70 lines
2.9 KiB
Text
70 lines
2.9 KiB
Text
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/cr_integratedBrierScore.R
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\name{integratedBrierScore}
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\alias{integratedBrierScore}
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\title{Integrated Brier Score}
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\usage{
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integratedBrierScore(responses, predictions, event, time,
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censoringDistribution = NULL, parallel = TRUE)
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}
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\arguments{
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\item{responses}{A list of responses corresponding to the provided
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mortalities; use \code{\link{CR_Response}}.}
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\item{predictions}{The predictions to be tested against.}
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\item{event}{The event type for the error to be calculated on.}
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\item{time}{\code{time} specifies the upper bound of the integral.}
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\item{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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\item{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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\value{
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A numeric vector of the Integrated Brier Score for each prediction.
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}
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\description{
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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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\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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model <- train(CR_Response(delta, T) ~ x, data, ntree=100, numberOfSplits=0, mtry=1, nodeSize=1)
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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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scores <- integratedBrierScore(CR_Response(data$delta, data$T), predictions, 1, 6.0)
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}
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\references{
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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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