R: Neural Networks for BAMLSS

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 `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

`bamlss`, `predict.bamlss`, `bfit`, `boost`

### Examples

    ## ... coming soon ...!

[Package bamlss version 1.1-6 Index]