"summary"
prints topic words with their corpus frequency. "labels"
is for easy printing of tables of indicative words for each topic. "perspectives"
depicts differences between two topics, content covariates or combinations. "hist"
creates a histogram of the expected distribution of topic proportions across the documents.
"plot"(x, type = c("summary", "labels", "perspectives", "hist"), n = NULL, topics = NULL, labeltype=c("prob", "frex", "lift", "score"), frexw = 0.5, main = NULL, xlim = NULL, ylim = NULL, xlab = NULL, family = "", width = 80, covarlevels = NULL, plabels = NULL, text.cex=1, custom.labels=NULL, topic.names=NULL, ...)
summary
, labels
and perspectives
respectively.
"prob", "frex", "lift", "score"
is used for choosing the most important words. See
labelTopics
for more detail. Passing an argument to
custom.labels
will overide this.
plabels
instead.
"labels"
type
. summary
produces a plot which displays the topics ordered by their expected frequency across the corpus. labels
plots the top words selected according to the chosen criteria for each selected topics. perspectives
plots two topic or topic-covariate combinations. Words are sized proportional to their use within the plotted topic-covariate combinations and oriented along the X-axis based on how much they favor one of the two configurations. If the words cluster on top of each other the user can either set the plot size to be larger or shrink the total number of words on the plot. The vertical configuration of the words is random and thus can be rerun to produce different results each time. hist
plots a histogram of the MAP estimates of the document-topic loadings across all documents. The median is also denoted by a dashed red line.
plotQuote
, plot.topicCorr
#Examples with the Gadarian Data
plot(gadarianFit)
plot(gadarianFit,type="labels")
plot(gadarianFit, type="perspectives", topics=c(1,2))
plot(gadarianFit,type="hist")
Run the code above in your browser using DataLab