Fits various tracks (different random starting values) and chooses best model using nnet

Fits n_init tracks with different initial values and decides on best model based on information criteria.

Usage

nn_fit_nnet(
  x,
  y,
  q,
  n_init,
  inf_crit = "BIC",
  lambda = 0,
  response = "continuous",
  unif = 3,
  maxit = 1000,
  ...
)

Arguments

x

Matrix of covariates

y

Vector of response

q

Number of hidden nodes

n_init

Number of random initialisations (tracks)

inf_crit

Information criterion: "BIC" (default), "AIC" or "AICc"

lambda

Ridge penalty

response

Response type: "continuous" (default) or "binary"

unif

Random initial values max value

maxit

maximum number of iterations for nnet (default = 100)

...

additional argument for nnet

Value

The best model from the different tracks