Corpus clustering based on the Reinert method - Double clustering

Usage

rainette2(
  x,
  y = NULL,
  max_k = 5,
  min_segment_size1 = 10,
  min_segment_size2 = 15,
  doc_id = NULL,
  min_members = 10,
  min_chi2 = 3.84,
  parallel = FALSE,
  full = TRUE,
  uc_size1,
  uc_size2,
  ...
)

Arguments

x: either a quanteda dfm object or the result of rainette()
y: if x is a rainette() result, this must be another rainette() result from same dfm but with different uc size.
max_k: maximum number of clusters to compute
min_segment_size1: if x is a dfm, minimum uc size for first clustering
min_segment_size2: if x is a dfm, minimum uc size for second clustering
doc_id: character name of a dtm docvar which identifies source documents.
min_members: minimum members of each cluster
min_chi2: minimum chi2 for each cluster
parallel: if TRUE, use parallel::mclapply to compute partitions (won't work on Windows, uses more RAM)
full: if TRUE, all crossed groups are kept to compute optimal partitions, otherwise only the most mutually associated groups are kept.
uc_size1: deprecated, use min_segment_size1 instead
uc_size2: deprecated, use min_segment_size2 instead
...: if x is a dfm object, parameters passed to rainette() for both simple clusterings

Value

A tibble with optimal partitions found for each available value of k as rows, and the following columns :

clusters list of the crossed original clusters used in the partition
k the number of clusters
chi2 sum of the chi2 value of each cluster
n sum of the size of each cluster
groups group membership of each document for this partition (NA if not assigned)

Details

You can pass a quanteda dfm as x object, the function then performs two simple clustering with varying minimum uc size, and then proceed to find optimal partitions based on the results of both clusterings.

If both clusterings have already been computed, you can pass them as x and y arguments and the function will only look for optimal partitions.

doc_id must be provided unless the corpus comes from split_segments, in this case segment_source is used by default.

If full = FALSE, computation may be much faster, but the chi2 criterion will be the only one available for best partition detection, and the result may not be optimal.

For more details on optimal partitions search algorithm, please see package vignettes.

References

Reinert M, Une méthode de classification descendante hiérarchique : application à l'analyse lexicale par contexte, Cahiers de l'analyse des données, Volume 8, Numéro 2, 1983. http://www.numdam.org/item/?id=CAD_1983__8_2_187_0
Reinert M., Alceste une méthodologie d'analyse des données textuelles et une application: Aurelia De Gerard De Nerval, Bulletin de Méthodologie Sociologique, Volume 26, Numéro 1, 1990. doi:10.1177/075910639002600103

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)

res1 <- rainette(dtm, k = 5, min_segment_size = 10)
#>   Merging segments to comply with min_segment_size...
#>   Clustering...
#>   Done.
res2 <- rainette(dtm, k = 5, min_segment_size = 15)
#>   Merging segments to comply with min_segment_size...
#>   Clustering...
#>   Done.

res <- rainette2(res1, res2, max_k = 4)
#>   Searching for best partitions...
#>   Computing size 2 partitions...
#>   Computing size 3 partitions...
#>   Computing size 4 partitions...
#>   Selecting best partitions...
#>   Done.
# }