(4), 757–773. doi:10.1093/ biostatistics/kxu010.
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#'
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#' @examples
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#' data <- data.frame(delta=c(1,1,0,0,2,2), T=1:6, x=1:6)
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#'
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#' model <- train(CR_Response(delta, T) ~ x, data, ntree=100, numberOfSplits=0, mtry=1, nodeSize=1)
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#'
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#' newData <- data.frame(delta=c(1,0,2,1,0,2), T=1:6, x=1:6)
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#' predictions <- predict(model, newData)
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#'
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#' scores <- integratedBrierScore(CR_Response(data$delta, data$T), predictions, 1, 6.0)
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#'
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integratedBrierScore <- function(responses, predictions, event, time, censoringDistribution = NULL, parallel = TRUE){
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if(length(responses$eventTime) != length(predictions)){
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stop("Length of responses and predictions must be equal.")
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}
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java.censoringDistribution <- NULL
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if(!is.null(censoringDistribution)){
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java.censoringDistribution <- processCensoringDistribution(censoringDistribution)
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java.censoringDistribution <- .object_Optional(java.censoringDistribution)
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}
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else{
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java.censoringDistribution <- .object_Optional(NULL)
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}
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predictions.java <- lapply(predictions, function(x){return(x$javaObject)})
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predictions.java <- convertRListToJava(predictions.java)
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errors <- .jcall(.class_CompetingRiskUtils, "[D", "calculateIBSError",
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responses$javaObject,
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predictions.java,
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java.censoringDistribution,
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as.integer(event),
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time,
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parallel)
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return(errors)
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} |