# Neural network evaluation by CV

### Description

Evaluation for Artificial Neural Network (ANN) classification by cross-validation

### Usage

1 2 |

### Arguments

`X` |
standardized complete X data matrix (training and test data) |

`grp` |
factor with groups for complete data (training and test data) |

`train` |
row indices of X indicating training data objects |

`kfold` |
number of folds for cross-validation |

`decay` |
weight decay, see |

`size` |
number of hidden units, see |

`maxit` |
maximal number of iterations for ANN, see |

`plotit` |
if TRUE a plot will be generated |

`legend` |
if TRUE a legend will be added to the plot |

`legpos` |
positioning of the legend in the plot |

`...` |
additional plot arguments |

### Details

The data are split into a calibration and a test data set (provided by "train"). Within the calibration set "kfold"-fold CV is performed by applying the classification method to "kfold"-1 parts and evaluation for the last part. The misclassification error is then computed for the training data, for the CV test data (CV error) and for the test data.

### Value

`trainerr` |
training error rate |

`testerr` |
test error rate |

`cvMean` |
mean of CV errors |

`cvSe` |
standard error of CV errors |

`cverr` |
all errors from CV |

`decay` |
value(s) for weight decay, taken from input |

`size` |
value(s) for number of hidden units, taken from input |

### Author(s)

Peter Filzmoser <P.Filzmoser@tuwien.ac.at>

### References

K. Varmuza and P. Filzmoser: Introduction to Multivariate Statistical Analysis in Chemometrics. CRC Press, Boca Raton, FL, 2009.

### See Also

`nnet`

### Examples

1 2 3 4 5 6 7 8 9 10 11 12 |