Fit a QRNN model via nlm()

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Description

Helper function used to fit a QRNN model via the nlm() function and a variant of the finite smoothing algorithm.

Usage

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qrnn.nlm(x, y, n.hidden, tau, iter.max, n.trials, bag,
         lower, eps.seq, Th, Th.prime, penalty, trace,
         ...)

Arguments

x

covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables.

y

predictand column matrix with number of rows equal to the number of samples.

n.hidden

number of hidden nodes in the QRNN model.

tau

desired tau-quantile.

iter.max

maximum number of iterations of the optimization algorithm.

n.trials

number of repeated trials used to avoid local minima.

bag

logical variable indicating whether or not bootstrap aggregation (bagging) should be used.

lower

left censoring point.

eps.seq

sequence of eps values for the finite smoothing algorithm; used in huber and related functions.

Th

hidden layer transfer function; use sigmoid for a nonlinear model and linear for a linear model.

Th.prime

derivative of the hidden layer transfer function Th.

penalty

weight penalty for weight decay regularization.

trace

logical variable indicating whether or not diagnostic messages are printed during optimization.

...

additional parameters passed to the nlm optimization routine.

Value

a list containing elements

W1

matrix of optimized input-hidden layer weights.

W2

matrix of optimized hidden-output layer weights.

See Also

qrnn.cost, qrnn.fit, qrnn.eval