```
#' Calculates the optimal number of clusters using a cluster R^2 equivalent metric, namely, the between sum of squares over the total sum of squares metric.
#'
#'@param Data The data that you wish to examine containing the cluster dimensions
#'@param clusterIter The number of clusters centers for examination. A setting of 10 will run 2 through 10 cluster functions and return the best choice.
#'@param threshold The difference in the cluster R^2 must meet in order to include the next cluster iteration.
#'@author Helena Ristov
#'
#'@export
numClusters <- function(Data, clusterIter = 10, threshold = .01){
for(i in 2:clusterIter){
cData <- kmeans(Data, centers = i, nstart= 100)
if(i == 2){
explainedVar <- cData$betweenss/cData$totss
}else{
nextExpVar <- cData$betweenss/cData$totss
explainedVar <- rbind(explainedVar,nextExpVar)
}
}
numClusters <- first(which(diff(explainedVar) < threshold))+1
xaxis <- seq(2,clusterIter, by=1)
p <- qplot(x = xaxis, y = explainedVar, geom = c("point", "line"), xlab = 'Clusters', main = 'Explained Variance by Number of Cluster Centers')
p <- p + geom_line(size=0.8, colour='blue') + geom_point(colour="black", size = 4.5)
plot(p)
return(numClusters)
}
```

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