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 |

`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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
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.
``` |

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