rainette2 {rainette} | R Documentation |
Corpus clustering based on the Reinert method - Double clustering
Description
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 |
y |
if |
max_k |
maximum number of clusters to compute |
min_segment_size1 |
if |
min_segment_size2 |
if |
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 |
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 |
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.
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)
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
See Also
rainette()
, cutree_rainette2()
, rainette2_plot()
, rainette2_explor()
Examples
require(quanteda)
corpus <- data_corpus_inaugural
corpus <- head(corpus, n = 10)
corpus <- split_segments(corpus)
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)
res2 <- rainette(dtm, k = 5, min_segment_size = 15)
res <- rainette2(res1, res2, max_k = 4)