n | R Documentation |
This smooth constructor implements single hidden layer neural networks.
## 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"))
... |
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. |
Function n()
, similar to function s
a simple smooth specification
object.
bamlss
, predict.bamlss
, bfit
, boost
## ... coming soon ...!
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