60 lines
2.2 KiB
Text
60 lines
2.2 KiB
Text
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% Generated by roxygen2: do not edit by hand
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% Please edit documentation in R/cr_components.R
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\name{CompetingRiskSplitFinders}
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\alias{CompetingRiskSplitFinders}
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\alias{GrayLogRankSplitFinder}
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\alias{LogRankSplitFinder}
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\title{Competing Risk Split Finders}
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\usage{
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GrayLogRankSplitFinder(events, eventsOfFocus = NULL)
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LogRankSplitFinder(events, eventsOfFocus = NULL)
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}
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\arguments{
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\item{events}{A vector of integers specifying which competing risk events
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should be focused on when determining differences. Currently, equal weights
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will be assigned to all included groups.}
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\item{eventsOfFocus}{The split finder will only maximize differences
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between the two groups with respect to these specified events. Default is
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\code{NULL}, which will cause the split finder to focus on all events
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included in \code{events}.}
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}
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\value{
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An internal rJava Java object used in \code{\link{train}}.
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}
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\description{
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Creates a SplitFinder rJava Java object, which is then used internally when
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training a competing risk random forest. The split finder is responsible for
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finding the best split according to the logic of the split finder.
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}
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\details{
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These split finders require that the response be \code{\link{CR_Response}}.
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The user only needs to pass this object into \code{\link{train}} as the
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\code{splitFinder} parameter.
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Roughly speaking, the Gray log-rank split finder looks at
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differences between the cumulative incidence functions of the two groups,
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while the plain log-rank split finder look at differences between the
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cause-specific hazard functions. See the references for a more detailed
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discussion.
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}
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\note{
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The Gray log-rank split finder \strong{requires} that the response
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include the censoring time.
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}
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\examples{
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splitFinder <- GrayLogRankSplitFinder(1:2)
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splitFinder <- LogRankSplitFinder(1:2)
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}
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\references{
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Kogalur, U., Ishwaran, H. Random Forests for Survival,
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Regression, and Classification: A Parallel Package for a General
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Implemention of Breiman's Random Forests: Theory and Specifications. URL
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https://kogalur.github.io/randomForestSRC/theory.html#section8.2
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Ishwaran, H., et. al. (2014) Random survival forests for competing risks,
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Biostatistics (2014), 15, 4, pp. 757–773
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}
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