This internal biomod2 function allows the user to compute all single
species distribution models (asked by the BIOMOD_Modeling
function).
bm_RunModelsLoop(
bm.format,
weights,
calib.lines,
modeling.id,
models,
models.pa,
bm.options,
metric.eval,
var.import,
scale.models = TRUE,
nb.cpu = 1,
seed.val = NULL,
do.progress = TRUE
)bm_RunModel(
model,
run.name,
dir.name = ".",
modeling.id = "",
bm.options,
Data,
weights.vec,
calib.lines.vec,
eval.data = NULL,
metric.eval = c("ROC", "TSS", "KAPPA"),
var.import = 0,
scale.models = TRUE,
nb.cpu = 1,
seed.val = NULL,
do.progress = TRUE
)
A list
containing for each model a list
containing the following elements :
model
: the name of correctly computed model
calib.failure
: the name of incorrectly computed model
pred
: the prediction outputs for calibration data
pred.eval
: the prediction outputs for evaluation data
evaluation
: the evaluation outputs returned by the
bm_FindOptimStat
function
var.import
: the mean of variables importance returned by the
bm_VariablesImportance
function
a BIOMOD.formated.data
or BIOMOD.formated.data.PA
object returned by the BIOMOD_FormatingData
function
a matrix
containing observation weights for each pseudo-absence (or
allData
) dataset
a matrix
containing calibration / validation lines for each
pseudo-absence (or allData
) x repetition (or allRun
) combination that can be
obtained with the bm_CrossValidation
function
a character
corresponding to the name (ID) of the simulation set
(a random number by default)
a vector
containing model names to be computed, must be among
ANN
, CTA
, FDA
, GAM
, GBM
, GLM
, MARS
,
MAXENT
, MAXNET
, RF
, SRE
, XGBOOST
(optional, default NULL
)
A list
containing for each model a vector
defining which pseudo-absence datasets
are to be used, must be among colnames(bm.format@PA.table)
a BIOMOD.models.options
object returned by the
bm_ModelingOptions
function
a vector
containing evaluation metric names to be used, must
be among ROC
, TSS
, KAPPA
, ACCURACY
, BIAS
, POD
,
FAR
, POFD
, SR
, CSI
, ETS
, HK
, HSS
, OR
,
ORSS
(optional, default NULL
)
An integer
corresponding to the number of permutations to be done for each variable to
estimate variable importance
(optional, default FALSE
)
A logical
value defining whether all models predictions should be scaled with a
binomial GLM or not
(optional, default 1
)
An integer
value corresponding to the number of computing resources to be used to
parallelize the single models computation
(optional, default NULL
)
An integer
value corresponding to the new seed value to be set
(optional, default TRUE
)
A logical
value defining whether the progress bar is to be rendered or not
a character
corresponding to the model name to be computed, must be either
ANN
, CTA
, FDA
, GAM
, GBM
, GLM
, MARS
,
MAXENT
, MAXNET
, RF
, SRE
, XGBOOST
a character
corresponding to the model to be run (sp.name + pa.id +
run.id)
(optional, default .
)
A character
corresponding to the modeling folder
a data.frame
containing observations, coordinates and environmental
variables that can be obtained with the get_species_data
function
a vector
containing observation weights the concerned pseudo-absence
(or allData
) dataset
a vector
containing calibration / validation lines for the
concerned pseudo-absence (or allData
) x repetition (or allRun
) combination
(optional, default NULL
)
A data.frame
containing validation observations, coordinates and environmental
variables that can be obtained with the get_eval_data
function
Damien Georges
rpart
, prune
, gbm
,
nnet
, earth
,
fda
, mars
, maxnet
,
randomForest
, xgboost
,
bm_ModelingOptions
, BIOMOD_Modeling
,
bm_MakeFormula
, bm_SampleFactorLevels
,
bm_FindOptimStat
, bm_VariablesImportance
Other Secundary functions:
bm_BinaryTransformation()
,
bm_CrossValidation()
,
bm_FindOptimStat()
,
bm_MakeFormula()
,
bm_ModelingOptions()
,
bm_PlotEvalBoxplot()
,
bm_PlotEvalMean()
,
bm_PlotRangeSize()
,
bm_PlotResponseCurves()
,
bm_PlotVarImpBoxplot()
,
bm_PseudoAbsences()
,
bm_SRE()
,
bm_SampleBinaryVector()
,
bm_SampleFactorLevels()
,
bm_Tuning()
,
bm_VariablesImportance()