partition.crit: Combines the results of appplying an index to each group of...
In gclus: Clustering Graphics

Description Usage Arguments Details Value Author(s) References See Also Examples

Applies the function gfun to each group of x and y values and combines the results using the function cfun

1	partition.crit(x, y, groups, gfun = gave, cfun = sum, ...)

`x`	is a numeric vector.
`y`	is a numeric vector.
`groups`	is a vector of group memberships.
`gfun`	is applied to the `x` and `y` data in each group.
`cfun`	combines the values returned by `gfun`.
`...`	arguements are passed to `gfun`.

The function gfun is applied to each group of x and y values. The function cfun is applied to the vector or matrix of gfun results.

The result of applying cfun.

Catherine B. Hurley

See Gordon, A. D. (1999). Classification. Second Edition. London: Chapman and Hall / CRC

gave, colpairs, order.single

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.