ensemble: Ensemble Forecasting of SDMs

In sdm: Species Distribution Modelling

Description

Make a Raster object with a weighted averaging over all predictions from several fitted model in a sdmModel object.

Usage

## S4 method for signature 'sdmModels'
ensemble(x, newdata, filename="",setting,...)

Arguments

x	a sdmModels object
newdata	Raster* object or data.frame
filename	character, output file name
setting	list, contains the parameters that are used in the ensemble procedure; see details
...	additional arguments passed to the predict function

Details

ensemble function uses the fitted models in an sdmModels object to generate an ensemble/consensus of predictions by individual models. Several methods do exist for this procedure, that are (or will be) implemented in this function, and can be defined in the method argument. A list can be introduced in the setting argument in which several parameters can be set including:

- method: specify which ensemble method should be used. Currently, 'unweighted' (unweighted averaging), and 'weighted' (weighted averaging) are implemented, but more methods will be added.

- stat: if the method='weighted' is used, this specify which evaluation statistics can be used as weight in the weighted averaging procedure. Alternatively, one may directly introduce weights (see the next argument)

- weights: an optional numeric vector (with a length equal to the models that are successfully fitted) to specify the weights for weighted averaging procedure (if the method='weighted' is specified)

- id: specify the model IDs that should be considered in the ensemble procedure. If missing, all the models that are successfully fitted are considered.

- wtest: specify which test dataset ("training","test.dep","test.indep") should be used to extract the statistic (stat) values as weights (if a relevant method is specified)

Value

- a Raster object if predictors is a Raster object

- a numeric vector if predictors is a data.frame object

Author(s)

Babak Naimi [e-mail]

http://biogeoinformatics.org

References

#

See Also

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Examples

## Not run: 


file <- system.file("external/species.shp", package="sdm") # get the location of the species data

species <- shapefile(file) # read the shapefile

path <- system.file("external", package="sdm") # path to the folder contains the data

lst <- list.files(path=path,pattern='asc$',full.names = T) # list the name of the raster files 


# stack is a function in the raster package, to read/create a multi-layers raster dataset
preds <- stack(lst) # making a raster object

d <- sdmData(formula=Occurrence~., train=species, predictors=preds)

d

# fit the models (5 methods, and 10 replications using bootstrapping procedure):
m <- sdm(Occurrence~.,data=d,methods=c('rf','tree','fda','mars','svm'),
          replicatin='boot',n=10)
    
# ensemble using weighted averaging based on AUC statistic:    
p1 <- ensemble(m, newdata=preds, filename='ens.img',setting=list(method='weighted',stat='AUC'))
plot(p1)

# ensemble using weighted averaging based on TSS statistic
# and optimum threshold critesion 2 (i.e., Max(spe+sen)) :    
p2 <- ensemble(m, newdata=preds, filename='ens2.img',setting=list(method='weighted',
                                                                  stat='TSS',opt=2))
plot(p2)


## End(Not run)

sdm documentation built on May 2, 2019, 6:32 p.m.