Visualize the density of the data over the environmental data

Based on a fitted model, plot the density of observations over the estimated variable and environmental space. Opposed to the partial and spartial functions, which are rather low-level interfaces, this function provides more detail in the light of the data. It is also able to contrast different variables against each other and show the used data.

Usage

partial_density(mod, x.var, df = FALSE, ...)

# S4 method for ANY,character
partial_density(mod, x.var, df = FALSE, ...)

Arguments

mod: A trained DistributionModel object. Requires a fitted model and inferred prediction.
x.var: A character indicating the variable to be investigated. Can be a vector of length 1 or 2.
df: logical if plotting data should be returned instead (Default: FALSE).
...: Other engine specific parameters.

Value

A ggplot2 object showing the marginal response in light of the data.

Details

This functions calculates the observed density of presence and absence points over the whole surface of a specific variable. It can be used to visually inspect the fit of the model to data.

Note

By default all variables that are not x.var are hold constant at the mean.

References

Warren, D.L., Matzke, N.J., Cardillo, M., Baumgartner, J.B., Beaumont, L.J., Turelli, M., Glor, R.E., Huron, N.A., Simões, M., Iglesias, T.L. Piquet, J.C., and Dinnage, R. 2021. ENMTools 1.0: an R package for comparative ecological biogeography. Ecography, 44(4), pp.504-511.

Examples

if (FALSE) {
 # Do a partial calculation of a trained model
 partial_density(fit, x.var = "Forest.cover")
 # Or with two variables
 partial_density(fit, x.var = c("Forest.cover", "bio01"))
}