sim_dfm: Simulate a document-feature-matrix from a LDA specification
In contefranz/OpTop: Optimal topic specification for latent dirichlet allocation models
Description
Usage
Arguments
Value
Author(s)
References
See Also
Description
Simulate a document-feature-matrix from estimated DTW and TWW. The number of topics as well as
the number of documents are inferred from the LDA parameters.
Usage
1
Arguments
DTW
A matrix or data.frame with Document-Topic-Weights.
TWW
A matrix or data.frame with Topic-Word-Weights.
doc_length
A vector containing the desired document length as total number of word counts.
alpha
Parameter of the Dirichlet distribution for topics over documents.
seed
Input to set.seed.
Value
A dfm object.
Author(s)
Francesco Grossetti francesco.grossetti@unibocconi.it
Craig M. Lewis craig.lewis@owen.vanderbilt.edu
References
Lewis, C. and Grossetti, F. (2019 - forthcoming):
A Statistical Approach for Optimal Topic Model Identification.
See Also
contefranz/OpTop documentation built on Feb. 14, 2022, 7:04 p.m.
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Description Usage Arguments Value Author(s) References See Also
Description
Simulate a document-feature-matrix from estimated DTW and TWW. The number of topics as well as the number of documents are inferred from the LDA parameters.
Usage
1 |
Arguments
DTW |
A matrix or data.frame with Document-Topic-Weights. |
TWW |
A matrix or data.frame with Topic-Word-Weights. |
doc_length |
A vector containing the desired document length as total number of word counts. |
alpha |
Parameter of the Dirichlet distribution for topics over documents. |
seed |
Input to |
Value
A dfm object.
Author(s)
Francesco Grossetti francesco.grossetti@unibocconi.it
Craig M. Lewis craig.lewis@owen.vanderbilt.edu
References
Lewis, C. and Grossetti, F. (2019 - forthcoming):
A Statistical Approach for Optimal Topic Model Identification.
See Also
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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