Tidy topic models fit by the stm package. The arguments and return values
are similar to `lda_tidiers`

.

# S3 method for STM tidy( x, matrix = c("beta", "gamma", "theta"), log = FALSE, document_names = NULL, ... ) # S3 method for estimateEffect tidy(x, ...) # S3 method for estimateEffect glance(x, ...) # S3 method for STM augment(x, data, ...) # S3 method for STM glance(x, ...)

x | An STM fitted model object from either |
---|---|

matrix | Whether to tidy the beta (per-term-per-topic, default) or gamma/theta (per-document-per-topic) matrix. The stm package calls this the theta matrix, but other topic modeling packages call this gamma. |

log | Whether beta/gamma/theta should be on a log scale, default FALSE |

document_names | Optional vector of document names for use with per-document-per-topic tidying |

... | Extra arguments, not used |

data | For |

`tidy`

returns a tidied version of either the beta or gamma matrix if
called on an object from `stm`

or a tidied version of the estimated regressions
if called on an object from `estimateEffect`

.

`glance`

always returns a one-row table, with columns

- k
Number of topics in the model

- docs
Number of documents in the model

- uncertainty
Uncertainty measure

`augment`

must be provided a data argument, either a
`dfm`

from quanteda or a table containing one row per original
document-term pair, such as is returned by tdm_tidiers, containing
columns `document`

and `term`

. It returns that same data as a table
with an additional column `.topic`

with the topic assignment for that
document-term combination.

`glance`

always returns a one-row table, with columns

- k
Number of topics in the model

- docs
Number of documents in the model

- terms
Number of terms in the model

- iter
Number of iterations used

- alpha
If an LDA model, the parameter of the Dirichlet distribution for topics over documents

If `matrix == "beta"`

(default), returns a table with one row per topic and term,
with columns

- topic
Topic, as an integer

- term
Term

- beta
Probability of a term generated from a topic according to the structural topic model

If `matrix == "gamma"`

, returns a table with one row per topic and document,
with columns

- topic
Topic, as an integer

- document
Document name (if given in vector of

`document_names`

) or ID as an integer- gamma
Probability of topic given document

If called on an object from `estimateEffect`

, returns a table with columns

- topic
Topic, as an integer

- term
The term in the model being estimated and tested

- estimate
The estimated coefficient

- std.error
The standard error from the linear model

- statistic
t-statistic

- p.value
two-sided p-value

if (FALSE) { if (requireNamespace("stm", quietly = TRUE)) { library(dplyr) library(ggplot2) library(stm) library(janeaustenr) austen_sparse <- austen_books() %>% unnest_tokens(word, text) %>% anti_join(stop_words) %>% count(book, word) %>% cast_sparse(book, word, n) topic_model <- stm(austen_sparse, K = 12, verbose = FALSE, init.type = "Spectral") # tidy the word-topic combinations td_beta <- tidy(topic_model) td_beta # Examine the topics td_beta %>% group_by(topic) %>% top_n(10, beta) %>% ungroup() %>% ggplot(aes(term, beta)) + geom_col() + facet_wrap(~ topic, scales = "free") + coord_flip() # tidy the document-topic combinations, with optional document names td_gamma <- tidy(topic_model, matrix = "gamma", document_names = rownames(austen_sparse)) td_gamma # using stm's gardarianFit, we can tidy the result of a model # estimated with covariates effects <- estimateEffect(1:3 ~ treatment, gadarianFit, gadarian) glance(effects) td_estimate <- tidy(effects) td_estimate } }