# Fit MONMLP model via nlm optimization function

### Description

Helper function used to fit a MONMLP model via the `nlm`

routine.

### Usage

1 2 3 4 5 | ```
monmlp.nlm(x, y, hidden1, hidden2 = 0, iter.max = 5000,
n.trials = 1, Th = tansig, To = linear,
Th.prime = tansig.prime, To.prime = linear.prime,
monotone = NULL, init.weights = c(-0.5, 0.5),
max.exceptions = 10, silent = FALSE, ...)
``` |

### Arguments

`x` |
covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of covariates. |

`y` |
predictand matrix with number of rows equal to the number of samples and number of columns equal to the number of predictands. |

`hidden1` |
number of hidden nodes in the first hidden layer. |

`hidden2` |
number of hidden nodes in the second hidden layer. |

`iter.max` |
maximum number of iterations of the |

`n.trials` |
number of repeated trials used to avoid local minima. |

`Th` |
hidden layer transfer function. |

`To` |
output layer transfer function. |

`Th.prime` |
derivative of the hidden layer transfer function. |

`To.prime` |
derivative of the output layer transfer function. |

`monotone` |
column indices of covariates for which the monotonicity constraint should hold. |

`init.weights` |
either a vector giving the minimum and maximum allowable values of the random weights or an initial weight vector. |

`max.exceptions` |
maximum number of exceptions of the |

`silent` |
logical determining if diagnostic messages should be suppressed. |

`...` |
additional parameters passed to the |

### Value

a list containing elements

`weights` |
final weight vector |

`cost` |
final value of the cost function |

`code` |
termination code from |

### See Also

`monmlp.fit`