Plot residuals for a submodel from a ubmsFit object, for multiple
posterior draws. By default, residuals are plotted against fitted values.
When the submodel has a binomial response (e.g., detection models), regular
residual plots are not typically informative. Instead, the residuals and
fitted values are divided into bins based on fitted value and the averages
are plotted. For a count response (e.g., Poisson), Pearson residuals are
calculated. To plot residuals against values of a particular covariate instead
of the fitted values, supply the name of the covariate (as a string) to the
covariate argument.
# S4 method for class 'ubmsFit'
plot_residuals(
object,
submodel,
covariate = NULL,
draws = 9,
nbins = NULL,
...
)A fitted model of class ubmsFit
Submodel to plot residuals for, for example "det"
If specified, plot residuals against values of a covariate.
Covariate name should be provided as a string. If NULL,
residuals are plotted against predicted values.
An integer indicating the number of posterior draws to use. Separate plots are generated for each draw, so this number should be relatively small. The default and maximum number of draws is the size of the posterior sample.
For submodels with a binomial response, manually set the number of bins to use
Currently ignored
A ggplot of residuals vs. fitted values or covariate values,
with one panel per posterior draw. For binned residual plots, the shaded area
represents plus/minus two standard deviations around the mean residual.
If the model is true, we would expect about 95
fall within this area.