Ensamble of Small Models: Projects Simple Bivariate Models Into New Space Or Time
This function projects simple bivariate models on new.env
ecospat.ESM.Projection(ESM.modeling.output,
new.env,
name.env,
parallel,
cleanup)ESM.modeling.output |
|
new.env |
A set of explanatory variables onto which models will be projected. It could be a |
name.env |
A name for the new.env object. If not specified (default) the name of the new.env object will be used. It is necessary to specify a unique name when projecting various new.env objects in a loop. |
parallel |
Logical. If TRUE, the parallel computing is enabled |
cleanup |
Numeric. Calls removeTmpFiles() to delete all files from rasterOptions()$tmpdir which are older than the given time (in hours). This might be necessary to prevent running over quota. No cleanup is used by default |
The basic idea of ensemble of small models (ESMs) is to model a species distribution based on small, simple models, for example all possible bivariate models (i.e. models that contain only two predictors at a time out of a larger set of predictors), and then combine all possible bivariate models into an ensemble (Lomba et al. 2010; Breiner et al. 2015).
The ESM set of functions could be used to build ESMs using simple bivariate models which are averaged using weights based on model performances (e.g. AUC) accoring to Breiner et al (2015). They provide full functionality of the approach described in Breiner et al. (2015).
The name of new.env must be a regular expression (see ?regex)
Returns the projections for all selected models (same as in biomod2)
See "BIOMOD.projection.out" for details.
Frank Breiner frank.breiner@wsl.ch
with contributions of Olivier Broennimann olivier.broennimann@unil.ch
Lomba, A., L. Pellissier, C.F. Randin, J. Vicente, F. Moreira, J. Honrado and A. Guisan. 2010. Overcoming the rare species modelling paradox: A novel hierarchical framework applied to an Iberian endemic plant. Biological Conservation, 143,2647-2657.
Breiner F.T., A. Guisan, A. Bergamini and M.P. Nobis. 2015. Overcoming limitations of modelling rare species by using ensembles of small models. Methods in Ecology and Evolution, 6,1210-1218.
Breiner F.T., Nobis M.P., Bergamini A., Guisan A. 2018. Optimizing ensembles of small models for predicting the distribution of species with few occurrences. Methods in Ecology and Evolution. doi: 10.1111/2041-210X.12957
library(biomod2)
# Loading test data
data(ecospat.testNiche.inv)
inv <- ecospat.testNiche.inv
# species occurrences
xy <- inv[,1:2]
sp_occ <- inv[11]
# env
current <- inv[3:10]
### Formating the data with the BIOMOD_FormatingData() function from the package biomod2
sp <- 1
myBiomodData <- BIOMOD_FormatingData( resp.var = as.numeric(sp_occ[,sp]),
expl.var = current,
resp.xy = xy,
resp.name = colnames(sp_occ)[sp])
### Calibration of simple bivariate models
my.ESM <- ecospat.ESM.Modeling( data=myBiomodData,
models=c('GLM','RF'),
NbRunEval=2,
DataSplit=70,
weighting.score=c("AUC"),
parallel=FALSE)
### Evaluation and average of simple bivariate models to ESMs
my.ESM_EF <- ecospat.ESM.EnsembleModeling(my.ESM,weighting.score=c("SomersD"),threshold=0)
### Projection of simple bivariate models into new space
my.ESM_proj_current<-ecospat.ESM.Projection(ESM.modeling.output=my.ESM,
new.env=current)
### Projection of calibrated ESMs into new space
my.ESM_EFproj_current <- ecospat.ESM.EnsembleProjection(ESM.prediction.output=my.ESM_proj_current,
ESM.EnsembleModeling.output=my.ESM_EF)
## get the model performance of ESMs
my.ESM_EF$ESM.evaluations
## get the weights of the single bivariate models used to build the ESMs
my.ESM_EF$weights
## get the variable contributions of ESMs
ecospat.ESM.VarContrib(my.ESM,my.ESM_EF)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.