ecospat (version 3.2)

ecospat.ESM.responsePlot: Produce response plots for ESMs

Description

This function creates response plots (evaluation strips) for Ensmebles of Small Models (ESMs).

Usage

ecospat.ESM.responsePlot( ESM.EnsembleModeling.output,
                              ESM.modeling.output,
                              fixed.var.metric = 'median')

Arguments

ESM.modeling.output

a list object returned by ecospat.ESM.Modeling

ESM.EnsembleModeling.output

a list object returned by ecospat.ESM.EnsembleModeling

fixed.var.metric

either 'median' (default), 'mean', 'min' or 'max' specifying the statistic used to fix as constant the remaining variables when the predicted response is estimated for one of the variables. (same as in response.plot2

Value

A plot of the response curves is produced (red line Ensemble, grey lines single algorithms) and a list with the output is provided.

Details

This function plots the response curves of a model for each variable, while keeping the remianing variables constant. This is an adaptation of the Evaluation Strip method proposed by Elith et al.(2005)

References

Elith, J., Ferrier, S., Huettmann, FALSE. & Leathwick, J. R. 2005 The evaluation strip: A new and robust method for plotting predicted responses from species distribution models. Ecological Modelling 186, 280-289.

Examples

# NOT RUN {
   
# }
# NOT RUN {
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','CTA'),
                                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)
output.plot <- ecospat.ESM.responsePlot(ESM.EnsembleModeling.output = my.ESM_EF,
                         ESM.modeling.output = my.ESM)

# }