Biplots for predictive co-correspondence analysis

Produces biplots of the response and predictor from the results of a predictive co-correspondence analysis.

# S3 method for predcoca
plot(x,
    which = "response",
    choices = 1:2,
    display = c("species", "sites"),
    type,
    xlim = NULL,
    ylim = NULL,
    main = "",
    sub = "",
    ylab, xlab,
    ann = par("ann"),
    axes = TRUE,
    ...)

Arguments

x	an object of class "predcoca", the result of a call to coca.
which	character; should the response or predictor scores be plotted. Can be specified in several ways: response choices are one from c("y", "Y", "y1", "response"); predictor choices are one from c("x", "X", "y2", "predictor").
choices	a vector of length 2 indicating which predictive CoCA axes to plot.
display	which sets of scores are drawn. See scores.predcoca.
type	one of "points", "text", or "none". Determines how the site and species scores are displayed. If type = "points", scores are plotted as points. If type = "text", then the row names of the scores matrices are plotted. If type = "none", then the scores are not plotted.
xlim, ylim	limits for the x and y axes. If non supplied, suitable limits will be determined from the data.
xlab, ylab	labels for the x and y axes. If non supplied suitable labels are formed from the result object.
main, sub	the main and sub titles for the plot.
ann	logical, if TRUE plots are annotated and not if FALSE, currently ignored.
axes	a logical value indicating whether both axes should be drawn on the plot.
...	other graphical parameters as in 'par' may also be passed as arguments.

References

Ter Braak, C.J.F and Schaffers, A.P. (2004) Co-Correspondence Analysis: a new ordination method to relate two community compositions. Ecology 85(3), 834--846

Author

Gavin L. Simpson.

See also

coca, plot.default

Examples

## predictive CoCA
data(beetles)
data(plants)

## log transform the beetle data
beetles <- log(beetles + 1)

## predictive CoCA using SIMPLS and formula interface
bp.pred <- coca(beetles ~ ., data = plants)
#> 
#> Removed some species that contained no data in: beetles, plants 

## draw the plot for the response scores
plot(bp.pred)


## plot of both
layout(matrix(1:2, ncol = 2))
plot(bp.pred, which = "response", main = "Beetles")
plot(bp.pred, which = "predictor", main = "Plants")

layout(1)