bm_ModelingOptions {biomod2} | R Documentation |
Configure the modeling options for each selected model
Description
Parameterize and/or tune biomod2's single models options.
Usage
bm_ModelingOptions(
data.type,
models = c("ANN", "CTA", "FDA", "GAM", "GBM", "GLM", "MARS", "MAXENT", "MAXNET", "RF",
"SRE", "XGBOOST"),
strategy,
user.val = NULL,
user.base = "bigboss",
bm.format = NULL,
calib.lines = NULL
)
Arguments
data.type |
a |
models |
a |
strategy |
a |
user.val |
(optional, default |
user.base |
(optional, default |
bm.format |
(optional, default |
calib.lines |
(optional, default |
Details
This function creates a BIOMOD.models.options
object containing parameter values
for each single model that can be run within biomod2 through
BIOMOD_Modeling
function.
12 models are currently available, and are listed within the ModelsTable
dataset.
Different strategies are available to set those parameters, through the strategy
argument :
- default
all parameters names and values are directly retrieve from functions to be called through
formalArgs
andformals
functions respectively- bigboss
default parameter values are updated with values predefined by biomod2 team
- user.defined
default parameter values are updated with values provided by the user
- tuned
default parameter values are updated by calling
bm_Tuning
function
Value
A BIOMOD.models.options
of object that can be used to build species
distribution model(s) with the BIOMOD_Modeling
function.
Note
MAXENT
being the only external model (not called through a R
package),
default parameters, and their values, are the following :
-
path_to_maxent.jar = getwd()
: acharacter
corresponding to path tomaxent.jar
file -
memory_allocated = 512
: aninteger
corresponding to the amount of memory (in Mo) reserved forjava
to runMAXENT
, must be either64
,128
,256
,512
,1024
... orNULL
to use defaultjava
memory limitation parameter -
initial_heap_size = NULL
: acharacter
corresponding to initial heap space (shared memory space) allocated tojava
(argument-Xms
when callingjava
), must be either1024K
,4096M
,10G
... orNULL
to use defaultjava
parameter. Used inBIOMOD_Projection
but not inBIOMOD_Modeling
. -
max_heap_size = NULL
: acharacter
corresponding to maximum heap space (shared memory space) allocated tojava
(argument-Xmx
when callingjava
), must be either1024K
,4096M
,10G
... orNULL
to use defaultjava
parameter, and must be larger thaninitial_heap_size
. Used inBIOMOD_Projection
but not inBIOMOD_Modeling
. -
background_data_dir = 'default'
: acharacter
corresponding to path to folder where explanatory variables are stored asASCII
files (raster format). If specified,MAXENT
will generate its own background data from rasters of explanatory variables ('default'
value). Otherwise biomod2 pseudo-absences will be used (seeBIOMOD_FormatingData
). -
visible = FALSE
: alogical
value defining whetherMAXENT
user interface is to be used or not -
linear = TRUE
: alogical
value defining whether linear features are to be used or not -
quadratic = TRUE
: alogical
value defining whether quadratic features are to be used or not -
product = TRUE
: alogical
value defining whether product features are to be used or not -
threshold = TRUE
: alogical
value defining whether threshold features are to be used or not -
hinge = TRUE
: alogical
value defining whether hinge features are to be used or not -
l2lqthreshold = 10
: aninteger
corresponding to the number of samples at which quadratic features start being used -
lq2lqptthreshold = 80
: aninteger
corresponding to the number of samples at which product and threshold features start being used -
hingethreshold = 15
: aninteger
corresponding to the number of samples at which hinge features start being used -
beta_lqp = -1.0
: anumeric
corresponding to the regularization parameter to be applied to all linear, quadratic and product features (negative value enables automatic setting) -
beta_threshold = -1.0
: anumeric
corresponding to the regularization parameter to be applied to all threshold features (negative value enables automatic setting) -
beta_hinge = -1.0
: anumeric
corresponding to the regularization parameter to be applied to all hinge features (negative value enables automatic setting) -
beta_categorical = -1.0
: anumeric
corresponding to the regularization parameter to be applied to all categorical features (negative value enables automatic setting) -
betamultiplier = 1
: anumeric
corresponding to the number by which multiply all automatic regularization parameters (higher number gives a more spread-out distribution) -
defaultprevalence = 0.5
: anumeric
corresponding to the default prevalence of the modelled species (probability of presence at ordinary occurrence points)
Author(s)
Damien Georges, Wilfried Thuiller, Maya Gueguen
See Also
ModelsTable
, BIOMOD.models.options
,
bm_Tuning
, BIOMOD_Modeling
Other Secundary functions:
bm_BinaryTransformation()
,
bm_CrossValidation()
,
bm_FindOptimStat()
,
bm_MakeFormula()
,
bm_PlotEvalBoxplot()
,
bm_PlotEvalMean()
,
bm_PlotRangeSize()
,
bm_PlotResponseCurves()
,
bm_PlotVarImpBoxplot()
,
bm_PseudoAbsences()
,
bm_RunModelsLoop()
,
bm_SRE()
,
bm_SampleBinaryVector()
,
bm_SampleFactorLevels()
,
bm_Tuning()
,
bm_VariablesImportance()
Examples
library(terra)
# Load species occurrences (6 species available)
data(DataSpecies)
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)
data(bioclim_current)
myExpl <- terra::rast(bioclim_current)
# ---------------------------------------------------------------#
# Format Data with true absences
myBiomodData <- BIOMOD_FormatingData(resp.var = myResp,
expl.var = myExpl,
resp.xy = myRespXY,
resp.name = myRespName)
# k-fold selection
cv.k <- bm_CrossValidation(bm.format = myBiomodData,
strategy = 'kfold',
nb.rep = 2,
k = 3)
# ---------------------------------------------------------------#
allModels <- c('ANN', 'CTA', 'FDA', 'GAM', 'GBM', 'GLM'
, 'MARS', 'MAXENT', 'MAXNET', 'RF', 'SRE', 'XGBOOST')
# default parameters
opt.d <- bm_ModelingOptions(data.type = 'binary',
models = allModels,
strategy = 'default')
# providing formated data
opt.df <- bm_ModelingOptions(data.type = 'binary',
models = allModels,
strategy = 'default',
bm.format = myBiomodData,
calib.lines = cv.k)
opt.d
opt.d@models
opt.d@options$ANN.binary.nnet.nnet
names(opt.d@options$ANN.binary.nnet.nnet@args.values)
opt.df@options$ANN.binary.nnet.nnet
names(opt.df@options$ANN.binary.nnet.nnet@args.values)
# ---------------------------------------------------------------#
# bigboss parameters
opt.b <- bm_ModelingOptions(data.type = 'binary',
models = allModels,
strategy = 'bigboss')
# user defined parameters
user.SRE <- list('_allData_allRun' = list(quant = 0.01))
user.XGBOOST <- list('_allData_allRun' = list(nrounds = 10))
user.val <- list(SRE.binary.biomod2.bm_SRE = user.SRE
, XGBOOST.binary.xgboost.xgboost = user.XGBOOST)
opt.u <- bm_ModelingOptions(data.type = 'binary',
models = c('SRE', 'XGBOOST'),
strategy = 'user.defined',
user.val = user.val)
opt.b
opt.u
## Not run:
# tuned parameters with formated data
opt.t <- bm_ModelingOptions(data.type = 'binary',
models = c('SRE', 'XGBOOST'),
strategy = 'tuned',
bm.format = myBiomodData)
opt.t
## End(Not run)