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a few color and text-size changes to the javascript
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| % Generated by roxygen2 (4.0.1): do not edit by hand | ||
| \name{newJSON} | ||
| \alias{newJSON} | ||
| \title{Create the JSON object to read into the javascript visualization} | ||
| \usage{ | ||
| newJSON(phi = matrix(), theta = matrix(), alpha = numeric(), | ||
| beta = numeric(), doc.length = integer(), vocab = character(), | ||
| term.frequency = integer(), R = 30, print.progress = FALSE) | ||
| } | ||
| \arguments{ | ||
| \item{phi}{matrix, with each row containing the | ||
| distribution over terms for a topic, with as many rows as | ||
| there are topics in the model, and as many columns as | ||
| there are terms in the vocabulary.} | ||
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| \item{theta}{matrix, with each row containing the | ||
| probability distribution over topics for a document, with | ||
| as many rows as there are documents in the corpus, and as | ||
| many columns as there are topics in the model.} | ||
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| \item{alpha}{numeric vector with as many elements as | ||
| there are topics in the model, containing the parameters | ||
| of the Dirichlet prior distribution over topics for each | ||
| document.} | ||
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| \item{beta}{numeric vector with as many elements as there | ||
| are terms in the vocabulary, containing the parameters of | ||
| the Dirichlet prior distribution over terms for each | ||
| topic.} | ||
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| \item{doc.length}{integer vector containing the number of | ||
| tokens in each document in the corpus.} | ||
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| \item{vocab}{character vector of the terms in the | ||
| vocabulary (in the same order as the columns of | ||
| \code{phi} and the elements of \code{beta})} | ||
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| \item{term.frequency}{integer vector containing the | ||
| frequency of each term in the vocabulary} | ||
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| \item{R}{integer, the number of terms to display in the | ||
| barcharts of the interactive viz. Default is 30. | ||
| Recommended to be roughly between 10 and 50.} | ||
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| \item{print.progress}{logical; should the function print | ||
| progress to the screen during computation?} | ||
| } | ||
| \value{ | ||
| a JSON object in R that can be written to a file to feed | ||
| the interactive visualization | ||
| } | ||
| \description{ | ||
| This function creates the JSON object that feeds the | ||
| javascript visualization that is currently stored in | ||
| 'path-to-LDAvis/LDAvis/inst/htmljs/' | ||
| } | ||
| \details{ | ||
| The function first computes the topic frequencies (across | ||
| the whole corpus), and then it reorders the topics in | ||
| decreasing order of frequency. The main computation is to | ||
| loop through the topics and through 101 values of lambda | ||
| (0, 0.01, 0.02, .., 1) to compute the R most relevant terms | ||
| for each topic and value of lambda. If \code{print.progress | ||
| = TRUE} progress in this loop (which can take a minute or | ||
| two) will print to the screen. For more details, see | ||
| Sievert and Shirley, 2014, ACL Workshop. | ||
| } | ||
| \examples{ | ||
| # This example uses Newsgroup documents from | ||
| # http://qwone.com/~jason/20Newsgroups/ | ||
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| #data("Newsgroupdata", package = "LDAvis") | ||
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| # K = 50 topics | ||
| # W = 22524 terms in the vocabulary | ||
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| #json <- newJSON(...) | ||
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| # Open vis in a browser! | ||
| #serVis(json) | ||
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| # By default serVis uses a temporary directory | ||
| # Instead, we could write files to current working directory | ||
| #serVis(json, out.dir = '.', open.browser = FALSE) | ||
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| # If you have a GitHub account and want to quickly share with others! | ||
| serVis(json, as.gist = TRUE) | ||
| } | ||
| \seealso{ | ||
| \link{serVis} | ||
| } | ||
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