LDA {topicmodels} | R Documentation |
Estimate a LDA model using the VEM algorithm or Gibbs Sampling.
LDA(x, k, method = c("VEM", "Gibbs"), control = NULL, model = NULL, ...)
x |
Object of class "DocumentTermMatrix" |
k |
Integer; number of topics |
method |
The method to be used for fitting; currently
method = "VEM" or method= "Gibbs" are
supported. |
control |
A named list of the control parameters for estimation
or an object of class "LDAcontrol" . |
model |
Object of class "LDA" for initialization. |
... |
Optional arguments. Currently not used. |
The C code for LDA from David M. Blei is used to estimate and fit a latent dirichlet allocation model with the VEM algorithm.
For Gibbs Sampling the C++ code from Xuan-Hieu Phan is used.
LDA()
returns an object of class
"LDA"
.
Bettina Gruen
Blei D.M., Ng A.Y., Jordan M.I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.
Phan X.H., Nguyen L.M., Horguchi S. (2008). Learning to Classify Short and Sparse Text & Web with Hidden Topics from Large-scale Data Collections. In "Proceedings of the 17th International World Wide Web Conference (WWW 2008)," pp.91-100. Beijing, China.
data("AssociatedPress", package = "topicmodels") lda <- LDA(AssociatedPress[1:20,], control = list(alpha = 0.1), k = 2) lda_inf <- LDA(AssociatedPress[21:30,], model = lda, control = list(em = list(iter.max = -1L)))