Get weights for the skip layer in a neural network
Get weights for the skip layer in a neural network, only valid for networks created using skip = TRUE with the nnet function.
neuralskips(mod_in, ...) ## S3 method for class 'nnet' neuralskips(mod_in, rel_rsc = NULL, ...)
mod_in |
input object for which an organized model list is desired. |
... |
arguments passed to other methods |
rel_rsc |
numeric indicating the scaling range for the width of connection weights in a neural interpretation diagram. Default is |
This function is similar to neuralweights except only the skip layer weights are returned.
Returns a list of connections for each output node, where each element of the list is the connection for each input node in sequential order to the respective output node. The first weight in each element is not the bias connection, unlike the results for neuralweights.
data(neuraldat) set.seed(123) ## using nnet library(nnet) mod <- nnet(Y1 ~ X1 + X2 + X3, data = neuraldat, size = 5, linout = TRUE, skip = TRUE) neuralskips(mod)
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