ecospat: ecospat.CCV.createDataSplitTable – R documentation

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ecospat.CCV.createDataSplitTable

Creates a DataSplitTable for usage in ecospat.ccv.modeling.

Description

Creates a DataSplitTable with calibration and evaluation data either for cross-validation or repeated split sampling at the community level (i.e., across all species).

Usage

ecospat.CCV.createDataSplitTable(NbRunEval, 
                                 DataSplit,
                                 validation.method,
                                 NbSites,
                                 sp.data=NULL,
                                 minNbPresences=NULL,
                                 minNbAbsences=NULL,
                                 maxNbTry=1000)

Arguments

`NbRunEval`	number of cross-validation or split sample runs
`DataSplit`	proportion (%) of sites used for model calibration
`validation.method`	the type of `DataSplitTable` that should be created. Must be either `'cross-validation'` or `'split-sample'`
`NbSites`	number of total sites available. Is ignored if sp.data is provided.
`sp.data`	a data.frame where the rows are sites and the columns are species (values 1,0)
`minNbPresences`	the desired minimum number of Presences required in each run
`minNbAbsences`	the desired minimum number of Absences required in each run
`maxNbTry`	number of random tries allowed to create a fitting DataSplitTable

Details

If a sp.data data.frame with species presences and absences is provided the function tries to create a DataSplitTable which ensures that the maximum possible number of species can be modelled (according to the specified minimum presences and absences.)

Value

DataSplitTable

a matrix with TRUE/FALSE for each model run (TRUE=Calibration point, FALSE=Evaluation point)

Author(s)

Daniel Scherrer <daniel.j.a.scherrer@gmail.com>

Examples

#Creating a DataSplitTable for 200 sites, 25 runs with an 
#80/20 calibration/evaluation cross-validation

DataSplitTable <- ecospat.CCV.createDataSplitTable(NbSites = 200, 
                                                   NbRunEval=25, 
                                                   DataSplit=80, 
                                                   validation.method='cross-validation')
                                                   
#Loading species occurence data and remove empty communities
testData <- ecospat.testData[,c(24,34,43,45,48,53,55:58,60:63,65:66,68:71)]
sp.data <- testData[which(rowSums(testData)>0), sort(colnames(testData))]

#Creating a DataSplitTable based on species data directly
DataSplitTable <- ecospat.CCV.createDataSplitTable(NbRunEval = 20,
                                                   DataSplit = 70,
                                                   validation.method = "cross-validation",
                                                   NbSites = NULL,
                                                   sp.data = sp.data, 
                                                   minNbPresence = 15, 
                                                   minNbAbsences = 15, 
                                                   maxNbTry = 250)

ecospat

Spatial Ecology Miscellaneous Methods

v3.2

GPL

Authors

Olivier Broennimann [cre, aut, ctb], Valeria Di Cola [aut, ctb], Blaise Petitpierre [ctb], Frank Breiner [ctb], Daniel Scherrer [ctb], Manuela D`Amen [ctb], Christophe Randin [ctb], Robin Engler [ctb], Wim Hordijk [ctb], Heidi Mod [ctb], Julien Pottier [ctb], Mirko Di Febbraro [ctb], Loic Pellissier [ctb], Dorothea Pio [ctb], Ruben Garcia Mateo [ctb], Anne Dubuis [ctb], Luigi Maiorano [ctb], Achilleas Psomas [ctb], Charlotte Ndiribe [ctb], Nicolas Salamin [ctb], Niklaus Zimmermann [ctb], Antoine Guisan [aut]

Initial release

2021-02-17