partition.crit {gclus} | R Documentation |
Applies the function gfun
to each group of x and y values
and combines the results using the function cfun
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 |
cfun |
combines the values returned by |
... |
arguements are passed to |
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
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.