R: Post-processing Dirichlet Process Mixture Models results to...

postProcess.DPMMclust {NPflow}

R Documentation

Post-processing Dirichlet Process Mixture Models results to get a mixture distribution of the posterior locations

Post-processing Dirichlet Process Mixture Models results to get a mixture distribution of the posterior locations

postProcess.DPMMclust(
  x,
  burnin = 0,
  thin = 1,
  gs = NULL,
  lossFn = "F-measure",
  K = 10,
  ...
)

`x`	a `DPMMclust` object.
`burnin`	integer giving the number of MCMC iterations to burn (defaults is half)
`thin`	integer giving the spacing at which MCMC iterations are kept. Default is `1`, i.e. no thining.
`gs`	optional vector of length `n` containing the gold standard partition of the `n` observations to compare to the point estimate.
`lossFn`	character string specifying the loss function to be used. Either "F-measure" or "Binder" (see Details). Default is "F-measure".
`K`	integer giving the number of mixture components. Default is `10`.
`...`	further arguments passed to or from other methods

The cost of a point estimate partition is calculated using either a pairwise coincidence loss function (Binder), or 1-Fmeasure (F-measure).

a list:

Boris Hejblum