stm_tidiers | R Documentation |
Tidy topic models fit by the stm package. The arguments and return values
are similar to lda_tidiers()
.
## S3 method for class 'STM' tidy( x, matrix = c("beta", "gamma", "theta", "frex", "lift"), log = FALSE, document_names = NULL, ... ) ## S3 method for class 'estimateEffect' tidy(x, ...) ## S3 method for class 'estimateEffect' glance(x, ...) ## S3 method for class 'STM' augment(x, data, ...) ## S3 method for class 'STM' glance(x, ...)
x |
An STM fitted model object from either |
matrix |
Which matrix to tidy:
|
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 for tidying, such as |
data |
For |
tidy
returns a tidied version of either the beta, gamma, FREX, or
lift matrix if called on an object from stm::stm()
, or a tidied version of
the estimated regressions if called on an object from stm::estimateEffect()
.
glance
returns a tibble with exactly one row of model summaries.
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 with an additional
column .topic
with the topic assignment for that document-term combination.
lda_tidiers()
, stm::calcfrex()
, stm::calclift()
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) # tidy the word-topic combinations td_beta <- tidy(topic_model) td_beta # Examine the topics td_beta %>% group_by(topic) %>% slice_max(beta, n = 10) %>% ungroup() %>% ggplot(aes(beta, term)) + geom_col() + facet_wrap(~ topic, scales = "free") # high FREX words per topic tidy(topic_model, matrix = "frex") # high lift words per topic tidy(topic_model, matrix = "lift") # 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
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