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\name{similarity_measures_regression}
\alias{similarity_measures_regression}
\alias{edist}
\alias{msdist}
\alias{rmsdist}
\alias{madist}
\alias{qadist}
\alias{cprob}
\alias{ccc}
\alias{pcc}
\alias{cosine}
\alias{rbfkernel}
\alias{tanimoto}
\title{Similarity Measure Infrastructure for Stability Assessment with Numerical Responses}
\description{
Functions that provide objects with functionality used by
\code{\link{stability}} to measure the similarity between numeric
predictions of two results in regression problems.
}
\usage{
edist()
msdist()
rmsdist()
madist()
qadist(p = 0.95)
cprob(kappa = 0.1)
rbfkernel()
tanimoto()
cosine()
ccc()
pcc()
}
\arguments{
\item{p}{A numeric value between 0 and 1 specifying the probability to which
the sample quantile of the absolute distance between the predictions is
computed.}
\item{kappa}{A positive numeric value specifying the upper limit of the
absolute distance between the predictions to which the coverage probability
is computed.}
}
\details{
The similarity measure functions provide objects that include functionality
used by \code{\link{stability}} to measure the similarity between numeric
predictions of two results in regression problems.
The \code{edist} (euclidean distance), \code{msdist} (mean squared distance),
\code{rmsdist} (root mean squared distance), \code{madist} (mean absolute
distance) and \code{qadist} (quantile of absolute distance) functions
implement scale-variant distance measures that are unbounded.
The \code{cprob} (coverage probability), \code{rbfkernel} (gaussian radial
basis function kernel), \code{tanimoto} (tanimoto coefficient) and
\code{cosine} (cosine similarity) functions implement scale-variant distance
measures that are bounded.
The \code{ccc} (concordance correlation coefficient) and \code{pcc} (pearson
correlation coefficient) functions implement scale-invariant distance
measures that are bounded between 0 and 1.
}
\seealso{\code{\link{stability}}}
\examples{
\donttest{
set.seed(0)
library("partykit")
airq <- subset(airquality, !is.na(Ozone))
m1 <- ctree(Ozone ~ ., data = airq[sample(1:nrow(airq), replace = TRUE),])
m2 <- ctree(Ozone ~ ., data = airq[sample(1:nrow(airq), replace = TRUE),])
p1 <- predict(m1)
p2 <- predict(m2)
## euclidean distance
m <- edist()
m$measure(p1, p2)
## mean squared distance
m <- msdist()
m$measure(p1, p2)
## root mean squared distance
m <- rmsdist()
m$measure(p1, p2)
## mean absolute istance
m <- madist()
m$measure(p1, p2)
## quantile of absolute distance
m <- qadist()
m$measure(p1, p2)
## coverage probability
m <- cprob()
m$measure(p1, p2)
## gaussian radial basis function kernel
m <- rbfkernel()
m$measure(p1, p2)
## tanimoto coefficient
m <- tanimoto()
m$measure(p1, p2)
## cosine similarity
m <- cosine()
m$measure(p1, p2)
## concordance correlation coefficient
m <- ccc()
m$measure(p1, p2)
## pearson correlation coefficient
m <- pcc()
m$measure(p1, p2)
}
}
\keyword{stability}
\keyword{similariy}
\keyword{measures}
|
