Experimental feature: Fit keyATM base with Collapsed Variational Bayes
keyATMvb(
docs,
model,
no_keyword_topics,
keywords = list(),
model_settings = list(),
vb_options = list(),
priors = list(),
options = list(),
keep = list()
)A keyATM_output object
texts read via keyATM_read()
keyATM model: base, covariates, and dynamic
the number of regular topics
a list of keywords
a list of model specific settings (details are in the online documentation)
a list of settings for Variational Bayes
convtol: the default is 1e-4
init: mcmc (default) or random
a list of priors of parameters
a list of options same as keyATM(). Options are used when initialization method is mcmc.
a vector of the names of elements you want to keep in output