plot.dsm.var: Create plots of abundance uncertainty

In dsm: Density Surface Modelling of Distance Sampling Data

Create plots of abundance uncertainty

Description

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(s)

David L. Miller

See Also

dsm_var_prop, dsm_var_gam, dsm_var_movblk

dsm documentation built on Aug. 21, 2022, 1:07 a.m.