grnn.train {BETS}R Documentation

Train a General Regression Neural Network


Creates a set of probabilistic neural networks as proposed by Specht [1991]. The user provides a set of regressors and the function chooses which subset is the best, based on an accuracy measure (by default, the MAPE) between fited and actual values. These networks have only one parameter, the sigma, which is the standard deviation of each activation function (gaussian) of the pattern layer. Sigma can also be automatically chosen. This function builds on grnn-package.


grnn.train(train.set, sigma, step = 0.1, select = TRUE, names = NA)



A ts list (a list of ts objects). The first element must be the dependent variable. The other elements, the regressors.


A numeric or a numeric vector. The sigma parameter, that is, the standard deviation of the activation functions (gaussians) of the pattern layer. Can be either a fixed value or a range (a vector containing the minimum and the maximum values).


A numeric value. If sigma is a range, the user must provide a step value to vary sigma. The function is going to select the best sigma based on MAPE.


A boolean. Must be set to FALSE if the regressors should not be chosen. The default is TRUE.


A character vector. Optional. The names of the regressors. If not provided, indexes will be used and reported.


A list of result objects, each representing a network. These objects are ordered by MAPE (the 20 best MAPEs) and its fields are:

grnn.train also returns a diagnostic of training rounds and a sigma versus accuracy plot.


Talitha Speranza

[Package BETS version 0.4.9 Index]