Plotting models

The SDMs are integrated with the Makie plotting package.

using SpeciesDistributionToolkit
using CairoMakie

We will use the demonstration data from the SDeMo package:

model = SDM(RawData, NaiveBayes, SDeMo.__demodata()...)

❎  RawData → NaiveBayes → P(x) ≥ 0.5 🗺️

Plotting instances

f = Figure()
ax = Axis(f[1,1], aspect=DataAspect())
scatter!(ax, model)
current_figure()

This can be coupled with information about the model itself, to provide more interesting visualisations:

f = Figure()
ax = Axis(f[1,1], aspect=DataAspect())
scatter!(ax, model, color=labels(model))
current_figure()

Note that models also implement the occurences interface, so we can easily split presence and absences from the model.

f = Figure()
ax = Axis(f[1,1], aspect=DataAspect())
scatter!(ax, presences(model), color=:white, strokecolor=:orange, strokewidth=1, label="Presences")
scatter!(ax, absences(model), color=:white, strokecolor=:teal, strokewidth=0.5, markersize=5, label="Absences")
axislegend(ax, position=:lt)
hidespines!(ax)
hidedecorations!(ax)
current_figure()

The models currently do not have a field for the species name, but this will be part of a future release. If you need to specify the species name from a model, for now you need to use Occurrences(model; name="Sitta whiteheadi").

Model diagnostic plots

These plots require a trained model:

train!(model)

☑️  RawData → NaiveBayes → P(x) ≥ 0.034 🗺️

Coming soon

These plots will be included in a future release of SDeMo- stay tuned!