grnn: General Regression Neural Networks (GRNNs)

In GRNNs: General Regression Neural Networks Package

Description

This GRNNs uses various distance functions including: "euclidean", "minkowski", "manhattan", "maximum", "canberra", "angular", "correlation", "absolute_correlation", "hamming", "jaccard","bray", "kulczynski", "gower", "altGower", "morisita", "horn", "mountford", "raup", "binomial", "chao", "cao","mahalanobis".

Usage

grnn(p_input, p_train, v_train, fun = "euclidean", best.spread, scale = TRUE)

Arguments

p_input	The dataframe of input predictors
p_train	The dataframe of training predictor dataset
v_train	The dataframe of training response variables
fun	The distance function
best.spread	The vector of best spreads
scale	The logic statements (TRUE/FALSE)

Value

The predictions

Examples

data("met")
data("physg")
best.spread<-c(0.33,0.33,0.31,0.34,0.35,0.35,0.32,0.31,0.29,0.35,0.35)
predict<-physg[1,]
physg.train<-physg[-1,]
met.train<-met[-1,]
prediction<-grnn(predict,physg.train,met.train,fun="euclidean",best.spread,scale=TRUE)

GRNNs documentation built on Sept. 8, 2021, 5:09 p.m.