Create plots of abundance uncertainty

Note that the prediction data set must have x and y columns even if these were not used in the model.

Usage

# S3 method for class '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,
  ...
)

Arguments

x: a dsm.var object
poly: 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: limits for the fill colours
breaks: breaks for the colour fill
legend.breaks: breaks as they should be displayed
xlab: label for the x axis
ylab: label for the y axis
observations: should observations be plotted?
plot: actually plot the map, or just return a ggplot2 object?
boxplot.coef: control trimming (as in summary.dsm.var), only has an effect if the bootstrap file was saved.
x.name: name of the variable to plot as the x axis.
y.name: name of the variable to plot as the y axis.
gg.grad: optional ggplot gradient object.
...: any other arguments

Value

a plot

Details

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)).

Author

David L. Miller