# n

##### Neural Networks for BAMLSS

This smooth constructor implements single hidden layer neural networks.

- Keywords
- regression

##### 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

`n()`

a formula of the type`~x1+x2+x3`

that specifies the covariates that should be modeled by the neural network. For function`predictn()`

, arguments to be passed to`predict.bamlss`

.- k
For function

`n()`

, the number of hidden nodes of the network. Note that one can set an argument`split = TRUE`

to split up the neural network into, e.g.,`nsplit = 5`

parts with`k`

nodes each. For function`n.weights()`

, argument`k`

is the number of input variables of the network (number of covariates).- type
Integer. Type

`1`

fits a complete network in each boosting iteration,`type = 2`

selects the best fitting node in each boosting iteration. for function`n.weights()`

, the type of activation function that should be used. For function`predictn()`

, the type of prediction that should be computed.- 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

`smooth.construct`

. For function`predictn()`

, a boosted`"bamlss"`

object.- 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

##### Examples

```
# NOT RUN {
## ... coming soon ...!
# }
```

*Documentation reproduced from package bamlss, version 1.1-2, License: GPL-2 | GPL-3*