R: Different classes for controlling the estimation of topic...

TopicModelcontrol-class {topicmodels} | R Documentation

## Different classes for controlling the estimation of topic models

### Description

Classes to control the estimation of topic models which are inheriting from the virtual base class `"TopicModelcontrol"`.

### Objects from the Class

Objects can be created from named lists.

### Slots

Class `"TopicModelcontrol"` contains

`seed`:

  Object of class `"integer"`; used to set the seed in the external code for VEM estimation and to call `set.seed` for Gibbs sampling. For Gibbs sampling it can also be set to `NA` (default) to avoid changing the seed of the random number generator in the model fitting call.

`verbose`:

  Object of class `"integer"`. If a positive integer, then the progress is reported every `verbose` iterations. If 0 (default), no output is generated during model fitting.

`save`:

  Object of class `"integer"`. If a positive integer the estimated model is saved all `verbose` iterations. If 0 (default), no output is generated during model fitting.

`prefix`:

  Object of class `"character"`; path indicating where to save the intermediate results.

`nstart`:

  Object of class `"integer"`. Number of repeated random starts.

`best`:

  Object of class `"logical"`; if `TRUE` only the model with the maximum (posterior) likelihood is returned, by default equals `TRUE`.

`keep`:

  Object of class `"integer"`; if a positive integer, the log-likelihood is saved every `keep` iterations.

`estimate.beta`:

  Object of class `"logical"`; controls if beta, the term distribution of the topics, is fixed, by default equals `TRUE`.

Class `"VEMcontrol"` contains

`var`:

  Object of class `"OPTcontrol"`; controls the variational inference for a single document, by default `iter.max` equals 500 and `tol` 10^-6.

`em`:

  Object of class `"OPTcontrol"`; controls the variational EM algorithm, by default `iter.max` equals 1000 and `tol` 10^-4.

`initialize`:

  Object of class `"character"`; one of `"random"`, `"seeded"` and `"model"`, by default equals `"random"`.

Class `"LDAcontrol"` extends class `"TopicModelcontrol"` and has the additional slots

`alpha`:

  Object of class `"numeric"`; initial value for alpha.

Class `"LDA_VEMcontrol"` extends classes `"LDAcontrol"` and `"VEMcontrol"` and has the additional slots

`estimate.alpha`:

  Object of class `"logical"`; indicates if the parameter alpha is fixed a-priori or estimated, by default equals `TRUE`.

Class `"LDA_Gibbscontrol"` extends classes `"LDAcontrol"` and has the additional slots

`delta`:

  Object of class `"numeric"`; initial value for delta, by default equals 0.1.

`iter`:

  Object of class `"integer"`; number of Gibbs iterations, by default equals 2000.

`thin`:

  Object of class `"integer"`; number of omitted in-between Gibbs iterations, by default equals `iter`.

`burnin`:

  Object of class `"integer"`; number of omitted Gibbs iterations at beginning, by default equals 0.

`initialize`:

  Object of class `"character"`; one of `"random"`, `"beta"` and `"z"`, by default equals `"random"`.

Class `"CTM_VEMcontrol"` extends classes `"TopicModelcontrol"` and `"VEMcontrol"` and has the additional slots

`cg`:

  Object of class `"OPTcontrol"`; controls the conjugate gradient iterations in fitting the variational mean and variance per document, by default `iter.max` equals 500 and `tol` 10^-5.

Class `"OPTcontrol"` contains

`iter.max`:

  Object of class `"integer"`; maximum number of iterations.

`tol`:

  Object of class `"numeric"`; tolerance for convergence check.

### Author(s)

Bettina Gruen

[Package topicmodels version 0.2-11 Index]