## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----eval = FALSE-------------------------------------------------------------
# library(stm)
# library(quanteda)
# 
# # prepare data
# data <- corpus(gadarian, text_field = 'open.ended.response')
#  docvars(data)$text <- as.character(data)
# 
# data <- tokens(data, remove_punct = TRUE) |>
#    tokens_wordstem() |>
#    tokens_remove(stopwords('english')) |> dfm() |>
#    dfm_trim(min_termfreq = 2)
# 
# out <- convert(data, to = 'stm')
# 
# # fit models and effect estimates
# gadarian_3 <- stm(documents = out$documents,
#                  vocab = out$vocab,
#                  data = out$meta,
#                  prevalence = ~ treatment + s(pid_rep),
#                  K = 3, verbose = FALSE)
# prep_3 <- estimateEffect(1:3 ~ treatment + s(pid_rep), gadarian_3,
#                         meta = out$meta)
# gadarian_5 <- stm(documents = out$documents,
#                  vocab = out$vocab,
#                  data = out$meta,
#                  prevalence = ~ treatment + s(pid_rep),
#                  K = 5, verbose = FALSE)
# prep_5 <- estimateEffect(1:5 ~ treatment + s(pid_rep), gadarian_5,
#                         meta = out$meta)
# 
# # save objects in .RData file
# save.image('stm_gadarian.RData')

## ----eval = FALSE-------------------------------------------------------------
# library(stminsights)
# run_stminsights()