plot.predcoca function

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 class '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(s)

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)

## 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)