I think this should work
rgmm <- function(n, gmm) {
M <- sample(1:4, n, replace = TRUE, prob= gmm$weight)
mean <- gmm[M, ]$mean
sd <- gmm[M, ]$sd
return(gmm[M,]$sd*rnorm(n) + gmm[M,]$mean)
}
hist(rgmm(10000, gmm), breaks = 500)
On Dec 19, 4:14 pm, "Bill McNeill (UW)" <bill...@u.washington.edu>
wrote:
> I am trying to generate a set of data points from a Gaussian mixture
> model. My mixture model is represented by a data frame that looks
> like this:
>
> > gmm
>
> weight mean sd
> 1 0.3 0 1.0
> 2 0.2 -2 0.5
> 3 0.4 4 0.7
> 4 0.1 5 0.3
>
> I have written the following function that generates the appropriate data:
>
> gmm_data <- function(n, gmm) {
> c(rnorm(n*gmm[1,]$weight, gmm[1,]$mean, gmm[1,]$sd),
> rnorm(n*gmm[2,]$weight, gmm[2,]$mean, gmm[2,]$sd),
> rnorm(n*gmm[3,]$weight, gmm[3,]$mean, gmm[3,]$sd),
> rnorm(n*gmm[4,]$weight, gmm[4,]$mean, gmm[4,]$sd))
>
> }
>
> However, the fact that my mixture has four components is hard-coded
> into this function. A better implementation of gmm_data() would
> generate data points for an arbitrary number of mixture components
> (i.e. an arbitrary number of rows in the data frame).
>
> How do I do this? I'm sure it's simple, but I can't figure it out.
>
> Thanks.
> --
> Bill McNeillhttp://staff.washington.edu/billmcn/index.shtml
>
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> and provide commented, minimal, self-contained, reproducible code.
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