Flexible Discriminant Analysis is a classification model based on a mixture of linear regression models, which uses optimal scoring to transform the response variable so that the data are in a better form for linear separation, and multiple adaptive regression splines to generate the discriminant surface.
- Works well with a large number of predictor variables
- Automatically detects interactions between variables
- It is an efficient and fast algorithm, despite its complexity
- Robust to outliers
- Strong sensitivity to configuration setting
- Susceptible to overfitting
- More difficult to understand and interpret than other methods
No assumptions are made about the distributions of the environmental variables. However, they should not be highly correlated with one another because this could cause problems with the estimation.
Requires absence data
BCCVL uses the ‘fda’ package, implemented in biomod2. The user can set the following configuration options:
W. Hallgren, F. Santana, S. Low-Choy, Y. Zhao, B. Mackey (2019). Species distribution models can be highly sensitive to algorithm configuration, Ecological Modelling,408. doi.org/10.1016/j.ecolmodel.2019.108719.
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference and prediction. 2nd edition, Springer.