Note that the prediction data set must have x and y columns
even if these were not used in the model.
# S3 method for dsm.var
plot(x, poly = NULL, limits = NULL, breaks = NULL,
legend.breaks = NULL, xlab = "x", ylab = "y",
observations = TRUE, plot = TRUE, boxplot.coef = 1.5,
x.name = "x", y.name = "y", gg.grad = NULL, ...)a dsm.var object
a list or data.frame with columns x and
y, which gives the coordinates of a polygon to draw. It may
also optionally have a column group, if there are many
polygons.
limits for the fill colours
breaks for the colour fill
breaks as they should be displayed
label for the x axis
label for the y axis
should observations be plotted?
actually plot the map, or just return a ggplot2 object?
control trimming (as in summary.dsm.var), only
has an effect if the bootstrap file was saved.
name of the variable to plot as the x axis.
name of the variable to plot as the y axis.
optional ggplot gradient object.
any other arguments
a plot
In order to get plotting to work with dsm.var.prop and
dsm.var.gam, one must first format the data correctly since
these functions are designed to compute very general summaries. One summary
is calculated for each element of the list pred supplied to
dsm.var.prop and dsm.var.gam.
For a plot of uncertainty over a prediction grid, pred (a
data.frame), say, we can create the correct format by simply using
pred.new <- split(pred,1:nrow(pred)).
dsm.var.prop, dsm.var.gam, dsm.var.movblk