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

Usage

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

Arguments

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

Value

a list:

burnin:

an integer passing along the burnin argument

thin:

an integer passing along the thin argument

lossFn:

a character string passing along the lossFn argument

point_estim:

loss:

index_estim:

Details

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

See also

similarityMat summary.DPMMclust

Author

Boris Hejblum