# Main functions: abundance data

## Complete code example

Here are presented, in a full and complete example, all main functions (starting with `BIOMOD_[...]`) of `biomod2`.

The data set used is the DataSTOC containing abundance data.
Similar examples are presented for binary data on the Main functions (bin) webpage.

### Load dataset and variables

    library(biomod2)
    library(terra)

    # Load species abundances (20 species available)
    data('DataSTOC')
    head(DataSTOC)

    # Divide into calibration/validation and evaluation
    period1 <- which(DataSTOC[, 'period'] == '2006-2011')
    period2 <- which(DataSTOC[, 'period'] == '2012-2017')

    # Select the name of the studied species
    myRespName <- 'Periparus.ater'

    # Get corresponding count data
    myResp <- as.numeric(DataSTOC[, myRespName])

    # Get corresponding XY coordinates
    myRespXY <- DataSTOC[, c('X_WGS84', 'Y_WGS84')]

    # Get corresponding environmental variables
    myExpl <- DataSTOC[, c('temp', 'precip', 'sdiv_hab',
                           'cover_agri', 'cover_water', 'cover_wet')]

### Prepare data & parameters

#### Format data (observations & explanatory variables)

Main difference when providing abundance data instead of binary is to define the `data.type` parameter.
The distribution used in the selected algorithms afterwards will be set accordingly, for example :

Type | Data | Distribution
--- | --- | ---
`binary` | Only 1, and 0 or NA | `binomial`
`count` | Positive integer values | `poisson`
`multiclass` | Categorical classes | classification
`ordinal` | Ordered categorical classes | classification
`relative` | Continuous values between 0 and 1 | beta
`abundance` | Positive continuous values | `gaussian`

    # Format data with count data type
    myBiomodData <- BIOMOD_FormatingData(resp.name = myRespName,
                                         resp.var = myResp[period1],
                                         resp.xy = myRespXY[period1, ],
                                         expl.var = myExpl[period1, ],
                                         eval.resp.var = myResp[period2],
                                         eval.resp.xy = myRespXY[period2, ],
                                         eval.expl.var = myExpl[period2, ],
                                         data.type = 'count')
    myBiomodData
    summary(myBiomodData)
    plot(myBiomodData, plot.eval = TRUE)

Note that pseudo-absence selection is only possible when `data.type = 'binary'`.

#### Cross-validation datasets

Several cross-validation methods are available and can be selected with the `BIOMOD_Modeling` function, which calls the `bm_CrossValidation` function to do so. More examples are presented on the Auxiliary functions webpage.

When using the `balance` parameter with `multiclass` or `ordinal` data, proportions are balanced within each class as much as possible.

    # # k-fold selection
    # cv.k <- bm_CrossValidation(bm.format = myBiomodData,
    #                            strategy = 'kfold',
    #                            nb.rep = 2,
    #                            k = 3)
    #
    # # stratified selection (geographic)
    # cv.s <- bm_CrossValidation(bm.format = myBiomodData,
    #                            strategy = 'strat',
    #                            k = 2,
    #                            balance = 'presences',
    #                            strat = 'x')
    # head(cv.k)
    # head(cv.s)

#### Retrieve modeling options

Modeling options are automatically retrieved from selected models within the `BIOMOD_Modeling` function, which calls the `bm_ModelingOptions` function to do so. Model parameters can also be automatically tuned to a specific dataset, by calling the `bm_Tuning` function, however it can be quite long. More examples are presented on the Auxiliary functions webpage.

    # # bigboss parameters
    # opt.b <- bm_ModelingOptions(data.type = 'count',
    #                             models = c('DNN', 'XGBOOST'),
    #                             strategy = 'bigboss')
    #
    # # tuned parameters with formated data
    # opt.t <- bm_ModelingOptions(data.type = 'count',
    #                             models = c('DNN', 'XGBOOST'),
    #                             strategy = 'tuned',
    #                             bm.format = myBiomodData)
    #
    # opt.b
    # opt.t

### Run modeling

Not all algorithms are available when it comes to data type other than binary.

| ANN | CTA | DNN | FDA | GAM | GBM | GLM | MARS | MAXENT | MAXNET | RF | RFd | SRE | XGBOOST
--- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---
binary | x | x | x | x | x | x | x | x | x | x | x | x | x | x
multiclass |  | x | x | x |  |  |  | x |  |  | x |  |  | x
ordinal |  | x | x | x | x |  | x | x |  |  | x |  |  | x
count / relative / abundance |  | x | x |  | x | x | x | x |  |  | x |  |  | x

New evaluation metrics have been added :

- `Accuracy`, `Recall`, `Precision` and `F1` for multiclass and ordinal
- `RMSE`, `MSE`, `MAE`, `Max_error`, `Rsquared` and `Rsquared_aj` for count, relative and abundance

as well as new ensemble models :

- `EMmode` and `EMfreq` for multiclass and ordinal

The selection of single models for the ensemble modeling is different for `RMSE`, `MSE`, `MAE` and `Max_error` metrics : are kept all models whose evaluation value is < (best value + the given threshold).

#### Single models

    # Model single models
    myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
                                        modeling.id = 'AllModels',
                                        models = c('DNN', 'GBM', 'GLM', 'MARS', 'RF', 'XGBOOST'),
                                        CV.strategy = 'block',
                                        OPT.strategy = 'bigboss',
                                        metric.eval = c('Rsquared', 'Rsquared_aj', 'RMSE'),
                                        var.import = 3)
                                        # seed.val = 123)
                                        # nb.cpu = 8)
    myBiomodModelOut

    # Get evaluation scores & variables importance
    get_evaluations(myBiomodModelOut)
    get_variables_importance(myBiomodModelOut)

    # Represent evaluation scores & variables importance
    bm_PlotEvalMean(bm.out = myBiomodModelOut, dataset = 'calibration')
    bm_PlotEvalMean(bm.out = myBiomodModelOut, dataset = 'validation')
    bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, dataset = 'calibration', group.by = c('algo', 'algo'))
    bm_PlotEvalBoxplot(bm.out = myBiomodModelOut, dataset = 'calibration', group.by = c('algo', 'run'))
    bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'algo'))
    bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('expl.var', 'algo', 'run'))
    bm_PlotVarImpBoxplot(bm.out = myBiomodModelOut, group.by = c('algo', 'expl.var', 'run'))

    # Represent response curves
    bm_PlotResponseCurves(bm.out = myBiomodModelOut,
                          models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 7:9)],
                          fixed.var = 'median')
    bm_PlotResponseCurves(bm.out = myBiomodModelOut,
                          models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 7:9)],
                          fixed.var = 'min')
    bm_PlotResponseCurves(bm.out = myBiomodModelOut,
                          models.chosen = get_built_models(myBiomodModelOut)[3],
                          fixed.var = 'median',
                          do.bivariate = TRUE)

    # Explore models' outliers & residuals
    bm_ModelAnalysis(bm.mod = myBiomodModelOut,
                     models.chosen = get_built_models(myBiomodModelOut)[c(1:3, 7:9)])

#### Ensemble models

    # Model ensemble models
    myBiomodEM <- BIOMOD_EnsembleModeling(bm.mod = myBiomodModelOut,
                                          models.chosen = 'all',
                                          em.by = 'all',
                                          em.algo = c('EMmedian', 'EMmean', 'EMwmean'),
                                          metric.select = c('Rsquared_aj', 'RMSE'),
                                          metric.select.thresh = c(0.4, 2),
                                          metric.eval = c('Rsquared', 'Rsquared_aj', 'RMSE'),
                                          var.import = 3)
    myBiomodEM

    # Get evaluation scores & variables importance
    get_evaluations(myBiomodEM)
    get_variables_importance(myBiomodEM)

    # Represent evaluation scores & variables importance
    bm_PlotEvalMean(bm.out = myBiomodEM, dataset = 'calibration', group.by = 'full.name')
    bm_PlotEvalBoxplot(bm.out = myBiomodEM, dataset = 'calibration', group.by = c('full.name', 'full.name'))
    bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'full.name', 'full.name'))
    bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('expl.var', 'algo', 'merged.by.run'))
    bm_PlotVarImpBoxplot(bm.out = myBiomodEM, group.by = c('algo', 'expl.var', 'merged.by.run'))

    # Represent response curves
    bm_PlotResponseCurves(bm.out = myBiomodEM,
                          models.chosen = get_built_models(myBiomodEM)[c(2, 5)],
                          fixed.var = 'median')
    bm_PlotResponseCurves(bm.out = myBiomodEM,
                          models.chosen = get_built_models(myBiomodEM)[c(2, 5)],
                          fixed.var = 'min')
    bm_PlotResponseCurves(bm.out = myBiomodEM,
                          models.chosen = get_built_models(myBiomodEM)[5],
                          fixed.var = 'median',
                          do.bivariate = TRUE)

### Project models

The `digits` parameter is used to define the number of digits after the decimal point for the predicted values.
For relative data, the `on_0_1000` parameter can be used in the same way than with binary data.

Note that `integer` values are lighter when saved in memory than `float` (decimal values).

#### Single models

    # Project single models
    myBiomodProj <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
                                      proj.name = 'Period1',
                                      new.env = myExpl[period1, ],
                                      new.env.xy = myRespXY[period1, ],
                                      models.chosen = 'all',
                                      build.clamping.mask = TRUE,
                                      digits = 1)
    myBiomodProj
    plot(myBiomodProj, coord = myRespXY[period1, ])

#### Ensemble models

    # Project ensemble models (from single projections)
    myBiomodEMProj <- BIOMOD_EnsembleForecasting(bm.em = myBiomodEM,
                                                 bm.proj = myBiomodProj,
                                                 models.chosen = 'all')

    # Project ensemble models (building single projections)
    myBiomodEMProj <- BIOMOD_EnsembleForecasting(bm.em = myBiomodEM,
                                                 proj.name = 'Period1EM',
                                                 new.env = myExpl[period1, ],
                                                 models.chosen = 'all')
    myBiomodEMProj
    plot(myBiomodEMProj, coord = myRespXY[period1, ])

### Compare range sizes

    # Project onto future conditions
    myBiomodProjectionFuture <- BIOMOD_Projection(bm.mod = myBiomodModelOut,
                                                  proj.name = 'Period2',
                                                  new.env = myExpl[period2, ],
                                                  new.env.xy = myRespXY[period2, ],
                                                  models.chosen = 'all',
                                                  build.clamping.mask = TRUE)

    # Compute differences
    myBiomodRangeSize <- BIOMOD_RangeSize(proj.current = myBiomodProj,
                                          proj.future = myBiomodProjectionFuture,
                                          metric.binary = 'TSS')

    myBiomodRangeSize@Compt.By.Models