##
Intro

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:

- AUC: Area Under the ROC curve (Fielding and Bell 1997)
- TSS: True Skill Statistic (Allouche, Tsoar, and Kadmon 2006)
- AICc: Akaike Information Criterion corrected for small sample size (Warren and Seifert 2011)

We will compute the value of the metrics on the training dataset, using the `default model`

that we trained in a previous article.

##
AUC

As usually we first load the **SDMtune** package:

```
library(SDMtune)
#>
#> _____ ____ __ ___ __
#> / ___/ / __ \ / |/ // /_ __ __ ____ ___
#> \__ \ / / / // /|_/ // __// / / // __ \ / _ \
#> ___/ // /_/ // / / // /_ / /_/ // / / // __/
#> /____//_____//_/ /_/ \__/ \__,_//_/ /_/ \___/ version 1.1.5
#>
#> To cite this package in publications type: citation("SDMtune").
```

The AUC can be calculated using the function `auc()`

:

```
auc(default_model)
#> [1] 0.8728322
```

We can also plot the ROC curve using the function `plotROC()`

:

##
TSS

The TSS is computed with the function `tss()`

:

```
tss(default_model)
#> [1] 0.6419
```

##
AICc

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)
#> [1] 6618.725
```

###
Try yourself

Try to compute the three metrics using a model trained using the **Maxnet** method.

##
Conclusion

In this article you have learned:

- how to calculate the AUC;
- how to plot the ROC curve;
- how to calculate the TSS;
- how to calculate the AICc.

In the next article you will learn two different strategies that can be used to correctly evaluate the model performance.

###
References

Allouche, Omri, Asaf Tsoar, and Ronen Kadmon. 2006. “Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS).” *Journal of Applied Ecology* 43 (6): 1223–32. https://doi.org/10.1111/j.1365-2664.2006.01214.x.

Fielding, Alan H., and John F. Bell. 1997. “A review of methods for the assessment of prediction errors in conservation presence/absence models.” *Environmental Conservation* 24 (1): 38–49.

Warren, Dan L., and Stephanie N. Seifert. 2011. “Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria.” *Ecological Applications* 21 (2): 335–42. https://doi.org/10.1890/10-1171.1.