postProcess.DPMMclust: Post-processing Dirichlet Process Mixture Models results to...

In borishejblum/NPflow: Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

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

Description

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

Details

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

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:

Author(s)

Boris Hejblum

See Also

similarityMat summary.DPMMclust

borishejblum/NPflow documentation built on Feb. 2, 2024, 1:51 a.m.