Bivariate plot wrapper for distribution objectsSource:
Often there is an intention to display not only the predictions made with a SDM, but also the uncertainty of the prediction. Uncertainty be estimated either directly by the model or by calculating the variation in prediction values among a set of models.
In particular Bayesian engines can produce not only mean estimates of fitted responses, but also pixel-based estimates of uncertainty from the posterior such as the standard deviation (SD) or the coefficient of variation of a given prediction.
This function makes use of the
"biscale" R-package to create bivariate
plots of the fitted distribution object, allowing to visualize two variables
at once. It is mostly thought of as a convenience function to create such
bivariate plots for quick visualization.
Supported Inputs are either single trained Bayesian
with uncertainty or the output of an
ensemble() call. In both cases,
users have to make sure that
"yvar" are set
bivplot( mod, xvar = "mean", yvar = "sd", plot = TRUE, fname = NULL, title = NULL, col = "BlueGold", ... ) # S4 method for ANY,character,character,logical,ANY,ANY,character bivplot(mod,xvar,yvar,plot,fname,title,col,...)
DistributionModelor alternatively a
characterdenoting the value on the x-axis (Default:
characterdenoting the value on the y-axis (Default:
logicalindication of whether the result is to be plotted (Default:
characterspecifying the output filename a created figure should be written to.
Allows to respecify the title through a
characterstating the colour palette to use. Has to be either a predefined value or a vector of colours. See
Other engine specific parameters.
This function requires the biscale package to be installed. Although a work around without the package could be developed, it was not deemed necessary at this point. See also this gist.