grnn.search_rsq: Search for the optimal value of GRNN smoothing parameter...
In yager: Yet Another General Regression Neural Network

Description Usage Arguments Value Examples

The function grnn.search_rsq searches for the optimal value of GRNN smoothing parameter by cross-validation. It is applicable to the functional approximation

1	grnn.search_rsq(net, sigmas, nfolds = 4, seed = 1)

`net`	A GRNN object generated by grnn.fit()
`sigmas`	A numeric vector to search for the best smoothing parameter
`nfolds`	A scalar for the number of n-fold, 4 by default
`seed`	The seed value for the n-fold cross-validation, 1 by default

The list of all searching outcomes and the best outcome

data(iris, package = "datasets")
Y <- ifelse(iris[, 5] == "setosa", 1, 0)
X <- scale(iris[, 1:4])
gnet <- grnn.fit(x = X, y = Y)
grnn.search_rsq(net = gnet, sigmas = seq(3), nfolds = 2)