biomod2 (version 1.3.5)

BIOMOD_ModelingOptions: Configure the modeling options for each selected model

Description

Function to set the different options for each modeling technique.

Usage

BIOMOD_ModelingOptions( GLM = NULL,
                        GBM = NULL,
                        GAM = NULL,
                        CTA = NULL,
                        ANN = NULL,
                        SRE = NULL,
                        FDA = NULL,
                        MARS = NULL,
                        RF = NULL,
                        MAXENT = NULL)

Arguments

GLM

list of GLM options

GBM

list of GBM options

GAM

list of GAM options

CTA

list of CTA options

ANN

list of ANN options

SRE

list of SRE options

FDA

list of FDA options

MARS

list of MARS options

RF

list of RF options

MAXENT

list of MAXENT options

Value

• A "BIOMOD.Model.Options" object given to BIOMOD_Modeling

=-=-= GLM =-=-= (<code><a href="/link/glm?package=biomod2&version=1.3.5&to=stats" rd-options="stats" data-mini-rdoc="stats::glm">glm</a></code>)

• myFormula: a typical formula object (see example). If not NULL, type and interaction.level args are switched off. You can choose to either:
  • generate automatically the GLM formula by using the type and interaction.level arguments type : formula given to the model ('simple', 'quadratic' or 'polynomial'). interaction.level : integer corresponding to the interaction level between variables considered. Consider that interactions quickly enlarge the number of effective variables used into the GLM.
• or construct specific formula

item

• test : Information criteria for the stepwise selection procedure: AIC for Akaike Information Criteria, and BIC for Bayesian Information Criteria ('AIC' or 'BIC'). 'none' is also a supported value which implies to concider only the full model (no stepwise selection). This can lead to convergence issu and strange results.
• family : a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.) . BIOMOD only runs on presence-absence data so far, so binomial family by default.
• control : a list of parameters for controlling the fitting process. For glm.fit this is passed to glm.control.
• k : a smooth term in a formula argument to gam (see gam s or mgcv s)
• family : a description of the error distribution and link function to be used in the model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.) . BIOMOD only runs on presence-absence data so far, so binomial family by default.
• control : see gam.control or gam.control

=-=-= GBM =-=-= (<code><a href="/link/gbm?package=biomod2&version=1.3.5&to=gbm" rd-options="gbm" data-mini-rdoc="gbm::gbm">gbm</a></code>)

Please refer to gbm help file to get the meaning of this options.

• n.trees

cv.folds distribution interaction.depth shrinkage bag.fraction train.fraction

=-=-= GAM =-=-= (<code><a href="/link/gam?package=biomod2&version=1.3.5&to=gam" rd-options="gam" data-mini-rdoc="gam::gam">gam</a></code> or <code><a href="/link/gam?package=biomod2&version=1.3.5&to=mgcv" rd-options="mgcv" data-mini-rdoc="mgcv::gam">gam</a></code>)

• algo : either "GAM_mgcv" (default), "GAM_gam" or "BAM_mgcv" defining the chosen GAM function (seegam,gamresp.bamfor more details)

myFormula : a typical formula object (see example). If not NULL, type and interaction.level args are switched off. You can choose to either:

• generate automatically the GLM formula by using the type and interaction.level arguments type : the smother used to generate the formula. Only "s_smoother" available at time. interaction.level : integer corresponding to the interaction level between variables considered. Consider that interactions quickly enlarge the number of effective variables used into the GAM. Interaction are not considered if you choosed "GAM_gam" algo

or construct specific formula

=-=-= CTA =-=-= (<code><a href="/link/rpart?package=biomod2&version=1.3.5&to=rpart" rd-options="rpart" data-mini-rdoc="rpart::rpart">rpart</a></code>)

Please refer to rpart help file to get the meaning of the following options.

• method ('class')

parms ('default') cost control

code

rpart

=-=-= ANN =-=-= (<code><a href="/link/nnet?package=biomod2&version=1.3.5&to=nnet" rd-options="nnet" data-mini-rdoc="nnet::nnet">nnet</a></code>)

• NbCV : nb of cross validation to find best size and decay parameters

rang : Initial random weights on [-rang, rang] maxit : maximum number of iterations. Default 100

=-=-= SRE =-=-=

• quant : quantile of 'extreme environmental variable' removed for selection of species envelops

=-=-= FDA =-=-= (<code><a href="/link/fda?package=biomod2&version=1.3.5&to=mda" rd-options="mda" data-mini-rdoc="mda::fda">fda</a></code>)

Please refer to fda help file to get the meaning of these options.

• method

=-=-= MARS -=-= (<code><a href="/link/mars?package=biomod2&version=1.3.5&to=mda" rd-options="mda" data-mini-rdoc="mda::mars">mars</a></code>)

Please refer to mars help file to get the meaning of these options.

• degree

penalty thresh prune

=-=-= RF -=-=-= (<code><a href="/link/randomForest?package=biomod2&version=1.3.5&to=randomForest" rd-options="randomForest" data-mini-rdoc="randomForest::randomForest">randomForest</a></code>)

• do.classif : if TRUE classification random.forest computed else regression random.forest will be done

ntree mtry ('default)

=-=-= MAXENT -= <url>http://www.cs.princeton.edu/~schapire/maxent/</url>

• path_to_maxent.jar : character, the link tomaxent.jarfile (the working directory by default)

maximumiterations : integer (default 200), maximum iteration done visible : logical (default FALSE), make the Maxent user interface visible linear : logical (default TRUE), allow linear features to be used quadratic : logical (default TRUE), allow quadratic features to be used product : logical (default TRUE), allow product features to be used threshold : logical (default TRUE), allow threshold features to be used hinge : logical (default TRUE), allow hinge features to be used lq2lqptthreshold : integer (default 80), number of samples at which product and threshold features start being used l2lqthreshold : integer (default 10), number of samples at which quadratic features start being used hingethreshold : integer (default 15), number of samples at which hinge features start being used beta_threshold : numeric (default -1.0), regularization parameter to be applied to all threshold features; negative value enables automatic setting beta_categorical : numeric (default -1.0), regularization parameter to be applied to all categorical features; negative value enables automatic setting beta_lqp : numeric (default -1.0), regularization parameter to be applied to all linear, quadratic and product features; negative value enables automatic setting beta_hinge : numeric (default -1.0), regularization parameter to be applied to all hinge features; negative value enables automatic setting defaultprevalence : numeric (default 0.5), default prevalence of the species: probability of presence at ordinary occurrence points

Details

The aim of this function is to allow advanced user to change some default parameters of BIOMOD inner models. For each modeling technique, options can be set up.

Each argument have to be put in a list object.

The best way to use this function is to print defaut models options (Print_Default_ModelingOptions) or create a default 'BIOMOD.model.option object' and print it in your console. Then copy the output, change only the required parameters, and paste it as function arguments. (see example)

Here the detailed list of modifiable parameters. They correspond to the traditional parameters that could be setted out for each modeling technique (e.g. ?GLM)

Examples

# default BIOMOD.model.option object
myBiomodOptions <- BIOMOD_ModelingOptions()

# print the object
myBiomodOptions

# you can copy a part of the print, change it and custom your options 
# here we want to compute quadratic GLM and select best model with 'BIC' criterium
myBiomodOptions <- BIOMOD_ModelingOptions(
                      GLM = list( type = 'quadratic',
                                  interaction.level = 0,
                                  myFormula = NULL,
                                  test = 'BIC',
                                  family = 'binomial',
                                  control = glm.control(epsilon = 1e-08, maxit = 1000, trace = FALSE) ))
                                  
# check changes was done
myBiomodOptions

# you can prefer to establish your own GLM formula
myBiomodOptions <- BIOMOD_ModelingOptions(
                        GLM = list(myFormula = formula("Sp277 ~ bio3 + log(bio10) + poly(bio16,2) + bio19 + bio3:bio19")))

# check changes was done
myBiomodOptions

# you also can directly print default parameters and then follow the same processus
Print_Default_ModelingOptions()