`biomod2`

is a wrapper calling for single models functions from external packages. Modeling options are automatically retrieved from these packages, allowing the use of all arguments taken into account by these functions.

**Default** parameter values are unmodified and often non-adapted to species distribution modeling in general, and to specific dataset in particular. **Bigboss** options provided by biomod2 team tend to correct at least the species distribution modeling aspect, while **tuned** options allow to try and find more appropriate parameterization for user data through caret package mainly. The user can also defines its own modeling options parameterization (**user.defined**).

Note that only binary data type and associated models are allowed currently, but the package structure has been changed to enable the addition of new data types in near future, such as absolute or relative abundances.

In the dataset `ModelsTable`

, all the different algorithms are listed with their packages and functions :

```
model type package func train
1 ANN binary nnet nnet avNNet
2 CTA binary rpart rpart rpart
3 FDA binary mda fda fda
4 GAM binary gam gam gamLoess
5 GAM binary mgcv bam bam
6 GAM binary mgcv gam gam
7 GBM binary gbm gbm gbm
8 GLM binary stats glm glm
9 MARS binary earth earth earth
10 MAXENT binary MAXENT MAXENT ENMevaluate
11 MAXNET binary maxnet maxnet maxnet
12 RF binary randomForest randomForest rf
13 SRE binary biomod2 bm_SRE bm_SRE
14 XGBOOST binary xgboost xgboost xgbTree
```

*All the examples are made with the data of the package.*

*For the beginning of the code, see the main functions vignette.*

`biomod2`

has a set of `default`

options, matching most of the time the algorithmsâ€™ default values, but with some minor modifications to allow the `BIOMOD_Modeling`

function to run smoothly.

*Please be aware that this strategy can often lead to bad models or even some errors.*

```
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
modeling.id = 'Example',
models = c('RF', 'GLM'),
CV.strategy = 'random',
CV.nb.rep = 2,
CV.perc = 0.8,
OPT.strategy = 'default',
metric.eval = c('TSS','ROC'),
var.import = 2,
seed.val = 42)
```

You can retrieve the models options with `get_options`

```
get_options(myBiomodModelOut)
```

The `bigboss`

set of parameters is available in the dataset `OptionsBigboss`

. This set should give better results than the default set and will be continued to be optimized by the `biomod2`

Team.

*Keep in mind that this is something general and dependent of your case, the results can be not better than the default set.*

```
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
modeling.id = 'Example',
models = c('RF', 'GLM'),
CV.strategy = 'random',
CV.nb.rep = 2,
CV.perc = 0.8,
OPT.strategy = 'bigboss',
metric.eval = c('TSS','ROC'),
var.import = 2,
seed.val = 42)
```

With `tuned`

options, some algorithms can be trained over your dataset, and optimized parameters are returned to be used within the `BIOMOD_Modeling`

function. This tuning is mostly based upon the `caret`

package which calls a specific function to tune each algorithm (see column `train`

in `ModelsTable`

). As exception, the `ENMevaluate`

function of the `ENMeval`

package is called for `MAXENT`

and the `biomod2`

team wrote a special function for `SRE`

.

Here is the list of the parameters that can be tuned :

algorithm | parameters |
---|---|

ANN | `size` , `decay` , `bag` |

FDA | `degree` , `nprune` |

GAM | `select` , `method` |

GBM | `n.trees` , `interaction.depth` , `shrinkage` , `n.minobsinnode` |

MARS | `degree` , `nprune` |

RF | `mtry` |

SRE | `quant` |

XGBOOST | `nrounds` , `max_depth` , `eta` , `gamma` , `colsampl_bytree` , `min_child_weight` , `subsample` |

For almost every algorithm (except `MAXENT`

, `MAXNET`

and `SRE`

), you can choose to optimize the formula by setting `do.formula = TRUE`

. The optimized formula will be chosen between the different type (`simple`

, `quadratic`

, `polynomial`

, `s_smoother`

) and for different interaction level.

In the same way, a variable selection can be run for `GLM`

and `GAM`

if `do.stepAIC = TRUE`

(respectively, `MASS::stepAIC`

and `gam::step.Gam`

).

*More information about the training can be found in the documentation of the bm_Tuning function.*

```
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
modeling.id = 'Example',
models = c('RF','SRE'),
CV.strategy = 'random',
CV.nb.rep = 2,
CV.perc = 0.8,
OPT.strategy = 'tuned',
metric.eval = c('TSS','ROC'),
var.import = 2,
seed.val = 42)
print(get_options(myBiomodModelOut), dataset = '_allData_RUN1')
```

The `user.defined`

option allows you to adjust yourself the parameters of all the algorithms.

*Note that you can find information about the parameters of MAXENT within the documentation of the bm_ModelingOptions function.*

**Example :**

- You want to run 3 models :
`RF`

,`GLM`

and`MARS.`

- You have your
`BiomodData`

and you set your cross-validation table. - Globally, you want to use the
`bigboss`

parameters as a base.

```
myCVtable <- bm_CrossValidation(bm.format = myBiomodData,
strategy = "random",
nb.rep = 2,
perc = 0.8)
myOpt <- bm_ModelingOptions(data.type = 'binary',
models = c('RF','GLM','MARS'),
strategy = 'bigboss',
bm.format = myBiomodData,
calib.lines = myCVtable)
print(myOpt)
```

- You decide to tune the parameters for
`RF`

and you want to change the formula for`GLM`

.

```
tuned.rf <- bm_Tuning(model = 'RF',
tuning.fun = 'rf', ## see in ModelsTable
do.formula = TRUE,
bm.options = opt.d@options$RF.binary.randomForest.randomForest,
bm.format = myBiomodData,
calib.lines = myCVtable)
form.GLM <- bm_MakeFormula(resp.name = myBiomodData@sp.name,
expl.var = head(myBiomodData@data.env.var),
type = 'simple',
interaction.level = 0)
user.GLM <- list('_allData_RUN1' = list(formula = form.GLM),
'_allData_RUN2' = list(formula = form.GLM))
```

- As you have all the parameters ready, you can set your
`BIOMOD.models.options`

object and run the`BIOMOD_Modeling`

function.

```
## Gather in one list
## Models names can be found in OptionsBigboss@models
user.val <- list( RF.binary.randomForest.randomForest = tuned.rf,
GLM.binary.stats.glm= user.GLM)
myOpt <- bm_ModelingOptions(data.type = 'binary',
models = c('RF','GLM','MARS'),
strategy = "user.defined",
user.val = user.val,
user.base = "bigboss",
bm.format = myBiomodData,
calib.lines = myCVtable)
print(myOpt)
print(myOpt, dataset = '_allData_RUN1')
print(myOpt, dataset = '_allData_RUN2')
myBiomodModelOut <- BIOMOD_Modeling(bm.format = myBiomodData,
modeling.id = 'Example',
models = c('RF','GLM','MARS'),
CV.strategy = 'user.defined',
CV.user.table = myCVtable,
OPT.user = myOpt,
metric.eval = c('TSS','ROC'),
var.import = 2)
```

*You can find more examples in the Secondary functions vignette.*