stm (version 1.1.3)

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 returned 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. See stm for options. Note that the default option here is different from the main function.

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.

Details

See the vignette for interepretation of each of these measures.

See Also

plot.searchK make.heldout

Examples

## Not run: 
# 
# 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)
# plot(kresult)
# 
# ## End(Not run)