# colpairs: Applies a function to all pairs of columns In gclus: Clustering Graphics

## Description

Given an nxp matrix `m` and a function `f`, returns the pxp matrix got by applying `f` to all pairs of columns of `m` .

## Usage

 `1` ```colpairs(m, f, diag = 0, na.omit = FALSE, ...) ```

## Arguments

 `m` a matrix `f` a function of two vectors, which returns a single result. `diag` if supplied, this value is placed on the diagonal of the result. `na.omit` If `TRUE`, rows with missing values are omitted for each pair of columns. `...` argments are passed to `f`.

## Value

a matrix matrix got by applying `f` to all pairs of columns of `m` .

## Author(s)

Catherine B. Hurley

`gave`, `partition.crit`, `order.single`,`order.endlink`
 ``` 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``` ```data(state) state.m <- colpairs(state.x77, function(x,y) cor.test(x,y,"two.sided","kendall")\$estimate, diag=1) state.col <- dmat.color(state.m) # This is equivalent to state.m <- cor(state.x77,method="kendall") layout(matrix(1:2,nrow=1,ncol=2)) cparcoord(state.x77, panel.color= state.col) # Get rid of the panels with lots of line crossings (yellow) by reorderings cparcoord(state.x77, order.endlink(state.m), state.col) layout(matrix(1,1)) # m is a homogeneity measure of each pairwise variable plot m <- -colpairs(scale(state.x77), gave) o<- order.single(m) pcols = dmat.color(m) # Color panels by level of m and reorder variables so that # pairs with high m are near the diagonal. cpairs(state.x77,order=o, panel.colors=pcols) # In this case panels showing either of Area or Population # exhibit the most clumpiness because these variables # are skewed. ```