R: Summarizing Dirichlet Process Mixture Models

summary.DPMMclust {NPflow} | R Documentation

## Summarizing Dirichlet Process Mixture Models

### Description

Summary methods for `DPMMclust` objects.

### Usage

    ## S3 method for class 'DPMMclust'
    summary(
      object,
      burnin = 0,
      thin = 1,
      gs = NULL,
      lossFn = "F-measure",
      posterior_approx = FALSE,
      ...
    )

### Arguments

`object` | 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".
`posterior_approx` | logical flag whether a parametric approximation of the posterior should be computed. Default is `FALSE`
`...` | 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).

The number of retained sampled partitions is `m = (N - burnin)/thin`

### Value

a `list` containing the following elements:

- `nb_mcmcit`: an integer giving the value of `m`, the number of retained sampled partitions, i.e. `(N - burnin)/thin`

- `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

- `clust_distrib`: a character string passing along the `clust_distrib` argument

- `point_estim`: a `list` containing:

  - `c_est`: a vector of length `n`containing the point estimated clustering for each observations

  - `cost`: a vector of length `m` containing the cost of each sampled partition

  - `Fmeas`: if `lossFn` is `'F-measure'`, the `m x m` matrix of total F-measures for each pair of sampled partitions

  - `opt_ind`: the index of the point estimate partition among the `m` sampled

- `loss`: the loss for the point estimate. `NA` if `lossFn` is not `'Binder'`

- `param_posterior`: a list containing the parametric approximation of the posterior, suitable to be plugged in as prior for a new MCMC algorithm run

- `mcmc_partitions`: a list containing the `m` sampled partitions

- `alpha`: a vector of length `m` with the values of the `alpha` DP parameter

- `index_estim`: the index of the point estimate partition among the `m` sampled

- `hyperG0`: a list passing along the prior, i.e. the `hyperG0` argument

- `logposterior_list`: a list of length `m` containing the logposterior and its decomposition, for each sampled partition

- `U_SS_list`: a list of length `m` containing the containing the lists of sufficient statistics for all the mixture components, for each sampled partition

- `data`: a `d x n` matrix containing the clustered data

### Author(s)

Boris Hejblum

### See Also

`similarityMat` `similarityMatC`

[Package NPflow version 0.13.3 Index]