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

### 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`

[Package NPflow version 0.13.3 Index]