Fit MONMLP model via nlm optimization function

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Description

Helper function used to fit a MONMLP model via the nlm routine.

Usage

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monmlp.nlm(x, y, hidden1, hidden2 = 0, iter.max = 5000,
           n.trials = 1, Th = tansig, To = linear,
           Th.prime = tansig.prime, To.prime = linear.prime,
           monotone = NULL, init.weights = c(-0.5, 0.5),
           max.exceptions = 10, silent = FALSE, ...)

Arguments

x

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

y

predictand matrix with number of rows equal to the number of samples and number of columns equal to the number of predictands.

hidden1

number of hidden nodes in the first hidden layer.

hidden2

number of hidden nodes in the second hidden layer.

iter.max

maximum number of iterations of the nlm optimization algorithm.

n.trials

number of repeated trials used to avoid local minima.

Th

hidden layer transfer function.

To

output layer transfer function.

Th.prime

derivative of the hidden layer transfer function.

To.prime

derivative of the output layer transfer function.

monotone

column indices of covariates for which the monotonicity constraint should hold.

init.weights

either a vector giving the minimum and maximum allowable values of the random weights or an initial weight vector.

max.exceptions

maximum number of exceptions of the nlm routine before fitting is terminated with an error.

silent

logical determining if diagnostic messages should be suppressed.

...

additional parameters passed to the nlm optimization routine.

Value

a list containing elements

weights

final weight vector

cost

final value of the cost function

code

termination code from nlm

See Also

monmlp.fit