SDMSelect

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Cross-validation model selection and species distribution mapping

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# Summary

The R package **SDMSelect** is for species distribution modeling (SDM).
It uses a forward model selection with cross-validation to select
covariates and model specifications that allow for best predictions.
This selection is not specifically for SDM. Results can then be used for
mapping predicted species distribution and uncertainty of prediction.

<img src="https://raw.githubusercontent.com/statnmap/SDMSelect/master/img/ProbaOfPresence.png" alt="Distribution of probability of presence with SDMSelect library on Rstat" width="50%" />
<p class="caption">
Distribution of probability of presence with SDMSelect library on Rstat
</p>

</div>

# How to cite

Please give credit where credit is due and cite R and R packages when
you use them for data analysis. To cite `SDMSelect` properly, call the R
built-in command `citation("SDMSelect")`:

> Sébastien Rochette. (2019, June 25). statnmap/SDMSelect: SDMSelect: A
> R-package for cross-validation model selection and species
> distribution mapping (Version v0.1.5). Zenodo.
> <http://doi.org/10.5281/zenodo.3256536>

# Download and Install

Before installing `SDMSelect`, be sure to have updated versions of
`mgcv (>= 1.8-19)`, `dplyr (>= 0.7)` and `corrplot (>= 0.82)`.

To download the development version of the `SDMSelect` package, type the
following at the R command line:

``` r
install.packages("devtools")  
devtools::install_github("statnmap/SDMSelect")
```

Build with vignettes:

``` r
devtools::install_github("statnmap/SDMSelect", build_opts = c("--no-resave-data", "--no-manual"))
```

To be able to test the vignettes, you will previously need to install
`knitr`, `rmarkdown`, `dismo` and `rasterVis`. Vignette are available on
{pkgdown} website for [covariates
selection](https://statnmap.github.io/SDMSelect/articles/Covar_Selection.html)
and for [species distribution model
selection](https://statnmap.github.io/SDMSelect/articles/SDM_Selection.html)

Note that spatial libraries like `rgdal` and `sp` may require additional
softwares to be installed on your computer if you work with Mac or
Linux. Look how to install `proj4`, `geos` and `gdal` on your system.

## Issues

-   `namespace 'mgcv' 1.xx is being loaded, but >= 1.8.19 is required`.
    This means that you need to update `mgcv` library:
    `install.packages("mgcv", force = TRUE)`.
-   Error when building vignette: `win.asp is not graphical parameter`.
    This means you did not install the last version of `corrplot`:
    `install.packages("corrplot", force = TRUE)`  
-   `there is no package called 'dismo' ... cannot create a RasterLayer object from this file (file does not exist)`:
    install library `dismo`: `install.packages("dismo")`

# Examples

Vignettes have been created to show how to use the library for covariate
selection on simple cases (`vignette(package = "SDMSelect")`).

-   First case is covariate selection procedure for classical dataset
    (not geographical data, nor species distribution data):
    `vignette("Covar_Selection", package = "SDMSelect")`. See the
    vignette in the {pkgdown} website:
    <https://statnmap.github.io/SDMSelect/articles/Covar_Selection.html>

-   The second case is for spatial data of species occurence to produce
    predicted species distribution maps and maps of uncertainties:
    `vignette("SDM_Selection", package = "SDMSelect")`. See the vignette
    in the {pkgdown} website:
    <https://statnmap.github.io/SDMSelect/articles/SDM_Selection.html>

*Note that most figures of the vignette are saved in “inst” so that
model selection is not run during vignette building. However, code in
the vignettes can be run on your own computer and should return the same
outputs. You can also find the path of the complete vignettes to be run
on your computer with:
`system.file("Covar_Selection", "Covar_Selection.Rmd", package = "SDMSelect")`
and
`system.file("SDM_Selection", "SDM_Selection.Rmd", package = "SDMSelect")`.
Open and click `knit` button if you are on Rstudio. (This may require
5-10 minutes depending on the number of cores of your system).*  
Main functions are listed in the library general help: `?SDMSelect`.

# Description

I decided to transform into a R package some R-scripts I have been using
for years for my studies with species distribution modelling. I know it
could be improved in many ways, but it works as is.  
This library has originally been created for covariates selection to
predict species distribution (Biomass, density or presence/absence). Its
final aim is thus to produce maps of predicted distributions (Look at
vignette `SDM_Selection`). However, the core of the library is a N-times
k-fold cross-validation selection procedure that can be applied to any
kind of data, provided that model parameters are well defined (Look at
vignette `Covar_selection`).

This works with `dataframe`, `SpatialPointsDataFrame` and `tibbles`.  
This has been designed to run in parallel in multiple cores computer,
using library `parallel`. The only steps missing in this library are
data exploration and data cleansing. These are important parts of
modelling and should be realized prior to model selection procedure.

## Selection method

The model selection procedure will test different combinations of
covariates with LM, GLM, GLM natural splines and GAM models, with
different distributions (Gaussian, Gamma, Log-Normal, Tweedie; Binomial)
and with different maximum degrees of freedom for GLM with polynoms or
natural splines. The multiple k-fold cross-validation realised on the
same folds for each model/submodel type allows their comparison using
cross-validated RMSE or AUC. Covariates correlation can be tested prior
to the selection procedure to avoid fitting models with correlated
covariates. However, the cross-validation procedure is coupled with a
forward stepwise procedure. This means that covariates are added to best
models selected at the previous step. This obviously not choose a new
covariate that add no predictive power to a model, thus avoiding
selecting correlated covariates in the same model.  
Models are ordered according to RMSE (or AUC for presence-absence). The
cross-validation produces a distribution of (`N*k`) RMSE values for each
model at each step. Models are ordered according to mean RMSE. RMSE
distributions of model `2` to `n` are then statistically compared to the
best model. Models not statistically worse than the best model are
retained for the next step. After the stepwise procedure, the best model
among all is selected with the same method. Models not statistically
worse are also retained.

<img src="https://raw.githubusercontent.com/statnmap/SDMSelect/master/img/Help_for_model_Choice.png" alt="Models and covariates in models not statistically different from the best one. Covariates are ordered based on occurrence in the models selected." width="80%" />
<p class="caption">
Models and covariates in models not statistically different from the
best one. Covariates are ordered based on occurrence in the models
selected.
</p>

</div>

## Outputs

Outputs of the selection procedure are numerous, allowing for summary of
the model selection and the comparison of the different models
alltogether (GAM, GLM; Gaussian, Gamma, Log-Normal, Tweedie; Binomial).
All output files and figures are stored in a common folder and are not
showing up in the R session directly. The final model retained by the
user can then be analysed (residual analysis, variance analysis, effect
of covariates). For spatial data, maps of prediction can be produced.

## Uncertainties

A particular attention has been given to assessment of uncertainty. Each
prediction of a model is given with a standard error associated. This
standard errors have been used to estimate possible minimum and maximum
distributions of species (through estimations of quantiles).

<img src="https://raw.githubusercontent.com/statnmap/SDMSelect/master/img/MinMaxProbaOfPresence.png" alt="Distribution of minimum (quantile 5%) and maximum (quantile 95%) of probabilities of presence with SDMSelect library on Rstat" width="80%" />
<p class="caption">
Distribution of minimum (quantile 5%) and maximum (quantile 95%) of
probabilities of presence with SDMSelect library on Rstat
</p>

</div>

Concerning presence-absence data, the estimation of probability of
presence is not enough. The balance between presences and absences in
data may conduct to biased predictions. The best threshold value to
classify a probability of presence in presence or absence is here
explored in more details. Map of probability to be over the threshold
value is calculated.

<img src="https://raw.githubusercontent.com/statnmap/SDMSelect/master/img/CompObsPred.png" alt="Comparison of predictions against observations in a presence-absence data model and thresholds values with SDMSelect library on Rstat" width="50%" />
<p class="caption">
Comparison of predictions against observations in a presence-absence
data model and thresholds values with SDMSelect library on Rstat
</p>

</div>

<img src="https://raw.githubusercontent.com/statnmap/SDMSelect/master/img/ProbaOverThd.png" alt="Distribution of probabilities to be over the best threshold value separating presences from absences with SDMSelect library on Rstat" width="50%" />
<p class="caption">
Distribution of probabilities to be over the best threshold value
separating presences from absences with SDMSelect library on Rstat
</p>

</div>

## Caution

This library relies on a lot of other R-packages, which means that any
modifications of those may prevent this library to work correctly. As I
continue to use it regularly, I may see if some updates broke some of my
functions and would try to fix them.  
There are recent libraries like `dplyr`, `tidyr`, `ggplot2` that did not
originally exist when I first created these R-scripts. I now try to
implement them from time to time.

## Remark

This library is not as complete as can be `library(caret)` in terms of
model types available, however, as far as I know, `caret` model
selection procedure is designed for a unique combination of
model/distribution. Here, cross-validation outputs of each
model/distribution are kept, and can then be compared all together with
*paired* comparison. Not only the model with the best mean RMSE (or AUC)
is retained, but also all other models giving predictions statistically
as good as the best one. For biological purpose, knowing all models with
the same predictive power may modify outputs interpretation. Another
difference is that `SDMSelect` package is designed for predicting
species distribution models, which means that some outputs are maps of
species distributions as well as all maps of uncertainty that can be
deduced from model outputs.

# Collaboration

For your work on covariates selection and species distribution
modelling, you can contact me. Vignettes will give you a good starting
point. I’ll be happy to participate to scientific collaborations based
on this R-package. More information on my website
<https://statnmap.com>.

If you want to participate in improving this library, please have a look
at my todo list (in Vignettes directory or in the [pkgdown
site](https://statnmap.github.io/SDMSelect/articles/TODO.html)) and feel
free to clone, modify and provide pull requests. Please run the two Rmd
files in `inst` folder before submitting a pull request. These are the
complete versions of the two vignettes.

See full documentation realized using {pkgdown} at
<https://statnmap.github.io/SDMSelect/>

# License

This package is free and open source software, licensed under GPL.

SDMSelect

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