Q: Adding weights to functions not accepting weights If I had a vector of weights for each observation
data(iris)
wghts <- abs(rnorm(nrow(iris)))

And I had a function that did not accept weights as an argument:
kmeans(iris[-5], centers=3)

Can I multiply each variable by the weights to get the desired output?
There is a version of kmeans that accepts weights:
library(FactoClass)
kmeansW(iris[-5], centers=3, weight=wghts)

But there are aspects of regular kmeans output that I still want, including betweenss. And just for learning purposes, Is it possible to just multiply all observations by the weight vector to get the equivalent output?
newdf <- iris[-5]
newdf[] <- lapply(newdf, function(v) v*wghts)
kmeans(newdf, centers=3)

Did I successfully "add" the weights by multiplying each variable by the weights or am I missing some aspect of weighted kmeans theory?
In other words, is 
kmeans(newdf, centers=3) structurally equivalent to kmeansW(iris[-5], centers=3, weight=wghts)?
