Tune models parameters
Function to tune biomod single models parameters
BIOMOD_tuning(
data,
models = c("GLM", "GBM", "GAM", "CTA", "ANN", "SRE", "FDA", "MARS", "RF",
"MAXENT.Phillips"),
models.options = BIOMOD_ModelingOptions(),
method.ANN = "avNNet",
method.RF = "rf",
method.MARS = "earth",
method.GAM = "gam",
method.GLM = "glmStepAIC",
trControl = NULL,
metric = "ROC",
ctrl.CTA = NULL,
ctrl.RF = NULL,
ctrl.ANN = NULL,
ctrl.MARS = NULL,
ctrl.FDA = NULL,
ctrl.GAM = NULL,
ctrl.GBM = NULL,
ctrl.GLM = NULL,
tuneLength = 30,
decay.tune.ANN = c(0.001, 0.01, 0.05, 0.1),
size.tune.ANN = c(2, 4, 6, 8),
maxit.ANN = 500,
MaxNWts.ANN = 10 * (ncol(data@data.env.var) + 1) + 10 + 1,
type.GLM = c("simple", "quadratic", "polynomial", "s_smoother"),
interaction.GLM = c(0, 1),
cvmethod.ME = "randomkfold",
overlap.ME = FALSE,
kfolds.ME = 10,
n.bg.ME = 10000,
env.ME = NULL,
metric.ME = "ROC",
clamp.ME = TRUE,
parallel.ME = FALSE,
numCores.ME = NULL,
Yweights = NULL
)data |
BIOMOD.formated.data object returned by BIOMOD_FormatingData |
models |
vector of models names choosen among 'GLM', 'GBM', 'GAM', 'CTA', 'ANN', 'SRE', 'FDA', 'MARS', 'RF', 'MAXENT.Phillips' |
models.options |
BIOMOD.models.options object returned by BIOMOD_ModelingOptions. Default: BIOMOD_ModelingOptions() |
method.ANN |
which classification or regression model to use for artificial neural networks (default: "avNNet"). see http://topepo.github.io/caret/Neural_Network.html |
method.RF |
which classification or regression model to use for randomForest (default: "rf"). see http://topepo.github.io/caret/Random_Forest.html |
method.MARS |
which classification or regression model to use for mars (default: "earth"). see http://topepo.github.io/caret/Multivariate_Adaptive_Regression_Splines.html |
method.GAM |
which classification or regression model to use for GAM (default: "gam"). see http://topepo.github.io/caret/Generalized_Additive_Model.html |
method.GLM |
which classification or regression model to use for GLM: (default: 'glmStepAIC'). see http://topepo.github.io/caret/Generalized_Linear_Model.html |
trControl |
global control parameters for runing (default trainControl(method="cv",summaryFunction=twoClassSummary,classProbs=T),returnData = FALSE). for details see trainControl |
metric |
metric to select the optimal model (Default ROC). TSS (maximizing Sensitivity and Specificity) is also possible. see ?train |
ctrl.CTA |
specify control parameters only for CTA (default trControl) |
ctrl.RF |
specify control parameters only for RF (default trControl) |
ctrl.ANN |
specify control parameters only for ANN (default trControl) |
ctrl.MARS |
specify control parameters only for MARS (default trControl) |
ctrl.FDA |
specify control parameters only for FDA (default trControl) |
ctrl.GAM |
specify control parameters only for GAM (default trControl) |
ctrl.GBM |
specify control parameters only for GBM (default trControl) |
ctrl.GLM |
specify control parameters only for GLM (default trControl) |
tuneLength |
see ?train (default 30) |
decay.tune.ANN |
weight decay parameters used for tuning for ANN (default: c(0.001, 0.01, 0.05, 0.1)) Will be optimised by method specified in ctrl.ANN (if not available trControl). |
size.tune.ANN |
size parameters (number of units in the hidden layer) for ANN used for tuning (default: c(2,4,6,8)). Will be optimised using the method specified in ctrl.ANN (if not available trControl). |
maxit.ANN |
maximum number of iterations for ANN (default 500) |
MaxNWts.ANN |
The maximum allowable number of weights for ANN (default 10 * (ncol(myBiomodData'at'data.env.var) + 1) + 10 + 1). |
type.GLM |
vector of modeling types choosen among 'simple', 'quadratic', 'polynomial' or 's_smoother' (default c('simple','quadratic','polynomial','s_smoother')) |
interaction.GLM |
vector of interaction type choosen among 0, 1. Default c(0,1) |
cvmethod.ME |
method used for data partitioning for MAXENT.Phillips (default: 'randomkfold') |
overlap.ME |
logical; Calculates pairwise metric of niche overlap if TRUE (Default: FALSE). (see ?calc.niche.overlap) |
kfolds.ME |
number of bins to use for k-fold cross-validation used for MAXENT.Phillips (Default: 10). |
n.bg.ME |
Number of Background points used to run MAXENT.Phillips (Default: 10000) |
env.ME |
RasterStack of model predictor variables |
metric.ME |
metric to select the optimal model for MAXENT.Phillips (Default: ROC). One out of Mean.AUC (or ROC), Mean.AUC.DIFF, Mean.ORmin, Mean.OR10 and AICc. see ?ENMevaluate and Muscarella et al. 2014 |
clamp.ME |
logical; If TRUE (Default) "Features are constrained to remain within the range of values in the training data" (Elith et al. 2011) |
parallel.ME |
logical. If TRUE, the parallel computing is enabled for MAXENT.Phillips |
numCores.ME |
number of cores used to train MAXENT.Phillips |
Yweights |
response points weights. This argument will only affect models that allow case weights. |
BIOMOD.models.options object with optimized parameters
Frank Breiner frank.breiner@wsl.ch
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.
## Not run:
# species occurrences
DataSpecies <- read.csv(system.file("external/species/mammals_table.csv",
package="biomod2"))
head(DataSpecies)
# the name of studied species
myRespName <- 'GuloGulo'
# the presence/absences data for our species
myResp <- as.numeric(DataSpecies[,myRespName])
# the XY coordinates of species data
myRespXY <- DataSpecies[,c("X_WGS84","Y_WGS84")]
# Environmental variables extracted from BIOCLIM (bio_3, bio_4, bio_7, bio_11 & bio_12)
myExpl = stack( system.file( "external/bioclim/current/bio3.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio4.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio7.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio11.grd",
package="biomod2"),
system.file( "external/bioclim/current/bio12.grd",
package="biomod2"))
# 1. Formatting Data
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
# 2. Defining Models Options using default options.
### 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
#library(doParallel);cl<-makeCluster(8);doParallel::registerDoParallel(cl)
time.seq<-system.time(Biomod.tuning <- BIOMOD_tuning(myBiomodData,
env.ME = myExpl,
n.bg.ME = ncell(myExpl)))
#stopCluster(cl)
myBiomodModelOut <- BIOMOD_Modeling( myBiomodData,
models = c('RF','CTA'),
models.options = Biomod.tuning$models.options,
NbRunEval=1,
DataSplit=100,
VarImport=0,
models.eval.meth = c('ROC'),
do.full.models=FALSE,
modeling.id="test")
# eval.plot(Biomod.tuning$tune.MAXENT.Phillips at results)
par(mfrow=c(1,3))
plot(Biomod.tuning$tune.CTA.rpart)
plot(Biomod.tuning$tune.CTA.rpart2)
plot(Biomod.tuning$tune.RF)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.