I tried to construct the clustering method as function the following ways:
mydata <- mtcars
# Here I construct hclust as a function
hclustfunc <- function(x) hclust(as.matrix(x),method="complete")
# Define distance metric
distfunc <- function(x) as.dist((1-cor(t(x)))/2)
# Obtain distance
d <- distfunc(mydata)
# Call that hclust function
fit<-hclustfunc(d)
# Later I'd do
# plot(fit)
But why it gives the following error:
Error in if (is.na(n) || n > 65536L) stop("size cannot be NA nor exceed 65536") :
missing value where TRUE/FALSE needed
What's the right way to do it?
Do read the help for functions you use. ?hclust
is pretty clear that the first argument d
is a dissimilarity object, not a matrix:
Arguments:
d: a dissimilarity structure as produced by ‘dist’.
As the OP has now updated their question, what is need is
hclustfunc <- function(x) hclust(x, method="complete")
distfunc <- function(x) as.dist((1-cor(t(x)))/2)
d <- distfunc(mydata)
fit <- hclustfunc(d)
What you want is
hclustfunc <- function(x, method = "complete", dmeth = "euclidean") {
hclust(dist(x, method = dmeth), method = method)
}
and then
fit <- hclustfunc(mydata)
works as expected. Note you can now pass in the dissimilarity coefficient method as dmeth
and the clustering method.