% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cr_integratedBrierScore.R
\name{integratedBrierScore}
\alias{integratedBrierScore}
\title{Integrated Brier Score}
\usage{
integratedBrierScore(responses, predictions, event, time,
  censoringDistribution = NULL, parallel = TRUE)
}
\arguments{
\item{responses}{A list of responses corresponding to the provided
mortalities; use \code{\link{CR_Response}}.}

\item{predictions}{The predictions to be tested against.}

\item{event}{The event type for the error to be calculated on.}

\item{time}{\code{time} specifies the upper bound of the integral.}

\item{censoringDistribution}{Optional; if provided then weights are
  calculated on the errors. There are three ways to provide it - \itemize{
  \item{If you have all the censor times and just want to use a simple
  empirical estimate of the distribution, just provide a numeric vector of
  all of the censor times and it will be automatically calculated.} \item{You
  can directly specify the survivor function by providing a list with two
  numeric vectors called \code{x} and \code{y}. They should be of the same
  length and correspond to each point. It is assumed that previous to the
  first value in \code{y} the \code{y} value is 1.0; and that the function
  you provide is a right-continuous step function.} \item{You can provide a
  function from \code{\link[stats]{stepfun}}. Note that this only supports
  functions where \code{right = FALSE} (default), and that the first y value
  (corresponding to y before the first x value) will be to set to 1.0
  regardless of what is specified.}

  }}

\item{parallel}{A logical indicating whether multiple cores should be
utilized when calculating the error. Available as an option because it's
been observed that using Java's \code{parallelStream} can be unstable on
some systems. Default value is \code{TRUE}; only set to \code{FALSE} if you
get strange errors while predicting.}
}
\value{
A numeric vector of the Integrated Brier Score for each prediction.
}
\description{
Used to calculate the Integrated Brier Score, which for the competing risks
setting is the integral of the squared difference between each observed
cumulative incidence function (CIF) for each observation and the
corresponding predicted CIF. If the survivor function (1 - CDF) of the
censoring distribution is provided, weights can be calculated to account for
the censoring.
}
\examples{
data <- data.frame(delta=c(1,1,0,0,2,2), T=1:6, x=1:6)

model <- train(CR_Response(delta, T) ~ x, data, ntree=100, numberOfSplits=0, mtry=1, nodeSize=1)

newData <- data.frame(delta=c(1,0,2,1,0,2), T=1:6, x=1:6)
predictions <- predict(model, newData)

scores <- integratedBrierScore(CR_Response(data$delta, data$T), predictions, 1, 6.0)

}
\references{
Section 4.2 of Ishwaran H, Gerds TA, Kogalur UB, Moore RD, Gange
  SJ, Lau BM (2014). “Random Survival Forests for Competing Risks.”
  Biostatistics, 15(4), 757–773. doi:10.1093/ biostatistics/kxu010.
}