n {bamlss} | R Documentation |

## Neural Networks for BAMLSS

### Description

This smooth constructor implements single hidden layer neural networks.

### Usage

```
## The neural network smooth constructor.
n(..., k = 10, type = 2)
## Initialize weights.
n.weights(nodes, k, r = NULL, s = NULL,
type = c("sigmoid", "gauss", "softplus", "cos", "sin"),
x = NULL, ...)
## Second weights initializer, internally calls n.weights.
make_weights(object, data, dropout = 0.2)
## Boosted neural net predictions.
predictn(object, newdata, model = NULL,
mstop = NULL, type = c("link", "parameter"))
```

### Arguments

`...` |
For function |

`k` |
For function |

`type` |
Integer. Type |

`nodes` |
Number of nodes for each layer, i.e., can also be a vector. |

`r` , `s` |
Parameters controlling the shape of the activation functions. |

`x` |
A scaled covariate matrix, the data will be used to identify the range of the weights. |

`object` , `data` |
See |

`dropout` |
The fraction of inner weights that should be set to zero. |

`newdata` |
The data frame that should be used for prediction. |

`model` |
For which parameter of the distribution predictions should be computed. |

`mstop` |
The stopping iteration for which predictions should be computed. The default is to return a matrix of predictions, each column represents the prediction of one boosting iteration. |

### Value

Function `n()`

, similar to function `s`

a simple smooth specification
object.

### See Also

`bamlss`

, `predict.bamlss`

, `bfit`

, `boost`

### Examples

```
## ... coming soon ...!
```

*bamlss*version 1.2-3 Index]