table.cont: Plot of Contingency Tables

In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

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

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)

ade4 documentation built on May 2, 2019, 5:50 p.m.