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 dispersion.

Usage

searchK(
  documents,
  vocab,
  K,
  init.type = "Spectral",
  N = floor(0.1 * length(documents)),
  proportion = 0.5,
  heldout.seed = NULL,
  M = 10,
  cores = 1,
  ...
)

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.

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
cores: Number of CPUs to use for parallel computation
...: Other diagnostics parameters.

Details

See the vignette for interpretation of each of these measures. Each of these measures is also available in exported functions:

exclusivity: exclusivity
semantic coherence: semanticCoherence
heldout likelihood: make.heldout and eval.heldout
bound: calculated by stm accessible by max(model$convergence$bound)
lbound: a correction to the bound that makes the bounds directly comparable max(model$convergence$bound) + lfactorial(model$settings$dim$K)
residual dispersion: checkResiduals

Due to the need to calculate the heldout-likelihood N documents have proportion of the documents heldout at random. This means that even with the default spectral initialization the results can change from run to run. When the number of heldout documents is low or documents are very short, this also means that the results can be quite unstable. For example: the gadarian code demonstration below has heldout results based on only 34 documents and approximately 150 tokens total. Clearly this can lead to quite disparate results across runs. By contrast default settings for the poliblog5k dataset would yield a heldout sample of 500 documents with approximately 50000 tokens for the heldout sample. We should expect this to be substantially more stable.

Examples

Run this code


# \donttest{

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)

# }