biomod2 (version 4.2-4)

bm_CVnnet: Cross-validation for Neural Networks

Description

This internal biomod2 function allows the user to compute cross-validation for neural networks in ANN model (see nnet and BIOMOD_Modeling).

Usage

bm_CVnnet(
  Input,
  Target,
  size = c(2, 4, 6, 8),
  decay = c(0.001, 0.01, 0.05, 0.1),
  maxit = 200,
  nbCV = 5,
  weights = NULL,
  seedval = 555
)

Value

A data.frame containing the following elements :

• Size : the size

• Decay : the decay value

• AUC : the corresponding Area Under Curve

Arguments

Input

complete dataset with explanatory variables

Target

calibration dataset with observed presence / absence

size

(see parameter ANN$size in BIOMOD_ModelingOptions)

decay

(see parameter ANN$decay in BIOMOD_ModelingOptions)

maxit

(see parameter ANN$maxit in BIOMOD_ModelingOptions)

nbCV

(see parameter ANN$nbCV in BIOMOD_ModelingOptions)

weights

a vector of numeric values corresponding to weights over calibration lines

seedval

an integer value corresponding to the new seed value to be set

Author

Damien Georges

See Also

nnet, auc, roc, BIOMOD_ModelingOptions, BIOMOD_Modeling, bm_SampleBinaryVector, bm_RunModelsLoop

Other Secundary functions: bm_BinaryTransformation(), bm_CrossValidation(), bm_FindOptimStat(), bm_MakeFormula(), bm_PlotEvalBoxplot(), bm_PlotEvalMean(), bm_PlotRangeSize(), bm_PlotResponseCurves(), bm_PlotVarImpBoxplot(), bm_PseudoAbsences(), bm_RunModelsLoop(), bm_SRE(), bm_SampleBinaryVector(), bm_SampleFactorLevels(), bm_VariablesImportance()