\name{partition.crit} \alias{partition.crit} %- Also NEED an '\alias' for EACH other topic documented here. \title{ Combines the results of appplying an index to each group of observations } \description{ Applies the function \code{gfun} to each group of x and y values and combines the results using the function \code{cfun} } \usage{ partition.crit(x, y, groups, gfun = gave, cfun = sum, ...) } %- maybe also 'usage' for other objects documented here. \arguments{ \item{x}{is a numeric vector. } \item{y}{is a numeric vector. } \item{groups}{ is a vector of group memberships. } \item{gfun}{ is applied to the \code{x} and \code{y} data in each group. } \item{cfun}{ combines the values returned by \code{gfun}. } \item{\dots}{ arguements are passed to \code{gfun}. } } \details{ The function \code{gfun} is applied to each group of \code{x} and \code{y} values. The function \code{cfun} is applied to the vector or matrix of \code{gfun} results. } \value{ The result of applying \code{cfun}. } \references{ See Gordon, A. D. (1999). \emph{Classification}. Second Edition. London: Chapman and Hall / CRC } \author{ Catherine B. Hurley} \seealso{ \code{\link{gave}}, \code{\link{colpairs}}, \code{\link{order.single}}} \examples{ x <- runif(20) y <- runif(20) g <- rep(c("a","b"),10) partition.crit(x,y,g) data(bank) # m is a homogeneity measure of each pairwise variable plot m <- -colpairs(scale(bank[,-1]), partition.crit,gfun=gave,groups=bank[,1]) # Color panels by level of m and reorder variables so that # pairs with high m are near the diagonal. Panels shown # in pink have the highest amount of group homogeneity, as measured by # gave. cpairs(bank[,-1],order=order.single(m), panel.colors=dmat.color(m), gap=.3,col=c("purple","black")[bank[,"Status"]+1], pch=c(5,3)[bank[,"Status"]+1]) # Try a different measure m <- -colpairs(scale(bank[,-1]), partition.crit,gfun=diameter,groups=bank[,1]) cpairs(bank[,-1],order=order.single(m), panel.colors=dmat.color(m), gap=.3,col=c("purple","black")[bank[,"Status"]+1], pch=c(5,3)[bank[,"Status"]+1]) # Result is the same, in this case. } \keyword{multivariate } \keyword{cluster }