BIOMOD.models.out, BIOMOD.projection.out or BIOMOD.ensemble.models.out objectsR/biomod2_classes_3.R
getters.out.RdThese functions allow the user to easily retrieve informations stored in the different biomod2 objects from the different modeling steps, such as modeling options and formated data, models used or not, predictions, evaluations, variables importance.
# S4 method for class 'BIOMOD.formated.data'
get_species_data(obj)
# S4 method for class 'BIOMOD.formated.data.PA'
get_species_data(obj)
# S4 method for class 'BIOMOD.formated.data'
get_eval_data(obj)
# S4 method for class 'BIOMOD.models.out'
get_options(obj)
# S4 method for class 'BIOMOD.models.out'
get_calib_lines(obj, as.data.frame = FALSE, PA = NULL, run = NULL)
# S4 method for class 'BIOMOD.models.out'
get_formal_data(obj, subinfo = NULL)
# S4 method for class 'BIOMOD.models.out'
get_predictions(
obj,
evaluation = FALSE,
full.name = NULL,
PA = NULL,
run = NULL,
algo = NULL,
model.as.col = FALSE
)
# S4 method for class 'BIOMOD.models.out'
get_built_models(obj, full.name = NULL, PA = NULL, run = NULL, algo = NULL)
# S4 method for class 'BIOMOD.models.out'
get_evaluations(
obj,
full.name = NULL,
PA = NULL,
run = NULL,
algo = NULL,
metric.eval = NULL
)
# S4 method for class 'BIOMOD.models.out'
get_variables_importance(
obj,
full.name = NULL,
PA = NULL,
run = NULL,
algo = NULL,
expl.var = NULL
)
# S4 method for class 'BIOMOD.projection.out'
get_projected_models(
obj,
full.name = NULL,
PA = NULL,
run = NULL,
algo = NULL,
merged.by.algo = NULL,
merged.by.run = NULL,
merged.by.PA = NULL,
filtered.by = NULL
)
# S4 method for class 'BIOMOD.projection.out'
free(obj)
# S4 method for class 'BIOMOD.projection.out'
get_predictions(
obj,
metric.binary = NULL,
metric.filter = NULL,
full.name = NULL,
PA = NULL,
run = NULL,
algo = NULL,
merged.by.algo = NULL,
merged.by.run = NULL,
merged.by.PA = NULL,
filtered.by = NULL,
model.as.col = FALSE,
...
)
# S4 method for class 'BIOMOD.ensemble.models.out'
get_formal_data(obj, subinfo = NULL)
# S4 method for class 'BIOMOD.ensemble.models.out'
get_built_models(
obj,
full.name = NULL,
merged.by.algo = NULL,
merged.by.run = NULL,
merged.by.PA = NULL,
filtered.by = NULL,
algo = NULL
)
# S4 method for class 'BIOMOD.ensemble.models.out'
get_kept_models(obj)
# S4 method for class 'BIOMOD.ensemble.models.out'
get_predictions(
obj,
evaluation = FALSE,
full.name = NULL,
merged.by.algo = NULL,
merged.by.run = NULL,
merged.by.PA = NULL,
filtered.by = NULL,
algo = NULL,
model.as.col = FALSE
)
# S4 method for class 'BIOMOD.ensemble.models.out'
get_evaluations(
obj,
full.name = NULL,
merged.by.algo = NULL,
merged.by.run = NULL,
merged.by.PA = NULL,
filtered.by = NULL,
algo = NULL,
metric.eval = NULL
)
# S4 method for class 'BIOMOD.ensemble.models.out'
get_variables_importance(
obj,
full.name = NULL,
merged.by.algo = NULL,
merged.by.run = NULL,
merged.by.PA = NULL,
filtered.by = NULL,
algo = NULL,
expl.var = NULL
)a BIOMOD.formated.data, BIOMOD.formated.data.PA,
BIOMOD.models.out, BIOMOD.projection.out or
BIOMOD.ensemble.models.out object
a logical defining whether output should be returned as
data.frame or array object
(optional, default NULL)
A vector containing pseudo-absence set to be loaded, must be among PA1,
PA2, ..., allData
(optional, default NULL)
A vector containing repetition set to be loaded, must be among RUN1,
RUN2, ..., allRun
a character corresponding to the information to be extracted, must be
among NULL, expl.var.names, resp.var, expl.var, MinMax,
eval.resp.var, eval.expl.var (see Details)
a logical defining whether evaluation data should be used or not
(optional, default NULL)
A vector containing model names to be kept, must be either all or a
sub-selection of model names that can be obtained with the get_built_models
function
(optional, default NULL)
A character containing algorithm to be loaded, must be either
ANN, CTA, DNN, FDA, GAM, GBM, GLM, MARS,
MAXENT, MAXNET, RF, SRE, XGBOOST
(optional, default FALSE)
A boolean given to get_predictions. If TRUE
prediction are returned as a wide data.frame with each column containing
predictions for a single model and corresponding to the old output given by
biomod2 in version < 4.2-2. If FALSE predictions are returned
as a long data.frame with many additional informations readily
available.
(optional, default NULL)
A vector containing evaluation metric to be kept, must be among POD,
FAR, POFD, SR, ACCURACY, BIAS, AUCroc, AUCprg, TSS,
KAPPA, OR, ORSS, CSI, ETS, BOYCE, MPA
(optional, default NULL)
A vector containing explanatory variables to be kept, that can be obtained with the
get_formal_data(obj, subinfo = 'expl.var.names') function
(optional, default NULL)
A character containing merged algorithm to be loaded, must be among
ANN, CTA, DNN, FDA, GAM, GBM, GLM, MARS,
MAXENT, MAXNET, RF, SRE, XGBOOST, mergedAlgo
(optional, default NULL)
A vector containing merged repetition set to be loaded, must be among RUN1,
RUN2, ..., mergedRun
(optional, default NULL)
A vector containing merged pseudo-absence set to be loaded, must be among PA1,
PA2, ..., mergedData
(optional, default NULL)
A vector containing evaluation metric selected to filter single models to build the
ensemble models, must be among POD, FAR, POFD, SR,
ACCURACY, BIAS, AUCroc, AUCprg, TSS, KAPPA, OR, ORSS,
CSI, ETS, BOYCE, MPA
(optional, default NULL)
A vector containing evaluation metric selected to transform predictions into binary
values, must be among POD, FAR, POFD, SR, ACCURACY,
BIAS, AUCroc, AUCprg, TSS, KAPPA, OR, ORSS, CSI,
ETS, BOYCE, MPA
(optional, default NULL)
A vector containing evaluation metric to filter predictions, must be among POD,
FAR, POFD, SR, ACCURACY, BIAS, AUCroc, AUCprg, TSS,
KAPPA, OR, ORSS, CSI, ETS, BOYCE, MPA
(optional, one or several of the following arguments depending on the selected function)
get_species_dataa data.frame combining data.species,
coord, data.env.var (and PA.table) slots of
BIOMOD.formated.data (or BIOMOD.formated.data.PA) object
get_eval_dataa data.frame combining eval.data.species,
eval.coord, eval.data.env.var slots of
BIOMOD.formated.data or BIOMOD.formated.data.PA object
get_optionsa
BIOMOD.stored.options-class object from the
models.options slot of a BIOMOD.models.out-class
object
get_calib_linesa
BIOMOD.stored.data.frame-class object from the calib.lines
slot of a BIOMOD.models.out object
get_projected_modelsa vector from the
models.projected slot of a BIOMOD.projection.out
object
get_predictionsa BIOMOD.stored.data object
from the proj.out slot of a BIOMOD.models.out,
BIOMOD.projection.out or
BIOMOD.ensemble.models.out object
get_kept_modelsa vector containing names of the kept
models of a BIOMOD.ensemble.models.out object
get_formal_datadepending on the subinfo parameter :
NULLa BIOMOD.stored.formated.data-class (or
BIOMOD.stored.models.out-class) object from the
formated.input.data (or models.out) slot of a
BIOMOD.models.out (or
BIOMOD.ensemble.models.out) object
expl.var.namesa vector from the
expl.var.names slot of a BIOMOD.models.out or
BIOMOD.ensemble.models.out object
resp.vara vector from the data.species slot
of the formated.input.data slot of a
BIOMOD.models.out or
BIOMOD.ensemble.models.out object
expl.vara data.frame from the data.env.var
slot of the formated.input.data slot of a
BIOMOD.models.out or
BIOMOD.ensemble.models.out object
MinMaxa list of minimum and maximum values (or
levels if factorial) of variable contained in the data.env.var
slot of the formated.input.data slot of a
BIOMOD.models.out or
BIOMOD.ensemble.models.out object
eval.resp.vara vector from the
eval.data.species slot of the formated.input.data slot of
a BIOMOD.models.out or
BIOMOD.ensemble.models.out object
eval.expl.vara data.frame from the
eval.data.env.var slot of the formated.input.data slot of
a BIOMOD.models.out or
BIOMOD.ensemble.models.out object
get_built_modelsa vector from the
models.computed slot (or em.computed) of a
BIOMOD.models.out (or
BIOMOD.ensemble.models.out) object
get_evaluationsa data.frame from the models.evaluation
slot (or model_evaluation of each model in em.computed) of a
BIOMOD.models.out (or BIOMOD.ensemble.models.out)
object. Contains evaluation metric for different models and dataset.
Evaluation metric are calculated on the calibrating data (column calibration),
on the cross-validation data (column validation) or on the evaluation data
(column evaluation).
For cross-validation data, see CV.[...]
parameters in BIOMOD_Modeling function ; for evaluation data, see
eval.[...] parameters in BIOMOD_FormatingData.
get_variables_importancea
BIOMOD.stored.data.frame-class from
the variables.importance slot (or model_variables_importance
of each model in em.models) of a BIOMOD.models.out
(or BIOMOD.ensemble.models.out) object
BIOMOD.models.out, BIOMOD.projection.out,
BIOMOD.ensemble.models.out
Other Toolbox functions:
getters.bm,
load_stored_object(),
predict.bm,
predict.em,
predict2.bm,
predict2.em,
setters