Hellinger distances, either pairwise within a single tidied topic model dataframe or between two tidied topic model dataframes
Usage
# S3 method for data.frame
hellinger(
topicsdf1,
id1 = "document",
cat1 = "topic",
prob1 = "prob",
topicsdf2 = NULL,
id2 = "document",
cat2 = "topic",
prob2 = "prob",
df = FALSE
)Arguments
- df
Should the function return the matrix of Hellinger distances (default) or a tidy dataframe?
- topicsdf1, topicsdf2
Tidied topic model dataframes
- id1, id2
Unit identifiers (DOIs, auids, ORU name, etc.)
- cat1, cat2
Category identifiers (topics)
- prob1, prob2
Probability values (gamma)
Examples
set.seed(2022-06-09)
topics1 = rdirichlet(3, rep(5, 5)) |>
tibble::as_tibble(rownames = 'doc_id',
.name_repair = tmfast:::make_colnames) |>
dplyr::mutate(doc_id = stringr::str_c('doc_', doc_id)) |>
tidyr::pivot_longer(tidyselect::starts_with('V'),
names_to = 'topic',
values_to = 'gamma')
topics2 = rdirichlet(3, rep(5, 5)) |>
tibble::as_tibble(rownames = 'doc_id',
.name_repair = tmfast:::make_colnames) |>
dplyr::mutate(doc_id = stringr::str_c('doc_', as.integer(doc_id) + 5)) |>
tidyr::pivot_longer(tidyselect::starts_with('V'),
names_to = 'topic',
values_to = 'gamma')
hellinger(topics1, doc_id, prob1 = 'gamma', df = TRUE)
#> # A tibble: 9 × 3
#> doc_id document dist
#> <chr> <chr> <dbl>
#> 1 doc_1 doc_1 0.0000000105
#> 2 doc_1 doc_2 0.307
#> 3 doc_1 doc_3 0.267
#> 4 doc_2 doc_1 0.307
#> 5 doc_2 doc_2 0
#> 6 doc_2 doc_3 0.123
#> 7 doc_3 doc_1 0.267
#> 8 doc_3 doc_2 0.123
#> 9 doc_3 doc_3 0
hellinger(topics1, doc_id, prob1 = 'gamma',
topicsdf2 = topics2, id2 = doc_id, prob2 = 'gamma')
#> doc_6 doc_7 doc_8
#> doc_1 0.2361547 0.2632094 0.33018266
#> doc_2 0.1777705 0.1060308 0.12270871
#> doc_3 0.1296687 0.1732766 0.08788004