Given new values of the independent variables, tests a list of trained GRNNs and picks the best net, based on an accuracy measure between the forecasted and the actual values.
grnn.test(results, test.set)The object returned by grnn.train.
A ts list. The first element must be the actual values of the dependent variable. The others, the new values of the regressors.
A list object representing the best network (according to forecasting MAPE). Its fields are:
mape: The forecasting MAPE
model: The network object
sigma: The sigma parameter
id: The id number of the network, as given by grnn.train
mean: The predicted values
x: The original series
fitted: The fitted values
actual: The actual values (to be compared with the predicted values)
residuals: Difference between the fitted values and the series original values
regressors: The regressors used to train the network