Generate cluster keyness statistics from a rainette result
Usage
rainette_stats(
groups,
dtm,
measure = c("chi2", "lr", "frequency", "docprop"),
n_terms = 15,
show_negative = TRUE,
max_p = 0.05
)Arguments
- groups
groups membership computed by
cutree_rainetteorcutree_rainette2- dtm
the dfm object used to compute the clustering
- measure
statistics to compute
- n_terms
number of terms to display in keyness plots
- show_negative
if TRUE, show negative keyness features
- max_p
maximum keyness statistic p-value
Value
A list with, for each group, a data.frame of keyness statistics for the most specific n_terms features.
Examples
# \donttest{
require(quanteda)
corpus <- data_corpus_inaugural
corpus <- head(corpus, n = 10)
corpus <- split_segments(corpus)
#> Splitting...
#> Done.
tok <- tokens(corpus, remove_punct = TRUE)
tok <- tokens_remove(tok, stopwords("en"))
dtm <- dfm(tok, tolower = TRUE)
dtm <- dfm_trim(dtm, min_docfreq = 3)
res <- rainette(dtm, k = 3, min_segment_size = 15)
#> Merging segments to comply with min_segment_size...
#> Clustering...
#> Done.
groups <- cutree_rainette(res, k = 3)
rainette_stats(groups, dtm)
#> [[1]]
#> # A tibble: 15 × 6
#> feature chi2 p n_target n_reference sign
#> <chr> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 commerce 28.2 0.000000107 23 1 positive
#> 2 states 26.3 0.000000290 49 18 positive
#> 3 force 25.4 0.000000475 23 2 positive
#> 4 revenue 21.9 0.00000294 16 0 positive
#> 5 made 18.1 0.0000214 26 7 positive
#> 6 shall -16.5 0.0000479 4 35 negative
#> 7 good -15.1 0.000102 1 24 negative
#> 8 powers 14.6 0.000134 20 5 positive
#> 9 within 13.7 0.000215 16 3 positive
#> 10 confidence -13.6 0.000222 1 22 negative
#> 11 defense 13.3 0.000261 12 1 positive
#> 12 naval 12.7 0.000358 11 0 positive
#> 13 united 11.7 0.000614 32 16 positive
#> 14 fellow -11.5 0.000692 3 25 negative
#> 15 war 11.4 0.000729 34 18 positive
#>
#> [[2]]
#> # A tibble: 15 × 6
#> feature chi2 p n_target n_reference sign
#> <chr> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 shall 40.9 1.57e-10 28 11 positive
#> 2 confidence 37.0 1.18e- 9 19 4 positive
#> 3 high 21.6 3.39e- 6 14 5 positive
#> 4 fellow 20.4 6.29e- 6 18 10 positive
#> 5 duties 18.3 1.88e- 5 17 10 positive
#> 6 station 17.5 2.94e- 5 9 1 positive
#> 7 future 14.5 1.43e- 4 11 4 positive
#> 8 good 14.3 1.56e- 4 15 10 positive
#> 9 fervent 13.0 3.12e- 4 6 0 positive
#> 10 indulgence 13.0 3.12e- 4 6 0 positive
#> 11 presence 13.0 3.12e- 4 6 0 positive
#> 12 states -12.7 3.60e- 4 5 62 negative
#> 13 conscious 10.3 1.34e- 3 5 0 positive
#> 14 derive 10.3 1.34e- 3 5 0 positive
#> 15 endeavor 10.3 1.34e- 3 5 0 positive
#>
#> [[3]]
#> # A tibble: 15 × 6
#> feature chi2 p n_target n_reference sign
#> <chr> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 can 22.2 0.00000250 29 17 positive
#> 2 happiness 20.4 0.00000617 15 4 positive
#> 3 let 17.9 0.0000232 11 1 positive
#> 4 truth 14.7 0.000125 8 0 positive
#> 5 upon 14.4 0.000149 17 9 positive
#> 6 man 13.6 0.000223 9 1 positive
#> 7 government 13.3 0.000271 45 48 positive
#> 8 order 12.7 0.000372 11 3 positive
#> 9 republican 11.5 0.000690 8 1 positive
#> 10 force -11.3 0.000778 0 25 negative
#> 11 love 11.0 0.000909 9 2 positive
#> 12 made -9.67 0.00188 2 31 negative
#> 13 freedom 8.89 0.00286 9 3 positive
#> 14 choice 8.13 0.00436 5 0 positive
#> 15 false 8.13 0.00436 5 0 positive
#>
# }