aicc: AICc

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/aicc.R

Description

Compute the Akaike Information Criterion corrected for small samples size (Warren and Seifert, 2011).

Usage

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aicc(model, env)

Arguments

model

SDMmodel object.

env

stack containing the environmental variables.

Details

The function is available only for Maxent and Maxnet methods.

Value

The computed AICc

Author(s)

Sergio Vignali

References

Warren D.L., Seifert S.N., (2011). Ecological niche modeling in Maxent: the importance of model complexity and the performance of model selection criteria. Ecological Applications, 21(2), 335–342.

See Also

auc and tss.

Examples

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# Acquire environmental variables
files <- list.files(path = file.path(system.file(package = "dismo"), "ex"),
                    pattern = "grd", full.names = TRUE)
predictors <- raster::stack(files)

# Prepare presence and background locations
p_coords <- virtualSp$presence
bg_coords <- virtualSp$background

# Create SWD object
data <- prepareSWD(species = "Virtual species", p = p_coords, a = bg_coords,
                   env = predictors, categorical = "biome")

# Train a model
model <- train(method = "Maxnet", data = data, fc = "l")

# Compute the AICc
aicc(model, predictors)

Example output

   _____  ____   __  ___ __
  / ___/ / __ \ /  |/  // /_ __  __ ____   ___
  \__ \ / / / // /|_/ // __// / / // __ \ / _ \
 ___/ // /_/ // /  / // /_ / /_/ // / / //  __/
/____//_____//_/  /_/ \__/ \__,_//_/ /_/ \___/  version 1.1.3

To cite this package in publications type: citation("SDMtune").
Extracting predictor information for presence locations...
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Extracting predictor information for absence/background locations...
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[1] 6731.672

SDMtune documentation built on July 17, 2021, 9:06 a.m.