TopicModelcontrol-class {topicmodels}

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 (after omitting the burnin 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