BIOMOD_Tuning: Tune models parameters

Description

Function to tune biomod2 single models parameters

Usage

BIOMOD_Tuning(
  bm.format,
  bm.options = BIOMOD_ModelingOptions(),
  models = c("GLM", "GBM", "GAM", "CTA", "ANN", "SRE", "FDA", "MARS", "RF",
    "MAXENT.Phillips"),
  metric.eval = "ROC",
  ctrl.train = NULL,
  ctrl.train.tuneLength = 30,
  ctrl.ANN = NULL,
  ctrl.CTA = NULL,
  ctrl.FDA = NULL,
  ctrl.GAM = NULL,
  ctrl.GBM = NULL,
  ctrl.GLM = NULL,
  ctrl.MARS = NULL,
  ctrl.RF = NULL,
  ANN.method = "avNNet",
  ANN.decay.tune = c(0.001, 0.01, 0.05, 0.1),
  ANN.size.tune = c(2, 4, 6, 8),
  ANN.maxit = 500,
  ANN.MaxNWts = 10 * (ncol(bm.format@data.env.var) + 1) + 10 + 1,
  MARS.method = "earth",
  GAM.method = "gam",
  GLM.method = "glmStepAIC",
  GLM.type = c("simple", "quadratic", "polynomial", "s_smoother"),
  GLM.interaction = c(0, 1),
  ME.cvmethod = "randomkfold",
  ME.overlap = FALSE,
  ME.kfolds = 10,
  ME.n.bg = 10000,
  ME.env = NULL,
  ME.metric = "ROC",
  ME.clamp = TRUE,
  ME.parallel = FALSE,
  ME.numCores = NULL,
  RF.method = "rf",
  weights = NULL
)

Value

A BIOMOD.models.options object (see BIOMOD_ModelingOptions) with optimized parameters

Arguments

bm.format: a BIOMOD.formated.data or BIOMOD.formated.data.PA object returned by the BIOMOD_FormatingData function
bm.options: a BIOMOD.models.options object returned by the BIOMOD_ModelingOptions function
models: a vector containing model names to be tuned,
must be among GLM, GBM, GAM, CTA, ANN, SRE, FDA, MARS, RF, MAXENT.Phillips
metric.eval: a character corresponding to the evaluation metric used to select optimal models and tune parameters, must be either ROC or TSS (maximizing Sensitivity and Specificity)
ctrl.train: global control parameters that can be obtained from the trainControl function
ctrl.train.tuneLength: (see tuneLength parameter in train)
ctrl.ANN: control parameters for ANN
ctrl.CTA: control parameters for CTA
ctrl.FDA: control parameters for FDA
ctrl.GAM: control parameters for GAM
ctrl.GBM: control parameters for GBM
ctrl.GLM: control parameters for GLM
ctrl.MARS: control parameters for MARS
ctrl.RF: control parameters for RF
ANN.method: a character corresponding to the classification or regression model to use for ANN,
must be avNNet (see http://topepo.github.io/caret/train-models-by-tag.html#Neural_Network)
ANN.decay.tune: a vector of weight decay parameters for ANN
ANN.size.tune: a vector of size parameters (number of units in the hidden layer) for ANN
ANN.maxit: an integer corresponding to the maximum number of iterations for ANN
ANN.MaxNWts: an integer corresponding to the maximum allowable number of weights for ANN
MARS.method: a character corresponding to the classification or regression model to use for MARS,
must be earth (see http://topepo.github.io/caret/train-models-by-tag.html#Multivariate_Adaptive_Regression_Splines)
GAM.method: a character corresponding to the classification or regression model to use for GAM,
must be gam (see http://topepo.github.io/caret/train-models-by-tag.html#generalized-additive-model)
GLM.method: a character corresponding to the classification or regression model to use for GLM,
must be glmStepAIC (see http://topepo.github.io/caret/train-models-by-tag.html#Generalized_Linear_Model)
GLM.type: a vector of character corresponding to modeling types for GLM,
must be among simple, quadratic, polynomial, s_smoother
GLM.interaction: a vector of interaction types, must be among 0, 1
ME.cvmethod: a character corresponding to the method used to partition data for MAXENT.Phillips,
must be randomkfold
ME.overlap: (optional, default FALSE)
A logical value defining whether to calculate pairwise metric of niche overlap or not (see calc.niche.overlap)
ME.kfolds: an integer corresponding to the number of bins for k-fold cross-validation for MAXENT.Phillips
ME.n.bg: an integer corresponding to the number of background points used to run MAXENT.Phillips
ME.env: a RasterStack object containing model predictor variables
ME.metric: a character corresponding to the evaluation metric used to select optimal model and tune parameters for MAXENT.Phillips, must be either auc.val.avg, auc.diff.avg, or.mtp.avg, or.10p.avg or AICc
ME.clamp: (optional, default TRUE)
A logical value defining whether Features are constrained to remain within the range of values in the training data (Elith et al. 2011) or not
ME.parallel: (optional, default TRUE)
A logical value defining whether to enable parallel computing for MAXENT.Phillips or not
ME.numCores: an integer corresponding to the number of cores to be used to train MAXENT.Phillips
RF.method: a character corresponding to the classification or regression model to use for RF,
must be rf (see http://topepo.github.io/caret/train-models-by-tag.html#random-forest)
weights: a vector of numeric values corresponding to observation weights

Author

Frank Breiner

Details

ctrl.train parameter is set by default to :
caret::trainControl(method = 'cv', summaryFunction = caret::twoClassSummary,
classProbs = TRUE, returnData = FALSE).
All control parameters for other models are set to ctrl.train if unspecified.
For more details on MAXENT.Phillips tuning, please refer to ENMevaluate.
For more details on other models tuning, please refer to train.

References

Kuhn, Max. 2008. Building predictive models in R using the caret package. Journal of Statistical Software 28, 1-26.
Kuhn, Max, and Kjell Johnson. 2013. Applied predictive modeling. New York: Springer.
Muscarella, Robert, et al. 2014. ENMeval: An R package for conducting spatially independent evaluations and estimating optimal model complexity for Maxent ecological niche models. Methods in Ecology and Evolution, 5, 1198-1205.

Examples

Run this code


# Load species occurrences (6 species available)
myFile <- system.file('external/species/mammals_table.csv', package = 'biomod2')
DataSpecies <- read.csv(myFile, row.names = 1)
head(DataSpecies)

# Select the name of the studied species
myRespName <- 'GuloGulo'

# Get corresponding presence/absence data
myResp <- as.numeric(DataSpecies[, myRespName])

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

# Load environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myFiles <- paste0('external/bioclim/current/bio', c(3, 4, 7, 11, 12), '.grd')
myExpl <- raster::stack(system.file(myFiles, package = 'biomod2'))

# \dontshow{
myExtent <- raster::extent(0,30,45,70)
myExpl <- raster::stack(raster::crop(myExpl, myExtent))
# }

# ---------------------------------------------------------------
# Format Data with true absences
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
                                     expl.var = myExpl,
                                     resp.xy = myRespXY,
                                     resp.name = myRespName)


# ---------------------------------------------------------------
### Duration for turing all models sequential with default settings 
### on 3.4 GHz processor: approx. 45 min tuning all models in parallel
### (on 8 cores) using foreach loops runs much faster: approx. 14 min

if (FALSE) {
# library(doParallel)
# cl <- makeCluster(8)
# doParallel::registerDoParallel(cl) 

time.seq <- system.time(
  bm.tuning <- BIOMOD_Tuning(bm.format = myBiomodData, ME.env = myExpl, ME.n.bg = ncell(myExpl))
)

# stopCluster(cl)

plot(bm.tuning$tune.CTA.rpart)
plot(bm.tuning$tune.CTA.rpart2)
plot(bm.tuning$tune.RF)
plot(bm.tuning$tune.ANN)
plot(bm.tuning$tune.MARS)
plot(bm.tuning$tune.FDA)
plot(bm.tuning$tune.GBM)
plot(bm.tuning$tune.GAM)

# Get tuned modeling options
myBiomodOptions <- bm.tuning$models.options
}

Run the code above in your browser using DataLab

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples