Train presence absence models

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
options(knitr.table.format = "html")
library(SDMtune)
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"), pattern = "grd", full.names = TRUE)
predictors <- raster::stack(files)

Intro

All the previous articles are based on presence only methods, in this article you will learn how to train a presence absence model. The following examples are based on the Artificial Neural Networks method [@Venables2002], but you can adapt the code for any of the other supported methods.

Prepare the data for the analysis

We use the first 8 environmental variables and the same virtualSp() dataset selecting the absence instead of the background locations.

p_coords <- virtualSp$presence
a_coords <- virtualSp$absence
data <- prepareSWD(species = "Virtual species", p = p_coords, a = a_coords, env = predictors[[1:8]])
data

There are r sum(data@pa == 1) presence and r sum(data@pa == 0) absence locations.

For the model evaluation we will create a training and testing datasets, holding apart 20% of the data:

library(zeallot)
c(train, test) %<-% trainValTest(data, test = 0.2, seed = 25)

At this point we have r nrow(train@data) training and r nrow(test@data) testing locations. We create a 4-folds partition to run cross validation:

folds <- randomFolds(train, k = 4, seed = 25)

Train the model

We first train the model with default settings and 10 neurons:

set.seed(25)
model <- train("ANN", data = train, size = 10, folds = folds)
model

Let's check the training and testing AUC:

auc(model)
auc(model, test = TRUE)

Tune model hyperparameters

To check which hyperparameters can be tuned we use the function getTunableArgs() function:

getTunableArgs(model)

We use the function optimizeModel() to tune the hyperparameters:

h <- list(size = 10:50, decay = c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5),
          maxit = c(50, 100, 300, 500))

om <- optimizeModel(model, hypers = h, metric = "auc", seed = 25)

The best model is:

best_model <- om@models[[1]]
om@results[1, ]

The validation AUC increased from r auc(model, test = TRUE) of the default models to r om@results[1, 6] of the optimized one.

Evaluate the final model

We now train a model with the same configuration as found by the function optimizeModel() without cross validation, using all the train data, and we evaluate it using the held apart testing dataset:

set.seed(25)
final_model <- train("ANN", data = train, size = om@results[1, 1], decay = om@results[1, 2], maxit = om@results[1, 4])
plotROC(final_model, test = test)

Conclusion

In this tutorial you have learned a general way to train, evaluate and tune model using Artificial Neural Network, but you can apply the same workflow to other methods.

References



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SDMtune documentation built on July 17, 2021, 9:06 a.m.