Calculate a matrix whose rows represent P(topic_i|tokens)

Description

This function takes a phi matrix (P(token|topic)) and a theta matrix (P(topic|document)) and returns the phi prime matrix (P(topic|token)). Phi prime can be used for classifying new documents and for alternative topic labels.

Usage

CalcGamma(phi, theta, p_docs = NULL, correct = TRUE)

Arguments

Value

Returns a matrix whose rows correspond to topics and whose columns correspond to tokens. The i,j entry corresponds to P(topic_i|token_j)

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_topic_model) 

# Make a gamma matrix, P(topic|words)
gamma <- CalcGamma(phi = nih_sample_topic_model$phi, 
                   theta = nih_sample_topic_model$theta)

Calculate Hellinger Distance

Description

Calculates the Hellinger distances or the rows or columns of a numeric matrix or for two numeric vectors.

Usage

CalcHellingerDist(x, y = NULL, by_rows = TRUE)

Arguments

Value

If x is a matrix, this returns an square and symmetric matrix. The i,j entries correspond to the Hellinger Distance between the rows of x (or the columns of x if by_rows = FALSE). If x and y are vectors, this returns a numeric scalar whose value is the Hellinger Distance between x and y.

Examples

x <- rchisq(n = 100, df = 8)
y <- x^2
CalcHellingerDist(x = x, y = y)

mymat <- rbind(x, y)
CalcHellingerDist(x = mymat)

Calculate Jensen-Shannon Divergence

Description

This function calculates the Jensen Shannon Divergence for the rows or columns of a numeric matrix or for two numeric vectors.

Usage

CalcJSDivergence(x, y = NULL, by_rows = TRUE)

Arguments

Value

If x is a matrix, this returns an square and symmetric matrix. The i,j entries correspond to the Hellinger Distance between the rows of x (or the columns of x if by_rows = FALSE). If x and y are vectors, this returns a numeric scalar whose value is the Hellinger Distance between x and y.

Examples

x <- rchisq(n = 100, df = 8)
y <- x^2
CalcJSDivergence(x = x, y = y)

mymat <- rbind(x, y)
CalcJSDivergence(x = mymat)

Calculate the log likelihood of a document term matrix given a topic model

Description

This function takes a DTM, phi matrix (P(word|topic)), and a theta matrix (P(topic|document)) and returns a single value for the likelihood of the data given the model.

Usage

CalcLikelihood(dtm, phi, theta, ...)

Arguments

Value

Returns an object of class numeric corresponding to the log likelihood.

Note

This function performs parallel computation if dtm has more than 3,000 rows. The default is to use all available cores according to detectCores. However, this can be modified by passing the cpus argument when calling this function.

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_dtm) 
data(nih_sample_topic_model)

# Get the likelihood of the data given the fitted model parameters
ll <- CalcLikelihood(dtm = nih_sample_dtm, 
                     phi = nih_sample_topic_model$phi, 
                     theta = nih_sample_topic_model$theta)

ll

Probabilistic coherence of topics

Description

Calculates the probabilistic coherence of a topic or topics. This approximates semantic coherence or human understandability of a topic.

Usage

CalcProbCoherence(phi, dtm, M = 5)

Arguments

Value

Returns an object of class numeric corresponding to the probabilistic coherence of the input topic(s).

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_topic_model)
data(nih_sample_dtm) 

CalcProbCoherence(phi = nih_sample_topic_model$phi, dtm = nih_sample_dtm, M = 5)

Calculate the R-squared of a topic model.

Description

Function to calculate R-squared for a topic model. This uses a geometric interpretation of R-squared as the proportion of total distance each document is from the center of all the documents that is explained by the model.

Usage

CalcTopicModelR2(dtm, phi, theta, ...)

Arguments

Value

Returns an object of class numeric representing the proportion of variability in the data that is explained by the topic model.

Note

This function performs parallel computation if dtm has more than 3,000 rows. The default is to use all available cores according to detectCores. However, this can be modified by passing the cpus argument when calling this function.

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_dtm) 
data(nih_sample_topic_model)

# Get the R-squared of the model
r2 <- CalcTopicModelR2(dtm = nih_sample_dtm, 
                     phi = nih_sample_topic_model$phi, 
                     theta = nih_sample_topic_model$theta)


r2

Represent a document clustering as a topic model

Description

Represents a document clustering as a topic model of two matrices. phi: P(term | cluster) theta: P(cluster | document)

Usage

Cluster2TopicModel(dtm, clustering, ...)

Arguments

Value

Returns a list with two elements, phi and theta. 'phi' is a matrix whose j-th row represents P(terms | cluster_j). 'theta' is a matrix whose j-th row represents P(clusters | document_j). Each row of theta should only have one non-zero element.

Examples

## Not run: 
# Load pre-formatted data for use
data(nih_sample_dtm)
data(nih_sample) 

result <- Cluster2TopicModel(dtm = nih_sample_dtm, 
                             clustering = nih_sample$IC_NAME)

## End(Not run)

Convert a character vector to a document term matrix.

Description

This is the main document term matrix creating function for textmineR. In most cases, all you need to do is import documents as a character vector in R and then run this function to get a document term matrix that is compatible with the rest of textmineR's functionality and many other libraries. CreateDtm is built on top of the excellent text2vec library.

Usage

CreateDtm(
  doc_vec,
  doc_names = names(doc_vec),
  ngram_window = c(1, 1),
  stopword_vec = c(stopwords::stopwords("en"), stopwords::stopwords(source = "smart")),
  lower = TRUE,
  remove_punctuation = TRUE,
  remove_numbers = TRUE,
  stem_lemma_function = NULL,
  verbose = FALSE,
  ...
)

Arguments

Value

A document term matrix of class dgCMatrix. The rows index documents. The columns index terms. The i, j entries represent the count of term j appearing in document i.

Note

The following transformations are applied to stopword_vec as well as doc_vec: lower, remove_punctuation, remove_numbers

See stopwords for details on the default to the stopword_vec argument.

Examples

## Not run: 
data(nih_sample)

# DTM of unigrams and bigrams
dtm <- CreateDtm(doc_vec = nih_sample$ABSTRACT_TEXT,
                 doc_names = nih_sample$APPLICATION_ID, 
                 ngram_window = c(1, 2))

# DTM of unigrams with Porter's stemmer applied
dtm <- CreateDtm(doc_vec = nih_sample$ABSTRACT_TEXT,
                 doc_names = nih_sample$APPLICATION_ID,
                 stem_lemma_function = function(x) SnowballC::wordStem(x, "porter"))

## End(Not run)

Convert a character vector to a term co-occurrence matrix.

Description

This is the main term co-occurrence matrix creating function for textmineR. In most cases, all you need to do is import documents as a character vector in R and then run this function to get a term co-occurrence matrix that is compatible with the rest of textmineR's functionality and many other libraries. CreateTcm is built on top of the excellent text2vec library.

Usage

CreateTcm(
  doc_vec,
  skipgram_window = Inf,
  ngram_window = c(1, 1),
  stopword_vec = c(stopwords::stopwords("en"), stopwords::stopwords(source = "smart")),
  lower = TRUE,
  remove_punctuation = TRUE,
  remove_numbers = TRUE,
  stem_lemma_function = NULL,
  verbose = FALSE,
  ...
)

Arguments

Details

Setting skipgram_window counts the number of times that term j appears within skipgram_window places of term i. Inf and 0 create somewhat special TCMs. Setting skipgram_window to Inf counts the number of documents in which term j and term i occur together. Setting skipgram_window to 0 counts the number of terms shared by document j and document i. A TCM where skipgram_window is 0 is the only TCM that will be symmetric.

Value

A document term matrix of class dgCMatrix. The rows index documents. The columns index terms. The i, j entries represent the count of term j appearing in document i.

Note

The following transformations are applied to stopword_vec as well as doc_vec: lower, remove_punctuation, remove_numbers

See stopwords for details on the default to the stopword_vec argument.

Examples

## Not run: 
data(nih_sample)

# TCM of unigrams and bigrams
tcm <- CreateTcm(doc_vec = nih_sample$ABSTRACT_TEXT,
                 skipgram_window = Inf, 
                 ngram_window = c(1, 2))

# TCM of unigrams and a skip=gram window of 3, applying Porter's word stemmer
tcm <- CreateTcm(doc_vec = nih_sample$ABSTRACT_TEXT,
                 skipgram_window = 3,
                 stem_lemma_function = function(x) SnowballC::wordStem(x, "porter"))

## End(Not run)

Convert a DTM to a Character Vector of documents

Description

This function takes a sparse matrix (DTM) as input and returns a character vector whose length is equal to the number of rows of the input DTM.

Usage

Dtm2Docs(dtm, ...)

Arguments

Value

Returns a character vector. Each entry of this vector corresponds to the rows of dtm.

Note

This function performs parallel computation if dtm has more than 3,000 rows. The default is to use all available cores according to detectCores. However, this can be modified by passing the cpus argument when calling this function.

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample)
data(nih_sample_dtm) 

# see the original documents
nih_sample$ABSTRACT_TEXT[ 1:3 ]

# see the new documents re-structured from the DTM
new_docs <- Dtm2Docs(dtm = nih_sample_dtm)

new_docs[ 1:3 ]

Turn a document term matrix into a list for LDA Gibbs sampling

Description

Represents a document term matrix as a list.

Usage

Dtm2Lexicon(dtm, ...)

Arguments

Value

Returns a list. Each element of the list represents a row of the input matrix. Each list element contains a numeric vector with as many entries as tokens in the original document. The entries are the column index for that token, minus 1.

Examples

## Not run: 
# Load pre-formatted data for use
data(nih_sample_dtm)

result <- Dtm2Lexicon(dtm = nih_sample_dtm, 
                      cpus = 2)

## End(Not run)

Turn a document term matrix into a term co-occurrence matrix

Description

Turn a document term matrix, whose rows index documents and whose columns index terms, into a term co-occurrence matrix. A term co-occurrence matrix's rows and columns both index terms. See details, below.

Usage

Dtm2Tcm(dtm)

Arguments

Value

Returns a square dgCMatrix whose rows and columns both index terms. The i, j entries of this matrix represent the count of term j across documents containing term i. Note that, while square, this matrix is not symmetric.

Examples

data(nih_sample_dtm)

tcm <- Dtm2Tcm(nih_sample_dtm)

Fit a Correlated Topic Model

Description

A wrapper for the CTM function based on Blei's original code that returns a nicely-formatted topic model.

Usage

FitCtmModel(
  dtm,
  k,
  calc_coherence = TRUE,
  calc_r2 = FALSE,
  return_all = TRUE,
  ...
)

Arguments

Value

Returns a list with a minimum of two objects, phi and theta. The rows of phi index topics and the columns index tokens. The rows of theta index documents and the columns index topics.

Note

When passing additional arguments to CTM, you must unlist the elements in the control argument and pass them one by one. See examples for how to dot this correctly.

Examples

# Load a pre-formatted dtm 
data(nih_sample_dtm) 

# Fit a CTM model on a sample of documents
model <- FitCtmModel(dtm = nih_sample_dtm[ sample(1:nrow(nih_sample_dtm) , 10) , ], 
                     k = 3, return_all = FALSE)
                     
# the correct way to pass control arguments to CTM
## Not run: 
topics_CTM <- FitCtmModel(
    dtm = nih_sample_dtm[ sample(1:nrow(nih_sample_dtm) , 10) , ],
    k = 10,
    calc_coherence = TRUE,
    calc_r2 = TRUE,
    return_all = TRUE,
    estimate.beta = TRUE,
    verbose = 0,
    prefix = tempfile(),
    save = 0,
    keep = 0,
    seed = as.integer(Sys.time()),
    nstart = 1L,
    best = TRUE,
    var = list(iter.max = 500, tol = 10^-6),
    em = list(iter.max = 1000, tol = 10^-4),
    initialize = "random",
    cg = list(iter.max = 500, tol = 10^-5)
)

## End(Not run)

Fit a Latent Dirichlet Allocation topic model

Description

Fit a Latent Dirichlet Allocation topic model using collapsed Gibbs sampling.

Usage

FitLdaModel(
  dtm,
  k,
  iterations = NULL,
  burnin = -1,
  alpha = 0.1,
  beta = 0.05,
  optimize_alpha = FALSE,
  calc_likelihood = FALSE,
  calc_coherence = TRUE,
  calc_r2 = FALSE,
  ...
)

Arguments

Details

EXPLAIN IMPLEMENTATION DETAILS

Value

Returns an S3 object of class c("LDA", "TopicModel"). DESCRIBE MORE

Examples

# load some data
data(nih_sample_dtm)

# fit a model 
set.seed(12345)
m <- FitLdaModel(dtm = nih_sample_dtm[1:20,], k = 5,
                 iterations = 200, burnin = 175)

str(m)

# predict on held-out documents using gibbs sampling "fold in"
p1 <- predict(m, nih_sample_dtm[21:100,], method = "gibbs",
              iterations = 200, burnin = 175)

# predict on held-out documents using the dot product method
p2 <- predict(m, nih_sample_dtm[21:100,], method = "dot")

# compare the methods
barplot(rbind(p1[1,],p2[1,]), beside = TRUE, col = c("red", "blue")) 

Fit a topic model using Latent Semantic Analysis

Description

A wrapper for RSpectra::svds that returns a nicely-formatted latent semantic analysis topic model.

Usage

FitLsaModel(dtm, k, calc_coherence = TRUE, return_all = FALSE, ...)

Arguments

Details

Latent semantic analysis, LSA, uses single value decomposition to factor the document term matrix. In many LSA applications, TF-IDF weights are applied to the DTM before model fitting. However, this is not strictly necessary.

Value

Returns a list with a minimum of three objects: phi, theta, and sv. The rows of phi index topics and the columns index tokens. The rows of theta index documents and the columns index topics. sv is a vector of singular values.

Examples

# Load a pre-formatted dtm 
data(nih_sample_dtm) 

# Convert raw word counts to TF-IDF frequency weights
idf <- log(nrow(nih_sample_dtm) / Matrix::colSums(nih_sample_dtm > 0))

dtm_tfidf <- Matrix::t(nih_sample_dtm) * idf

dtm_tfidf <- Matrix::t(dtm_tfidf)

# Fit an LSA model
model <- FitLsaModel(dtm = dtm_tfidf, k = 5)

str(model)

Get cluster labels using a "more probable" method of terms

Description

Function extracts probable terms from a set of documents. Probable here implies more probable than in a corpus overall.

Usage

GetProbableTerms(docnames, dtm, p_terms = NULL)

Arguments

Value

Returns a numeric vector of the format p_terms. The entries of the vectors correspond to the difference in the probability of drawing a term from the set of documents given by docnames and the probability of drawing that term from the corpus overall (p_terms).

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_topic_model)
data(nih_sample_dtm) 

# documents with a topic proportion of .25 or higher for topic 2
mydocs <- rownames(nih_sample_topic_model$theta)[ nih_sample_topic_model$theta[ , 2 ] >= 0.25 ] 

term_probs <- Matrix::colSums(nih_sample_dtm) / sum(Matrix::colSums(nih_sample_dtm))

GetProbableTerms(docnames = mydocs, dtm = nih_sample_dtm, p_terms = term_probs)

Get Top Terms for each topic from a topic model

Description

Takes topics by terms matrix and returns top M terms for each topic

Usage

GetTopTerms(phi, M, return_matrix = TRUE)

Arguments

Value

If return_matrix = TRUE (the default) then a matrix. Otherwise, returns a data.frame or tibble whose columns correspond to a topic and whose m-th row correspond to the m-th top term from the input phi.

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_topic_model) 

top_terms <- GetTopTerms(phi = nih_sample_topic_model$phi, M = 5)

str(top_terms)

Internal helper functions for textmineR

Description

These functions are internal helper functions for textmineR. They are not designed to be called by users. Each of the functions here are C++ functions. There are corresponding R functions that call these that add additional functionality.

Get some topic labels using a "more probable" method of terms

Description

Function calls GetProbableTerms with some rules to get topic labels. This function is in "super-ultra-mega alpha"; use at your own risk/discretion.

Usage

LabelTopics(assignments, dtm, M = 2)

Arguments

Value

Returns a matrix whose rows correspond to topics and whose j-th column corresponds to the j-th "best" label assignment.

Examples

# make a dtm with unigrams and bigrams
data(nih_sample_topic_model)

m <- nih_sample_topic_model

assignments <- t(apply(m$theta, 1, function(x){
  x[ x < 0.05 ] <- 0
  x / sum(x)
}))

assignments[is.na(assignments)] <- 0

labels <- LabelTopics(assignments = assignments, dtm = m$data, M = 2)

Summarize topics in a topic model

Description

Create a data frame summarizing the contents of each topic in a model

Usage

SummarizeTopics(model)

Arguments

Details

'prevalence' is normalized to sum to 100. If your 'theta' matrix has negative values (as may be the case with an LSA model), a constant is added so that the least prevalent topic has a prevalence of 0.

'coherence' is calculated using CalcProbCoherence.

'label' is assigned using the top label from LabelTopics. This requires an "assignment" matrix. This matrix is like a "theta" matrix except that it is binary. A topic is "in" a document or it is not. The assignment is made by comparing each value of theta to the minimum of the largest value for each row of theta (each document). This ensures that each document has at least one topic assigned to it.

Value

An object of class data.frame or tibble with 6 columns: 'topic' is the name of the topic, 'prevalence' is the rough prevalence of the topic in all documents across the corpus, 'coherence' is the probabilistic coherence of the topic, 'top_terms_phi' are the top 5 terms for each topic according to P(word|topic), 'top_terms_gamma' are the top 5 terms for each topic according to P(topic|word).

Examples

## Not run: 
SummarizeTopics(nih_sample_topic_model)

## End(Not run)

Get term frequencies and document frequencies from a document term matrix.

Description

This function takes a document term matrix as input and returns a data frame with columns for term frequency, document frequency, and inverse-document frequency

Usage

TermDocFreq(dtm)

Arguments

Value

Returns a data.frame or tibble with 4 columns. The first column, term is a vector of token labels. The second column, term_freq is the count of times term appears in the entire corpus. The third column doc_freq is the count of the number of documents in which term appears. The fourth column, idf is the log-weighted inverse document frequency of term.

Examples

# Load a pre-formatted dtm and topic model
data(nih_sample_dtm)
data(nih_sample_topic_model) 

# Get the term frequencies 
term_freq_mat <- TermDocFreq(nih_sample_dtm)

str(term_freq_mat)

An OS-independent parallel version of lapply

Description

This function takes a vector or list and a function and applies in parallel.

Usage

TmParallelApply(
  X,
  FUN,
  cpus = parallel::detectCores(),
  export = NULL,
  libraries = NULL,
  envir = parent.frame()
)

Arguments

Details

This function is used to parallelize executions in textmineR. It is necessary because of differing capabilities between Windows and Unix. Unix systems use mclapply. Windows systems use parLapply.

Value

This function returns a list of length length(X).

Examples

## Not run: 
x <- 1:10000
f <- function(y) y * y + 12
result <- TmParallelApply(x, f)

## End(Not run)

Abstracts and metadata from NIH research grants awarded in 2014

Description

This dataset holds information on research grants awarded by the National Institutes of Health (NIH) in 2014. The data set was downloaded in approximately January of 2015 from https://exporter.nih.gov/ExPORTER_Catalog.aspx. It includes both 'projects' and 'abstracts' files.

Usage

  data("nih_sample")
  data("nih_sample_dtm")
  data("nih_sample_topic_model")
  

Format

A data.frame of 100 randomly-sampled grants' abstracts and metadata. A dgCMatrix representing the document term matrix of abstracts from 100 randomly-sampled grants. A list containing a topic model of these 100 sampled grants.

Source

National Institutes of Health ExPORTER https://exporter.nih.gov/ExPORTER_Catalog.aspx

Posterior methods for topic models

Description

posterior will draw from the posterior distribution of a topic model

Usage

posterior(object, ...)

Arguments

Draw from the posterior of an LDA topic model

Description

This function takes an object of class lda_topic_model and draws samples from the posterior of either phi or theta. This is useful for quantifying uncertainty around parametersof the final model.

Usage

## S3 method for class 'lda_topic_model'
posterior(object, which = "theta", num_samples = 100, ...)

Arguments

Value

Returns a data frame where each row is a single sample from the posterior. Each column is the distribution over a single parameter. The variable var is a facet for subsetting by document (for theta) or topic (for phi).

References

Heinrich, G. (2005) Parameter estimation for text analysis. Technical report. http://www.arbylon.net/publications/text-est.pdf

Examples

## Not run: 
a <- posterior(object = nih_sample_topic_model, which = "theta", num_samples = 20)

plot(density(a$t1[a$var == "8693991"]))

b <- posterior(object = nih_sample_topic_model, which = "phi", num_samples = 20)

plot(denisty(b$research[b$var == "t_5"]))

## End(Not run)

Predict method for Correlated topic models (CTM)

Description

Obtains predictions of topics for new documents from a fitted CTM model

Usage

## S3 method for class 'ctm_topic_model'
predict(object, newdata, ...)

Arguments

Value

a "theta" matrix with one row per document and one column per topic

Note

Predictions for this method are performed using the "dot" method as described in the textmineR vignette "c_topic_modeling".

Examples

# Load a pre-formatted dtm 
## Not run: 
data(nih_sample_dtm) 

model <- FitCtmModel(dtm = nih_sample_dtm[1:20,], k = 3,
                     calc_coherence = FALSE, calc_r2 = FALSE)

# Get predictions on the next 50 documents
pred <- predict(model, nih_sample_dtm[21:100,])

## End(Not run)

Get predictions from a Latent Dirichlet Allocation model

Description

Obtains predictions of topics for new documents from a fitted LDA model

Usage

## S3 method for class 'lda_topic_model'
predict(
  object,
  newdata,
  method = c("gibbs", "dot"),
  iterations = NULL,
  burnin = -1,
  ...
)

Arguments

Value

a "theta" matrix with one row per document and one column per topic

Examples

## Not run: 
# load some data
data(nih_sample_dtm)

# fit a model 
set.seed(12345)

m <- FitLdaModel(dtm = nih_sample_dtm[1:20,], k = 5,
                 iterations = 200, burnin = 175)

str(m)

# predict on held-out documents using gibbs sampling "fold in"
p1 <- predict(m, nih_sample_dtm[21:100,], method = "gibbs",
              iterations = 200, burnin = 175)

# predict on held-out documents using the dot product method
p2 <- predict(m, nih_sample_dtm[21:100,], method = "dot")

# compare the methods
barplot(rbind(p1[1,],p2[1,]), beside = TRUE, col = c("red", "blue")) 

## End(Not run)

Predict method for LSA topic models

Description

Obtains predictions of topics for new documents from a fitted LSA model

Usage

## S3 method for class 'lsa_topic_model'
predict(object, newdata, ...)

Arguments

Value

a "theta" matrix with one row per document and one column per topic

Examples

# Load a pre-formatted dtm 
data(nih_sample_dtm) 

# Convert raw word counts to TF-IDF frequency weights
idf <- log(nrow(nih_sample_dtm) / Matrix::colSums(nih_sample_dtm > 0))

dtm_tfidf <- Matrix::t(nih_sample_dtm) * idf

dtm_tfidf <- Matrix::t(dtm_tfidf)

# Fit an LSA model on the first 50 documents
model <- FitLsaModel(dtm = dtm_tfidf[1:50,], k = 5)

# Get predictions on the next 50 documents
pred <- predict(model, dtm_tfidf[51:100,])

textmineR

Description

Functions for Text Mining and Topic Modeling

Details

An aid for text mining in R, with a syntax that should be familiar to experienced R users. Provides a wrapper for several topic models that take similarly-formatted input and give similarly-formatted output. Has additional functionality for analyzing and diagnostics for topic models.

Deprecated functions in package textmineR.

Description

The functions listed below are deprecated and will be defunct in the near future. When possible, alternative functions with similar functionality are also mentioned.

Details

Below is a list of deprecated functions: RecursiveRbind Vec2Dtm JSD HellDist GetPhiPrime FormatRawLdaOutput Files2Vec DepluralizeDtm CorrectS CalcPhiPrime

Update methods for topic models

Description

update will update a previously-trained topic model based on new data. Useful for updates or transfer learning.

Usage

update(object, ...)

Arguments

Update a Latent Dirichlet Allocation topic model with new data

Description

Update an LDA model with new data using collapsed Gibbs sampling.

Usage

## S3 method for class 'lda_topic_model'
update(
  object,
  dtm,
  additional_k = 0,
  iterations = NULL,
  burnin = -1,
  new_alpha = NULL,
  new_beta = NULL,
  optimize_alpha = FALSE,
  calc_likelihood = FALSE,
  calc_coherence = TRUE,
  calc_r2 = FALSE,
  ...
)

Arguments

Value

Returns an S3 object of class c("LDA", "TopicModel").

Examples

## Not run: 
# load a document term matrix
d1 <- nih_sample_dtm[1:50,]

d2 <- nih_sample_dtm[51:100,]

# fit a model
m <- FitLdaModel(d1, k = 10, 
                 iterations = 200, burnin = 175,
                 optimize_alpha = TRUE, 
                 calc_likelihood = FALSE,
                 calc_coherence = TRUE,
                 calc_r2 = FALSE)

# update an existing model by adding documents
m2 <- update(object = m,
             dtm = rbind(d1, d2),
             iterations = 200,
             burnin = 175)
             
# use an old model as a prior for a new model
m3 <- update(object = m,
             dtm = d2, # new documents only
             iterations = 200,
             burnin = 175)
             
# add topics while updating a model by adding documents
m4 <- update(object = m,
             dtm = rbind(d1, d2),
             additional_k = 3,
             iterations = 200,
             burnin = 175)
             
# add topics to an existing model
m5 <- update(object = m,
             dtm = d1, # this is the old data
             additional_k = 3,
             iterations = 200,
             burnin = 175)


## End(Not run)