knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) options(knitr.table.format = "html")
default_model <- SDMtune:::bm_maxent files <- list.files(path = file.path(system.file(package = "dismo"), "ex"), pattern = "grd", full.names = TRUE) predictors <- raster::stack(files)
In the previous articles you have learned how to prepare the data for the analysis, how to train a model and how to make predictions using SDMtune. In this article you will learn how to evaluate your model using three different metrics.
SDMtune implements three evaluation metrics:
We will compute the value of the metrics on the training dataset, using the
default model that we trained in a previous article.
As usually we first load the SDMtune package:
The AUC can be calculated using the function
We can also plot the ROC curve using the function
The TSS is computed with the function
For the AICc we use the function
aicc(). In this case we need to pass to the
env argument the 'predictors' raster
stack object that we created in the first article:
aicc(default_model, env = predictors)
Try to compute the three metrics using a model trained using the Maxnet method.
In this article you have learned:
In the next article you will learn two different strategies that can be used to correctly evaluate the model performance.
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