stm (version 1.0.0)

searchK: Computes diagnostic values for models with different values of K (number of topics).

Description

With user-specified initialization, this function runs selectModel for different user-specified topic numbers and computes diagnostic properties for the resturned model. These include exclusivity, semantic coherence, heldout likelihood, bound, lbound, and residual.

Usage

searchK(documents, vocab, K, init.type = "Spectral", 
                    N=floor(.1*length(documents)), proportion=.5, heldout.seed=NULL,
                    M=10,...)

Arguments

documents

The documents to be used for the stm model

vocab

The vocabulary to be used for the stmmodel

K

A vector of different topic numbers

init.type

The method of initialization. Must be either Latent Dirichlet Allocation (LDA), Dirichlet Multinomial Regression Topic Model (DMR), a random initialization, `spectral', or a previous STM object. If an initialization other than spectral is

N

Number of docs to be partially held out

proportion

Proportion of docs to be held out.

heldout.seed

If desired, a seed to use when holding out documents for later heldout likelihood computation

M

M value for exclusivity computation

...

Other diagnostics parameters.

Value

• exclus
  Exclusivity of each model.
• semcoh
  Semantic coherence of each model.
• heldout
  Heldout likelihood for each model.
• residual
  Residual for each model.
• bound
  Bound for each model.
• lbound
  lbound for each model.
• em.its
  Total number of EM iterations used in fiting the model.

Examples

K<-c(5,10,15) 
temp<-textProcessor(documents=gadarian$open.ended.response,metadata=gadarian)
out <- prepDocuments(temp$documents, temp$vocab, temp$meta)
documents <- out$documents
vocab <- out$vocab
meta <- out$meta
set.seed(02138)
K<-c(5,10,15) 
kresult <- searchK(documents, vocab, K, prevalence=~treatment + s(pid_rep), data=meta)