table.cont {ade4} | R Documentation

## Plot of Contingency Tables

### Description

presents a graph for viewing contingency tables.

### Usage

    table.cont(df, x = 1:ncol(df), y = 1:nrow(df),
        row.labels = row.names(df), col.labels = names(df),
        clabel.row = 1, clabel.col = 1, abmean.x = FALSE, abline.x = FALSE,
        abmean.y = FALSE, abline.y = FALSE, csize = 1, clegend = 0, grid = TRUE)

### Arguments

`df` | a data frame with only positive or null values
`x` | a vector of values to position the columns
`y` | a vector of values to position the rows
`row.labels` | a character vector for the row labels
`col.labels` | a character vetor for the column labels
`clabel.row` | a character size for the row labels
`clabel.col` | a character size for the column labels
`abmean.x` | a logical value indicating whether the column conditional means should be drawn
`abline.x` | a logical value indicating whether the regression line of y onto x should be plotted
`abmean.y` | a logical value indicating whether the row conditional means should be drawn
`abline.y` | a logical value indicating whether the regression line of x onto y should be plotted
`csize` | a coefficient for the square size of the values
`clegend` | if not NULL, a character size for the legend used with `par("cex")*clegend`
`grid` | a logical value indicating whether a grid in the background of the plot should be drawn

### Author(s)

Daniel Chessel chessel@biomserv.univ-lyon1.fr

### Examples

    data(chats)
    chatsw <- data.frame(t(chats))
    chatscoa <- dudi.coa(chatsw, scann = FALSE)
    par(mfrow = c(2,2))
    table.cont(chatsw, abmean.x = TRUE, csi = 2, abline.x = TRUE,
        clabel.r = 1.5, clabel.c = 1.5)
    table.cont(chatsw, abmean.y = TRUE, csi = 2, abline.y = TRUE,
        clabel.r = 1.5, clabel.c = 1.5)
    table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1],
        abmean.x = TRUE, csi = 2, abline.x = TRUE, clabel.r = 1.5, clabel.c = 1.5)
    table.cont(chatsw, x = chatscoa$c1[,1], y = chatscoa$l1[,1],
        abmean.y = TRUE, csi = 2, abline.y = TRUE, clabel.r = 1.5, clabel.c = 1.5)
    par(mfrow = c(1,1))

    ## Not run:
    data(rpjdl)
    w <- data.frame(t(rpjdl$fau))
    wcoa <- dudi.coa(w, scann = FALSE)
    table.cont(w, abmean.y = TRUE, x = wcoa$c1[,1], y = rank(wcoa$l1[,1]),
        csi = 0.2, clabel.c = 0, row.labels = rpjdl$lalab, clabel.r = 0.75)
    ## End(Not run)

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