n {bamlss} | 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 ...!