Variable selection

The purpose of this vignette is to show how to select variables from SDeMo models.

using SpeciesDistributionToolkit
using CairoMakie
using PrettyTables

We will work on the demo data:

X, y = SDeMo.__demodata()

([9.200000000000001 8.1 15.5 6.6000000000000005 7.2 9.8 17.0 6.7 12.9 8.6 17.0 16.6 16.8 13.9 12.600000000000001 12.8 8.8 7.800000000000001 15.9 9.1 15.4 10.100000000000001 7.9 16.900000000000002 16.7 9.0 8.4 16.8 16.6 14.200000000000001 11.3 15.4 16.900000000000002 12.8 9.200000000000001 6.0 13.200000000000001 11.4 15.700000000000001 6.9 15.100000000000001 10.600000000000001 15.5 13.0 16.400000000000002 17.1 15.0 10.8 9.1 13.200000000000001 6.1000000000000005 7.4 14.5 16.8 16.7 8.1 8.5 17.0 15.9 15.9 16.900000000000002 17.1 15.8 7.300000000000001 8.700000000000001 14.4 14.0 11.600000000000001 16.900000000000002 16.3 16.0 12.8 13.8 16.0 12.0 8.8 17.0 12.5 16.900000000000002 16.6 16.1 10.200000000000001 9.9 5.9 7.1000000000000005 12.5 16.900000000000002 8.5 16.2 10.700000000000001 13.100000000000001 16.8 7.800000000000001 8.6 9.600000000000001 11.200000000000001 16.5 17.0 14.8 16.400000000000002 13.5 13.3 15.3 16.6 16.8 11.9 9.700000000000001 8.200000000000001 13.5 17.1 14.200000000000001 5.1000000000000005 10.4 9.600000000000001 17.1 11.200000000000001 16.8 9.1 8.9 14.5 11.600000000000001 17.3 6.800000000000001 16.5 10.3 9.1 14.0 16.7 6.300000000000001 17.0 17.0 14.8 13.9 7.300000000000001 9.600000000000001 11.3 15.8 11.100000000000001 16.900000000000002 16.6 17.1 12.700000000000001 11.5 16.7 8.700000000000001 6.6000000000000005 8.9 9.700000000000001 16.3 10.0 9.3 11.0 10.5 14.9 10.5 7.6000000000000005 12.5 13.8 9.200000000000001 15.3 13.100000000000001 9.3 8.3 14.100000000000001 7.6000000000000005 15.200000000000001 10.3 16.8 9.0 9.5 10.100000000000001 16.3 13.4 17.1 10.0 16.2 16.2 8.3 14.700000000000001 16.2 6.9 10.700000000000001 17.0 17.0 14.4 16.3 15.8 16.400000000000002 17.0 15.0 10.700000000000001 10.600000000000001 8.8 16.5 9.200000000000001 16.3 12.700000000000001 13.4 15.700000000000001 12.600000000000001 10.600000000000001 16.900000000000002 16.2 6.6000000000000005 7.9 14.200000000000001 16.400000000000002 16.8 16.900000000000002 16.8 13.9 17.2 5.800000000000001 16.8 14.9 16.0 8.6 13.4 17.1 10.200000000000001 17.1 10.600000000000001 14.4 14.600000000000001 10.200000000000001 16.5 16.900000000000002 9.600000000000001 8.0 16.400000000000002 14.9 6.6000000000000005 10.0 15.8 10.700000000000001 8.200000000000001 11.4 9.4 16.0 10.5 9.5 11.700000000000001 10.5 15.8 8.3 15.8 11.5 12.4 13.4 15.600000000000001 13.0 10.700000000000001 11.700000000000001 12.5 16.7 12.9 10.0 16.900000000000002 14.5 16.8 16.7 8.9 16.400000000000002 16.900000000000002 15.0 15.5 7.1000000000000005 17.0 10.5 15.8 14.3 16.7 12.100000000000001 16.2 14.9 8.4 13.200000000000001 15.3 17.0 12.700000000000001 17.0 6.1000000000000005 14.200000000000001 9.8 16.8 15.200000000000001 11.0 13.9 17.2 10.600000000000001 9.9 7.9 16.5 15.100000000000001 16.0 17.0 15.8 10.4 13.600000000000001 14.200000000000001 9.200000000000001 16.7 15.100000000000001 16.400000000000002 9.8 16.900000000000002 10.0 13.0 12.5 16.5 16.400000000000002 16.900000000000002 10.9 16.8 15.9 14.5 16.900000000000002 16.1 9.3 16.8 12.0 16.1 12.100000000000001 17.1 10.100000000000001 10.600000000000001 10.100000000000001 16.5 7.4 7.6000000000000005 11.3 17.0 16.1 8.8 8.9 7.0 8.5 7.6000000000000005 10.5 14.0 11.100000000000001 5.9 16.900000000000002 16.7 16.6 17.0 13.9 12.600000000000001 16.7 6.800000000000001 10.0 16.2 16.0 10.600000000000001 16.6 14.4 7.7 7.6000000000000005 15.600000000000001 13.5 7.0 9.700000000000001 9.1 10.4 16.7 8.9 3.4000000000000004 16.5 16.6 16.1 10.3 16.6 17.0 14.700000000000001 16.8 7.800000000000001 9.8 7.9 15.8 16.2 10.3 17.1 9.1 10.700000000000001 17.0 9.4 16.6 14.700000000000001 16.900000000000002 16.8 16.900000000000002 16.2 17.1 17.0 5.2 17.0 8.9 14.600000000000001 10.700000000000001 11.0 6.9 16.7 16.7 9.4 11.600000000000001 15.5 13.600000000000001 15.4 14.8 8.3 11.5 15.3 16.6 17.1 8.700000000000001 16.400000000000002 16.5 16.7 13.5 17.0 16.1 17.0 15.9 14.600000000000001 13.5 10.5 15.9 11.9 16.1 14.5 13.8 9.200000000000001 8.9 14.5 17.0 14.100000000000001 13.4 10.8 16.2 16.5 16.900000000000002 11.3 15.200000000000001 8.5 6.6000000000000005 1.0 16.900000000000002 9.0 14.5 9.1 13.8 7.9 8.200000000000001 16.8 13.3 16.400000000000002 8.5 9.200000000000001 17.0 11.600000000000001 11.200000000000001 15.100000000000001 15.9 16.400000000000002 9.200000000000001 15.4 10.600000000000001 13.200000000000001 9.600000000000001 12.9 12.100000000000001 6.2 12.0 15.3 7.1000000000000005 11.0 13.600000000000001 16.3 9.700000000000001 12.5 14.4 7.0 12.3 16.900000000000002 10.700000000000001 16.400000000000002 12.3 17.1 17.0 8.8 11.5 15.3 16.6 13.0 9.3 9.3 14.5 9.0 11.700000000000001 17.0 12.8 14.4 8.8 15.700000000000001 14.0 16.400000000000002 14.9 8.0 7.9 16.5 9.3 16.400000000000002 8.9 9.4 16.0 16.8 16.900000000000002 6.9 11.600000000000001 12.8 12.9 16.8 8.200000000000001 8.9 9.600000000000001 12.9 16.2 14.200000000000001 15.0 16.7 10.700000000000001 8.200000000000001 7.4 8.9 8.700000000000001 11.100000000000001 6.9 14.4 17.0 17.0 15.8 14.0 14.0 10.5 16.5 8.1 9.5 7.2 13.700000000000001 10.700000000000001 13.8 14.0 14.9 10.8 9.0 14.9 8.0 14.5 16.3 6.1000000000000005 16.8 17.0 13.100000000000001 10.200000000000001 8.4 8.9 9.200000000000001 13.5 14.4 13.4 11.200000000000001 9.3 7.800000000000001 16.8 7.5 13.3 16.2 16.5 17.0 16.3 15.700000000000001 16.1 7.6000000000000005 10.0 11.100000000000001 14.200000000000001 8.5 16.5 7.5 12.0 7.800000000000001 14.0 9.4 10.4 16.400000000000002 16.3 10.5 16.5 15.5 16.900000000000002 15.200000000000001 14.8 8.3 15.5 7.6000000000000005 14.100000000000001 9.9 11.0 10.8 12.0 8.4 8.700000000000001 15.3 16.5 9.3 12.200000000000001 11.9 17.0 10.700000000000001 14.100000000000001 10.5 13.200000000000001 6.6000000000000005 10.8 8.6 9.0 9.5 16.5 9.3 11.4 10.0 6.7 10.4 8.8 9.3 16.400000000000002 3.7 16.0 9.600000000000001 15.4 16.900000000000002 11.9 10.700000000000001 7.7 8.5 16.8 13.8 9.8 11.4 15.600000000000001 7.1000000000000005 11.4 17.1 15.8 10.700000000000001 14.700000000000001 6.300000000000001 14.5 14.100000000000001 8.4 15.4 6.9 16.0 6.1000000000000005 10.100000000000001 16.900000000000002 15.8 13.600000000000001 10.3 8.200000000000001 12.700000000000001 12.700000000000001 14.5 16.6 17.1 14.4 9.4 14.9 15.5 11.200000000000001 16.1 8.6 15.700000000000001 9.3 9.8 4.1000000000000005 9.600000000000001 8.3 16.400000000000002 15.8 15.8 13.100000000000001 14.3 7.2 16.900000000000002 9.3 16.8 16.7 17.0 14.4 16.7 16.0 12.0 12.0 15.9 13.700000000000001 7.800000000000001 16.7 7.5 14.600000000000001 10.5 9.1 16.7 15.700000000000001 17.0 12.8 8.700000000000001 8.5 16.5 15.8 10.8 14.4 13.600000000000001 8.8 14.600000000000001 14.3 16.1 16.2 10.100000000000001 9.3 16.900000000000002 17.1 8.200000000000001 13.4 14.9 17.0 17.2 16.0 13.3 16.6 15.3 16.6 15.9 17.0 16.1 15.4 15.4 11.600000000000001 15.0 12.9 9.700000000000001 12.600000000000001 7.7 9.9 10.600000000000001 11.5 7.6000000000000005 15.4 9.4 10.600000000000001 14.9 6.2 10.5 15.3 14.100000000000001 14.5 8.4 16.5 17.0 12.8 16.3 11.200000000000001 10.600000000000001 12.5 8.4 9.4 10.100000000000001 16.2 15.9 10.0 16.900000000000002 11.5 15.9 12.8 16.3 14.8 7.5 8.1 15.0 4.2 11.9 12.100000000000001 14.9 13.8 10.200000000000001 9.9 7.300000000000001 14.700000000000001 16.7 15.0 13.4 13.4 9.3 16.3 9.5 3.3000000000000003 6.9 14.5 13.5 16.7 11.600000000000001 7.300000000000001 10.100000000000001 17.2 10.0 8.5 12.5 10.600000000000001 10.9 7.800000000000001 11.200000000000001 9.0 13.4 7.0 6.300000000000001 10.4 16.8 15.700000000000001 14.3 8.200000000000001 8.0 9.5 16.8 15.100000000000001 14.100000000000001 16.3 10.8 16.7 10.0 13.8 8.4 8.1 17.2 11.700000000000001 16.6 8.5 10.600000000000001 15.200000000000001 13.9 16.900000000000002 17.0 8.8 15.200000000000001 8.6 14.9 9.3 16.3 8.200000000000001 9.1 16.900000000000002 14.600000000000001 16.400000000000002 10.4 10.200000000000001 16.1 6.2 11.200000000000001 13.3 15.200000000000001 12.5 9.600000000000001 9.4 8.4 15.100000000000001 9.4 8.700000000000001 11.700000000000001 8.1 16.5 13.200000000000001 9.4 16.900000000000002 12.700000000000001 17.1 13.9 16.8 11.4 13.600000000000001 12.4 13.5 17.1 11.100000000000001 14.9 16.400000000000002 17.1 8.3 10.100000000000001 16.6 13.0 8.200000000000001 16.900000000000002 15.700000000000001 10.3 8.1 14.8 9.600000000000001 6.7 6.4 8.9 5.7 9.4 15.600000000000001 14.5 15.600000000000001 13.0 16.8 17.0 15.4 6.9 10.100000000000001 9.600000000000001 15.9 9.4 6.7 7.9 12.700000000000001 7.0 7.9 9.3 14.100000000000001 16.900000000000002 11.100000000000001 17.2 12.8 7.6000000000000005 15.700000000000001 16.900000000000002 16.0 12.3 15.700000000000001 15.3 7.6000000000000005 16.7 10.700000000000001 12.8 8.8 9.3 12.100000000000001 14.200000000000001 8.4 16.900000000000002 8.700000000000001 11.200000000000001 9.8 8.0 8.700000000000001 11.100000000000001 10.9 14.600000000000001 8.5 9.3 14.3 13.200000000000001 8.4 10.0 16.7 16.8 12.700000000000001 14.9 16.5 5.5 16.900000000000002 16.900000000000002 8.4 11.4 16.900000000000002 15.600000000000001 16.3 13.700000000000001 9.200000000000001 16.0 13.9 16.0 15.8 8.1 12.600000000000001 16.900000000000002 9.200000000000001 16.900000000000002 16.8 4.0 11.600000000000001 5.5 16.0 16.7 8.5 13.5 10.5 11.3 14.3 5.9 15.200000000000001 9.0 16.400000000000002 9.700000000000001 8.0 16.900000000000002 12.200000000000001 10.100000000000001 16.6 10.8 7.800000000000001 10.4 14.9 12.5 14.8 16.400000000000002 8.4 15.4 3.5 9.0 13.4 17.0 9.600000000000001 16.0 16.8 12.4 7.1000000000000005 9.200000000000001 11.3 15.4 9.0 8.5 16.900000000000002 14.4 9.9 16.900000000000002 17.1 15.0 11.100000000000001 15.100000000000001 15.0 16.6 15.0 11.700000000000001 13.8 16.900000000000002 9.5 15.5 16.5 16.1 10.9 9.1 12.9 13.4 17.2 16.7 13.600000000000001 16.8 12.4 9.200000000000001 13.5 6.7 17.2 11.3 16.900000000000002 12.100000000000001 7.800000000000001 7.0 7.6000000000000005 16.900000000000002 8.9 11.100000000000001 8.8 10.0 15.4 11.0 9.0 12.100000000000001 10.3 8.6 16.7 8.4 7.300000000000001 16.1 12.0 10.0 12.600000000000001 16.900000000000002 13.200000000000001 16.8 13.8 16.7 16.900000000000002 7.6000000000000005 17.0 8.200000000000001 12.3 17.1 15.4 14.600000000000001 15.100000000000001 11.100000000000001 12.0 16.8 1.8 8.1 6.800000000000001 8.3 17.0 15.3 13.5 12.3 12.200000000000001 9.700000000000001 10.4 11.700000000000001 16.6 9.0 16.6 16.400000000000002 17.2 9.4 10.700000000000001 9.1 16.900000000000002 9.3 8.700000000000001 16.7 8.8 15.0 14.8 17.0 10.200000000000001 8.200000000000001 13.9 11.8 15.700000000000001 8.6 16.5 13.700000000000001 12.700000000000001 6.6000000000000005 14.700000000000001 8.6 13.5 11.100000000000001 16.6 16.900000000000002 13.5 15.9 7.800000000000001 15.600000000000001 10.4 13.8 16.8 5.0 16.5 15.100000000000001 15.3 15.9 8.4 15.200000000000001 14.9 15.600000000000001 12.600000000000001 10.3 7.6000000000000005 15.8 16.8 16.2 14.700000000000001 11.100000000000001 7.300000000000001 13.100000000000001 13.8 13.700000000000001 12.600000000000001 17.1 15.9 14.5 11.600000000000001 10.700000000000001 17.1 16.3 15.200000000000001 16.400000000000002 14.4 10.0 12.200000000000001 15.200000000000001 14.700000000000001 9.0 12.600000000000001 13.8 15.200000000000001 7.7 9.4 9.600000000000001 16.900000000000002 11.200000000000001 11.9 16.6 8.1 9.8 9.700000000000001 8.700000000000001 7.1000000000000005 13.700000000000001 16.7 8.200000000000001 17.1 14.3 8.8 16.2 15.9 10.100000000000001 14.8 16.400000000000002 10.9 8.700000000000001 9.9 14.3 16.8 9.9 17.1 16.900000000000002 13.4 14.100000000000001 17.0 9.3 16.0 15.600000000000001 17.0 15.4 14.5 15.8 14.100000000000001 6.800000000000001 15.600000000000001 9.0 14.3 9.3 15.700000000000001 12.5 12.100000000000001 12.600000000000001 15.200000000000001 7.2 7.1000000000000005 11.8 16.8 10.100000000000001 15.700000000000001 5.800000000000001 16.3 17.2 11.4 6.300000000000001 7.5 15.0 15.9 12.9 10.9 7.300000000000001 15.600000000000001 15.3 7.6000000000000005 8.4 15.200000000000001 10.9 15.600000000000001 5.300000000000001 11.9 16.3 16.900000000000002 13.3 16.8 11.600000000000001 9.8 16.8 16.8 7.9 11.8 11.9 13.5 16.8 9.9 16.5 15.9 17.0 9.700000000000001 15.600000000000001 11.0 16.0 10.100000000000001 11.5 17.2 17.1 9.5 11.0 10.3 7.4 15.0 14.4 13.3 14.600000000000001 9.600000000000001 11.9 13.8 16.3 15.600000000000001 7.1000000000000005 6.1000000000000005 15.200000000000001 7.300000000000001 9.600000000000001 12.600000000000001 8.3 9.3 16.2 17.2 14.9 16.7 12.100000000000001 14.200000000000001 16.5 14.100000000000001 11.200000000000001 13.100000000000001 4.7 9.4 17.0 10.9 11.0 5.800000000000001 10.700000000000001 17.0 16.900000000000002 7.800000000000001 16.900000000000002 16.2 16.5 16.8 13.200000000000001 17.1 11.0 11.200000000000001 9.200000000000001 16.0 17.0 13.9 11.8 16.3 12.100000000000001 16.400000000000002 15.3 10.9 8.5 13.9 9.0 14.700000000000001 5.300000000000001 12.200000000000001 16.7 7.4 16.2 14.4 4.1000000000000005 9.3 8.700000000000001 15.200000000000001 8.8 7.6000000000000005 15.700000000000001 9.4 16.7 7.6000000000000005 16.8 16.5 16.400000000000002 17.0 9.200000000000001 16.5 7.4 15.4 9.600000000000001 16.900000000000002 16.900000000000002 6.2 17.1 17.0 16.7 9.4 16.900000000000002 9.600000000000001 14.4 16.8 13.600000000000001 9.3 11.9 16.1 13.200000000000001 16.8 16.7 16.2 14.600000000000001 5.1000000000000005 10.100000000000001 9.1 11.700000000000001 12.600000000000001 16.3 15.5 9.4 8.4 10.100000000000001 11.0 13.9 9.600000000000001 12.700000000000001 12.0 10.100000000000001 13.600000000000001 14.3 8.8 6.9 11.700000000000001 16.400000000000002 10.700000000000001 8.8 8.1 11.9 9.0 7.9 14.700000000000001 16.900000000000002 16.0 10.700000000000001 11.5 16.6 13.9 15.600000000000001 12.9 16.400000000000002 16.7 14.8 15.200000000000001 9.0 16.900000000000002 5.9 16.7 15.3 16.400000000000002 13.5 17.0 8.6 6.300000000000001 7.2 15.0 10.4 12.600000000000001 17.0 14.0 9.200000000000001 15.3 16.8 10.700000000000001 16.6 15.100000000000001 17.1 13.9 9.0 7.2 8.1 16.1 10.9 15.9 14.100000000000001 8.0 11.3 7.5 16.6 13.9; 4.2 4.1000000000000005 3.5 3.8000000000000003 4.3 3.4000000000000004 3.4000000000000004 3.9000000000000004 2.8000000000000003 3.9000000000000004 3.7 3.4000000000000004 3.7 4.1000000000000005 4.2 4.3 4.1000000000000005 4.1000000000000005 3.7 2.7 3.4000000000000004 4.2 4.2 2.9000000000000004 3.3000000000000003 4.0 4.0 4.0 4.1000000000000005 3.6 4.0 4.0 3.1 3.5 4.1000000000000005 3.8000000000000003 3.6 3.3000000000000003 3.9000000000000004 3.7 4.1000000000000005 4.2 2.6 2.9000000000000004 4.2 3.8000000000000003 3.9000000000000004 4.0 3.3000000000000003 4.2 4.3 3.6 3.9000000000000004 3.7 2.5 3.9000000000000004 3.9000000000000004 3.6 3.9000000000000004 3.3000000000000003 3.0 4.0 2.7 4.2 3.5 2.6 3.9000000000000004 3.9000000000000004 3.4000000000000004 3.2 3.3000000000000003 3.9000000000000004 2.6 3.8000000000000003 4.0 4.3 3.4000000000000004 4.1000000000000005 3.0 3.7 2.7 3.4000000000000004 4.2 4.0 3.5 4.1000000000000005 3.2 3.5 4.1000000000000005 3.6 4.2 3.1 3.9000000000000004 3.7 4.3 4.2 3.2 3.7 3.9000000000000004 2.7 3.6 3.5 3.9000000000000004 4.2 2.5 4.2 4.2 4.1000000000000005 4.0 3.8000000000000003 3.7 3.7 4.0 3.8000000000000003 3.8000000000000003 3.4000000000000004 2.5 4.0 4.2 4.1000000000000005 3.8000000000000003 3.6 3.9000000000000004 3.8000000000000003 3.8000000000000003 4.3 3.6 3.0 4.2 3.7 3.7 4.4 4.2 3.5 4.3 3.6 3.4000000000000004 4.2 3.7 2.6 3.9000000000000004 3.3000000000000003 3.5 3.5 4.3 4.1000000000000005 4.2 4.0 3.9000000000000004 4.0 4.4 4.0 4.3 3.9000000000000004 4.3 4.0 3.2 3.9000000000000004 3.8000000000000003 4.2 4.2 4.0 3.9000000000000004 3.9000000000000004 4.0 4.0 3.7 3.0 3.9000000000000004 3.8000000000000003 4.1000000000000005 3.2 4.0 4.1000000000000005 4.3 3.1 2.7 4.0 3.4000000000000004 3.9000000000000004 4.0 3.9000000000000004 3.9000000000000004 3.5 4.2 3.2 2.5 4.1000000000000005 3.8000000000000003 4.3 4.1000000000000005 3.7 4.0 3.2 4.1000000000000005 3.2 3.8000000000000003 4.2 3.3000000000000003 4.2 4.3 3.7 2.6 4.1000000000000005 4.0 3.5 3.9000000000000004 3.1 3.1 3.3000000000000003 3.9000000000000004 3.6 3.9000000000000004 3.1 3.4000000000000004 2.6 4.2 2.7 3.7 4.2 3.8000000000000003 4.3 3.3000000000000003 4.2 2.8000000000000003 4.0 3.4000000000000004 4.3 3.7 3.3000000000000003 3.9000000000000004 4.2 4.0 3.8000000000000003 4.1000000000000005 4.2 4.1000000000000005 4.0 3.3000000000000003 2.6 4.3 4.1000000000000005 3.6 3.9000000000000004 4.2 2.9000000000000004 2.7 3.9000000000000004 4.4 3.2 3.3000000000000003 4.0 3.2 4.4 2.5 3.9000000000000004 3.8000000000000003 3.1 3.0 3.3000000000000003 3.6 4.1000000000000005 3.9000000000000004 3.4000000000000004 4.1000000000000005 3.9000000000000004 4.0 3.8000000000000003 4.3 2.7 3.7 2.5 4.0 3.1 4.3 3.7 3.6 3.2 3.7 3.5 3.5 3.8000000000000003 3.2 4.2 4.1000000000000005 3.8000000000000003 4.1000000000000005 4.1000000000000005 3.7 4.3 3.7 4.3 3.6 3.3000000000000003 3.7 3.7 2.7 4.3 4.2 4.0 3.6 4.1000000000000005 2.5 4.1000000000000005 4.3 4.2 3.4000000000000004 4.3 4.2 3.8000000000000003 4.1000000000000005 3.8000000000000003 3.4000000000000004 3.1 4.0 3.9000000000000004 3.0 3.0 4.0 4.1000000000000005 4.0 4.2 4.3 3.7 2.7 4.1000000000000005 3.4000000000000004 4.2 4.1000000000000005 4.2 3.9000000000000004 3.4000000000000004 3.1 3.9000000000000004 4.2 4.2 4.3 4.0 4.2 4.2 4.3 3.9000000000000004 3.5 3.9000000000000004 3.7 3.7 4.3 2.6 2.5 3.8000000000000003 4.0 2.6 3.8000000000000003 4.0 3.5 2.6 4.2 4.0 3.3000000000000003 3.7 4.2 3.7 4.1000000000000005 4.1000000000000005 4.1000000000000005 4.2 4.2 3.8000000000000003 3.4000000000000004 3.9000000000000004 4.2 3.9000000000000004 3.7 4.2 3.9000000000000004 3.9000000000000004 3.7 3.5 2.9000000000000004 2.9000000000000004 3.3000000000000003 3.7 4.0 4.3 3.8000000000000003 4.2 4.1000000000000005 4.3 3.3000000000000003 3.2 3.9000000000000004 2.6 4.1000000000000005 3.9000000000000004 4.1000000000000005 3.7 4.3 3.7 4.3 3.7 3.9000000000000004 2.5 3.9000000000000004 4.0 4.3 4.2 3.8000000000000003 3.2 4.1000000000000005 3.9000000000000004 4.1000000000000005 4.0 3.8000000000000003 3.7 3.5 3.1 4.1000000000000005 4.1000000000000005 3.6 4.0 2.8000000000000003 3.8000000000000003 3.9000000000000004 3.9000000000000004 3.0 4.4 4.1000000000000005 3.5 3.9000000000000004 4.2 4.2 4.2 4.3 3.2 3.7 3.4000000000000004 3.4000000000000004 4.1000000000000005 3.8000000000000003 3.7 3.3000000000000003 3.3000000000000003 3.9000000000000004 4.4 4.3 3.8000000000000003 3.1 3.9000000000000004 3.9000000000000004 3.7 4.1000000000000005 4.4 3.9000000000000004 4.1000000000000005 4.4 3.1 4.0 3.5 3.9000000000000004 4.4 4.1000000000000005 4.1000000000000005 3.5 3.0 4.1000000000000005 3.4000000000000004 4.1000000000000005 3.9000000000000004 4.2 4.0 3.4000000000000004 4.0 3.3000000000000003 3.4000000000000004 4.2 4.0 2.9000000000000004 4.0 4.2 4.3 2.5 3.9000000000000004 4.3 4.2 3.5 3.9000000000000004 4.3 3.8000000000000003 3.7 4.1000000000000005 4.4 3.2 2.6 3.2 4.4 4.0 2.7 4.1000000000000005 3.5 3.8000000000000003 3.2 4.2 3.6 3.9000000000000004 4.0 3.5 4.1000000000000005 4.0 4.3 2.7 4.3 3.3000000000000003 4.3 3.9000000000000004 4.1000000000000005 3.9000000000000004 3.0 3.7 4.1000000000000005 4.1000000000000005 4.3 3.7 4.4 4.2 3.5 4.2 4.2 4.0 4.4 3.7 3.9000000000000004 3.2 3.9000000000000004 4.0 4.0 4.0 4.3 3.9000000000000004 3.4000000000000004 3.8000000000000003 3.9000000000000004 3.7 3.1 3.7 4.3 3.5 4.2 4.0 4.3 2.7 3.4000000000000004 3.1 3.4000000000000004 4.0 3.9000000000000004 3.6 4.1000000000000005 3.0 3.4000000000000004 4.0 3.3000000000000003 3.7 4.0 3.6 4.3 4.1000000000000005 4.2 3.3000000000000003 4.2 2.9000000000000004 4.4 4.1000000000000005 3.9000000000000004 2.7 4.2 4.2 3.9000000000000004 3.8000000000000003 3.8000000000000003 3.7 3.9000000000000004 3.4000000000000004 3.5 4.0 3.8000000000000003 2.7 4.2 4.1000000000000005 3.9000000000000004 2.7 3.9000000000000004 3.9000000000000004 4.1000000000000005 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294.0 377.0 251.0 312.0 239.0 268.0 349.0 295.0 228.0 324.0 308.0 209.0 287.0 229.0 269.0 332.0 392.0 260.0 233.0 279.0 247.0 277.0 391.0 244.0 144.0 309.0 288.0 210.0 349.0 383.0 296.0 238.0 235.0 346.0 251.0 216.0 215.0 248.0 219.0 308.0 228.0 251.0 271.0 248.0 257.0 308.0 193.0 278.0 158.0 316.0 212.0 312.0 278.0 238.0 238.0 302.0 191.0 294.0 265.0 243.0 231.0 349.0 248.0 322.0 267.0 183.0 272.0 293.0 346.0 203.0 217.0 270.0 338.0 400.0 268.0 316.0 313.0 311.0 256.0 373.0 236.0 321.0 317.0 243.0 230.0 268.0 172.0 257.0 355.0 255.0 220.0 266.0 200.0 313.0 282.0 229.0 217.0 159.0 214.0 247.0 276.0 216.0 286.0 232.0 322.0 318.0 196.0 225.0 307.0 327.0 269.0 392.0 230.0 263.0 248.0 259.0 353.0 356.0 304.0 190.0 199.0 314.0 317.0 195.0 269.0 253.0 244.0 367.0 306.0 246.0 198.0 252.0 188.0 282.0 245.0 219.0 236.0 330.0 304.0 348.0 374.0 245.0 301.0 282.0 291.0 370.0 213.0 179.0 349.0 200.0 254.0 217.0 239.0 317.0 215.0 232.0 286.0 303.0 183.0 216.0 179.0 347.0 218.0 303.0 160.0 249.0 311.0 273.0 184.0 273.0 198.0 327.0 300.0 224.0 263.0 248.0 286.0 342.0 282.0 278.0 235.0 317.0 341.0 238.0 259.0 185.0 306.0 242.0 280.0 194.0 210.0 364.0 214.0 274.0 243.0 329.0 200.0 224.0 398.0 217.0 250.0 217.0 232.0 236.0 335.0 246.0 180.0 253.0 227.0 266.0 193.0 294.0 185.0 156.0 212.0 199.0 340.0 267.0 285.0 303.0 257.0 197.0 179.0 306.0 303.0 289.0 256.0 267.0 237.0 339.0 281.0 326.0 295.0 197.0 250.0 294.0 273.0 204.0 217.0 380.0 339.0 204.0 273.0 246.0 184.0 176.0 143.0 312.0 282.0 187.0 299.0 275.0 239.0 246.0 261.0 178.0 223.0 302.0 192.0 353.0 161.0 294.0 180.0 336.0 245.0 383.0 296.0 291.0 313.0 312.0 261.0 245.0 328.0 304.0 238.0 174.0 267.0 196.0 179.0 244.0 247.0 247.0 298.0 291.0 152.0 335.0 264.0 162.0 247.0 198.0 338.0 206.0 254.0], Bool[1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 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1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0])

We will start with the full set of BIOCLIM variables:

sdm = SDM(RawData, DecisionTree, X, y)

RawData → DecisionTree → P(x) ≥ 0.5

Variable selections proceeds (in most cases) through cross-validation, so we can pick an appropriate splitting scheme for our data (here, 10 random holdout sets):

folds = montecarlo(sdm; n = 10)

10-element Vector{Tuple{Vector{Int64}, Vector{Int64}}}:
 ([4, 5, 6, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 55, 57, 58, 59, 61, 62, 63, 64, 66, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 82, 83, 85, 86, 87, 88, 90, 91, 92, 93, 96, 97, 98, 99, 100, 102, 103, 104, 105, 106, 108, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 121, 122, 123, 124, 125, 126, 127, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 143, 144, 145, 146, 147, 148, 149, 151, 152, 153, 154, 155, 157, 158, 159, 160, 161, 162, 164, 165, 167, 168, 169, 170, 171, 172, 174, 175, 176, 177, 178, 179, 180, 181, 184, 185, 186, 188, 189, 190, 192, 194, 195, 196, 197, 199, 201, 202, 203, 204, 205, 206, 208, 209, 210, 212, 213, 214, 215, 217, 218, 219, 220, 221, 222, 224, 226, 227, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 248, 249, 250, 251, 253, 254, 255, 256, 257, 259, 260, 262, 264, 266, 267, 268, 269, 270, 271, 272, 273, 275, 277, 278, 279, 280, 281, 282, 284, 285, 286, 287, 288, 290, 291, 293, 294, 295, 297, 298, 299, 300, 301, 302, 303, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 334, 335, 336, 337, 339, 340, 341, 345, 347, 348, 349, 351, 352, 353, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 372, 373, 374, 376, 377, 378, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 393, 395, 396, 397, 398, 399, 400, 401, 403, 406, 407, 408, 409, 410, 411, 413, 414, 415, 416, 417, 418, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 435, 436, 437, 438, 440, 441, 442, 443, 444, 445, 447, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 463, 464, 465, 467, 468, 470, 472, 473, 474, 475, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488, 489, 490, 491, 492, 493, 495, 496, 499, 500, 501, 503, 506, 507, 508, 509, 510, 511, 512, 513, 514, 516, 517, 518, 520, 521, 522, 523, 524, 525, 526, 527, 528, 529, 531, 532, 534, 536, 537, 538, 540, 541, 544, 545, 546, 548, 549, 550, 552, 553, 554, 555, 556, 557, 558, 560, 561, 562, 563, 564, 565, 566, 567, 568, 569, 572, 574, 575, 576, 577, 578, 579, 580, 581, 582, 585, 586, 587, 588, 589, 591, 592, 593, 594, 595, 596, 597, 598, 599, 600, 601, 603, 604, 605, 606, 607, 609, 610, 612, 613, 614, 616, 617, 618, 621, 622, 623, 624, 625, 629, 630, 631, 632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 644, 645, 646, 647, 648, 649, 650, 651, 652, 653, 655, 656, 657, 658, 659, 661, 662, 663, 664, 665, 667, 668, 669, 670, 671, 672, 673, 674, 676, 677, 678, 679, 680, 681, 682, 683, 684, 685, 686, 689, 690, 692, 693, 694, 697, 698, 699, 701, 703, 704, 705, 706, 707, 708, 709, 710, 711, 712, 713, 714, 715, 717, 718, 719, 721, 722, 723, 724, 725, 727, 729, 730, 731, 733, 734, 735, 736, 737, 739, 740, 741, 742, 743, 745, 746, 747, 748, 749, 752, 753, 754, 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1413, 850, 209, 139, 768, 412, 799, 926, 185, 694, 240, 1094, 614, 853, 1351, 60, 353, 722, 416, 677, 740, 622, 258, 278, 939, 1146, 931, 411, 905, 970, 128, 1059, 739, 9, 1323, 1471, 344, 486, 1262, 641, 1350, 1163, 449, 431, 1092, 1151, 578, 1126, 553, 92, 590, 29, 793, 441, 710, 683, 289, 642, 855, 391, 909, 941, 160, 1218, 296, 1441, 882, 539, 396, 1199, 1454, 1339, 997, 714, 723, 1380, 781, 579, 1281, 393, 206, 266, 234, 1101, 902, 172, 1217, 981, 1438, 1266, 1096, 1210, 1484, 727, 689, 409, 1034, 183, 324, 1288, 1031, 218, 1038, 71, 1036, 1356, 999, 968, 124, 260, 116, 308, 1230, 451, 951, 1219, 246, 1287, 1297, 1315, 38, 1393, 973, 1322, 658, 582, 771, 346, 1373, 1216, 1131, 1293, 913, 459, 1367, 25, 161, 53, 1224, 14, 786, 701, 1155, 512, 1389, 329, 454, 1002, 381, 1103, 1282, 585, 259, 1150])

Re-training

Variable selection will retrain the model at the end of the process, so it can be used directly.

We can apply different variable selection steps, the first of which is stepwise variance inflation factor:

variables!(sdm, StrictVarianceInflationFactor{5.0})
variables(sdm)

5-element Vector{Int64}:
  4
  5
  7
 15
 19

There are routines for forward/backward selection. All of them accept a final argument that represents variables which must be included in the model. All variable selection strategies are of the type VariableSelectionStrategy.

variables!(sdm, AllVariables)
variables!(sdm, ForwardSelection, folds; included = [1, 12])
variables(sdm)

9-element Vector{Int64}:
  1
 12
  3
  9
 11
  4
  5
  2
 16

Note that there is a keyword, verbose, which can be used to print the progress of variable selection. All methods using stepwise selection perform cross-validation and optimize the MCC.

Related documentation

SDeMo.VariableSelectionStrategy Type

VariableSelectionStrategy

This is an abstract type to which all variable selection types belong. The variable selection methods should define a method for variables!, whose first argument is a model, and the second argument is a selection strategy. The third and fourth positional arguments are, respectively, a list of variables to be included, and the folds to use for cross-validation. They can be omitted and would default to no default variables, and k-fold cross-validation.

source

SDeMo.ForwardSelection Type

ForwardSelection

Variables are included one at a time until the performance of the models stops improving.

source

SDeMo.BackwardSelection Type

ForwardSelection

Variables are removed one at a time until the performance of the models stops improving.

source

SDeMo.AllVariables Type

AllVariables

All variables in the training dataset are used. Note that this also crossvalidates and trains the model.

source

SDeMo.VarianceInflationFactor Type

VarianceInflationFactor{N}

Removes variables one at a time until the largest VIF is lower than N (a floating point number), or the performancde of the model stops increasing. Note that the resulting set of variables may have a largest VIF larger than the threshold. See StrictVarianceInflationFactor for an alternative.

source

SDeMo.StrictVarianceInflationFactor Type

StrictVarianceInflationFactor{N}

Removes variables one at a time until the largest VIF is lower than N (a floating point number). By contrast with VarianceInflationFactor, this approach to variable selection will not cross-validate the model, and might result in a model that is far worse than any other variable selection technique.

source