Package 'rainette' reference manual

Title:	The Reinert Method for Textual Data Clustering
Description:	An R implementation of the Reinert text clustering method. For more details about the algorithm see the included vignettes or Reinert (1990) <doi:10.1177/075910639002600103>.
Authors:	Julien Barnier [aut, cre], Florian Privé [ctb]
Maintainer:	Julien Barnier <[email protected]>
License:	GPL (>= 3)
Version:	0.3.1.9000
Built:	2024-09-02 05:10:38 UTC
Source:	https://github.com/juba/rainette

Split a dtm into two clusters with reinert algorithm

Description

Split a dtm into two clusters with reinert algorithm

Usage

cluster_tab(dtm, cc_test = 0.3, tsj = 3)
cluster_tab(dtm, cc_test = 0.3, tsj = 3)

Arguments

`dtm`	to be split, passed by `rainette`
`cc_test`	maximum contingency coefficient value for the feature to be kept in both groups.
`tsj`	minimum feature frequency in the dtm

Details

Internal function, not to be used directly

Value

An object of class hclust and rainette

Returns the number of segment of each cluster for each source document

Description

Returns the number of segment of each cluster for each source document

Usage

clusters_by_doc_table(obj, clust_var = NULL, doc_id = NULL, prop = FALSE)
clusters_by_doc_table(obj, clust_var = NULL, doc_id = NULL, prop = FALSE)

Arguments

`obj`	a corpus, tokens or dtm object
`clust_var`	name of the docvar with the clusters
`doc_id`	docvar identifying the source document
`prop`	if TRUE, returns the percentage of each cluster by document

Details

This function is only useful for previously segmented corpus. If doc_id is NULL and there is a sement_source docvar, it will be used instead.

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 = 2)
res <- rainette(dtm, k = 3, min_segment_size = 15)
corpus$cluster <- cutree(res, k = 3)
clusters_by_doc_table(corpus, clust_var = "cluster", prop = TRUE)

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 = 2)
res <- rainette(dtm, k = 3, min_segment_size = 15)
corpus$cluster <- cutree(res, k = 3)
clusters_by_doc_table(corpus, clust_var = "cluster", prop = TRUE)

Cut a tree into groups

Description

Cut a tree into groups

Usage

cutree(tree, ...)
cutree(tree, ...)

Arguments

`tree`	the hclust tree object to be cut
`...`	arguments passed to other methods

Details

If tree is of class rainette, invokes cutree_rainette(). Otherwise, just run stats::cutree().

Value

A vector with group membership.

Cut a rainette result tree into groups of documents

Description

Cut a rainette result tree into groups of documents

Usage

cutree_rainette(hres, k = NULL, h = NULL, ...)
cutree_rainette(hres, k = NULL, h = NULL, ...)

Arguments

`hres`	the `rainette` result object to be cut
`k`	the desired number of clusters
`h`	unsupported
`...`	arguments passed to other methods

Value

A vector with group membership.

Cut a rainette2 result object into groups of documents

Description

Cut a rainette2 result object into groups of documents

Usage

cutree_rainette2(res, k, criterion = c("chi2", "n"), ...)
cutree_rainette2(res, k, criterion = c("chi2", "n"), ...)

Arguments

`res`	the `rainette2` result object to be cut
`k`	the desired number of clusters
`criterion`	criterion to use to choose the best partition. `chi2` means the partition with the maximum sum of chi2, `n` the partition with the maximum size.
`...`	arguments passed to other methods

Value

A vector with group membership.

Returns, for each cluster, the number of source documents with at least n segments of this cluster

Description

Returns, for each cluster, the number of source documents with at least n segments of this cluster

Usage

docs_by_cluster_table(obj, clust_var = NULL, doc_id = NULL, threshold = 1)
docs_by_cluster_table(obj, clust_var = NULL, doc_id = NULL, threshold = 1)

Arguments

`obj`	a corpus, tokens or dtm object
`clust_var`	name of the docvar with the clusters
`doc_id`	docvar identifying the source document
`threshold`	the minimal number of segments of a given cluster that a document must include to be counted

Details

This function is only useful for previously segmented corpus. If doc_id is NULL and there is a sement_source docvar, it will be used instead.

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 = 2)
res <- rainette(dtm, k = 3, min_segment_size = 15)
corpus$cluster <- cutree(res, k = 3)
docs_by_cluster_table(corpus, clust_var = "cluster")

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 = 2)
res <- rainette(dtm, k = 3, min_segment_size = 15)
corpus$cluster <- cutree(res, k = 3)
docs_by_cluster_table(corpus, clust_var = "cluster")

Import a corpus in Iramuteq format

Description

Import a corpus in Iramuteq format

Usage

import_corpus_iramuteq(f, id_var = NULL, thematics = c("remove", "split"), ...)
import_corpus_iramuteq(f, id_var = NULL, thematics = c("remove", "split"), ...)

Arguments

`f`	a file name or a connection
`id_var`	name of metadata variable to be used as documents id
`thematics`	if "remove", thematics lines are removed. If "split", texts as splitted at each thematic, and metadata duplicated accordingly
`...`	arguments passed to `file` if `f` is a file name.

Details

A description of the Iramuteq corpus format can be found here : http://www.iramuteq.org/documentation/html/2-2-2-les-regles-de-formatages

Value

A quanteda corpus object. Note that metadata variables in docvars are all imported as characters.

Merges segments according to minimum segment size

Description

rainette_uc_index docvar

Usage

merge_segments(dtm, min_segment_size = 10, doc_id = NULL)
merge_segments(dtm, min_segment_size = 10, doc_id = NULL)

Arguments

`dtm`	dtm of segments
`min_segment_size`	minimum number of forms by segment
`doc_id`	character name of a dtm docvar which identifies source documents.

Details

If min_segment_size == 0, no segments are merged together. If min_segment_size > 0 then doc_id must be provided unless the corpus comes from split_segments, in this case segment_source is used by default.

Value

the original dtm with a new rainette_uc_id docvar.

return documents indices ordered by CA first axis coordinates

Description

return documents indices ordered by CA first axis coordinates

Usage

order_docs(m)
order_docs(m)

Arguments

`m`	dtm on which to compute the CA and order documents, converted to an integer matrix.

Details

Internal function, not to be used directly

Value

ordered list of document indices

Corpus clustering based on the Reinert method - Simple clustering

Description

Corpus clustering based on the Reinert method - Simple clustering

Usage

rainette(
  dtm,
  k = 10,
  min_segment_size = 0,
  doc_id = NULL,
  min_split_members = 5,
  cc_test = 0.3,
  tsj = 3,
  min_members,
  min_uc_size
)
rainette(
  dtm,
  k = 10,
  min_segment_size = 0,
  doc_id = NULL,
  min_split_members = 5,
  cc_test = 0.3,
  tsj = 3,
  min_members,
  min_uc_size
)

Arguments

`dtm`	quanteda dfm object of documents to cluster, usually the result of `split_segments()`
`k`	maximum number of clusters to compute
`min_segment_size`	minimum number of forms by document
`doc_id`	character name of a dtm docvar which identifies source documents.
`min_split_members`	don't try to split groups with fewer members
`cc_test`	contingency coefficient value for feature selection
`tsj`	minimum frequency value for feature selection
`min_members`	deprecated, use `min_split_members` instead
`min_uc_size`	deprecated, use `min_segment_size` instead

Details

See the references for original articles on the method. Computations and results may differ quite a bit, see the package vignettes for more details.

The dtm object is automatically converted to boolean.

If min_segment_size > 0 then doc_id must be provided unless the corpus comes from split_segments, in this case segment_source is used by default.

Value

The result is a list of both class hclust and rainette. Besides the elements of an hclust object, two more results are available :

uce_groups give the group of each document for each k
group give the group of each document for the maximum value of k available

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


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)
res <- rainette(dtm, k = 3, min_segment_size = 15)


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)
res <- rainette(dtm, k = 3, min_segment_size = 15)

Shiny gadget for rainette clustering exploration

Description

Shiny gadget for rainette clustering exploration

Usage

rainette_explor(res, dtm = NULL, corpus_src = NULL)
rainette_explor(res, dtm = NULL, corpus_src = NULL)

Arguments

`res`	result object of a `rainette` clustering
`dtm`	the dfm object used to compute the clustering
`corpus_src`	the quanteda corpus object used to compute the dtm

Value

No return value, called for side effects.

Examples

## Not run: 
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)
res <- rainette(dtm, k = 3, min_segment_size = 15)
rainette_explor(res, dtm, corpus)

## End(Not run)

## Not run: 
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)
res <- rainette(dtm, k = 3, min_segment_size = 15)
rainette_explor(res, dtm, corpus)

## End(Not run)

Generate a clustering description plot from a rainette result

Description

Generate a clustering description plot from a rainette result

Usage

rainette_plot(
  res,
  dtm,
  k = NULL,
  type = c("bar", "cloud"),
  n_terms = 15,
  free_scales = FALSE,
  measure = c("chi2", "lr", "frequency", "docprop"),
  show_negative = FALSE,
  text_size = NULL,
  show_na_title = TRUE,
  cluster_label = NULL,
  keyness_plot_xlab = NULL,
  colors = NULL
)
rainette_plot(
  res,
  dtm,
  k = NULL,
  type = c("bar", "cloud"),
  n_terms = 15,
  free_scales = FALSE,
  measure = c("chi2", "lr", "frequency", "docprop"),
  show_negative = FALSE,
  text_size = NULL,
  show_na_title = TRUE,
  cluster_label = NULL,
  keyness_plot_xlab = NULL,
  colors = NULL
)

Arguments

`res`	result object of a `rainette` clustering
`dtm`	the dfm object used to compute the clustering
`k`	number of groups. If NULL, use the biggest number possible
`type`	type of term plots : barplot or wordcloud
`n_terms`	number of terms to display in keyness plots
`free_scales`	if TRUE, all the keyness plots will have the same scale
`measure`	statistics to compute
`show_negative`	if TRUE, show negative keyness features
`text_size`	font size for barplots, max word size for wordclouds
`show_na_title`	if TRUE, show number of NA as plot title
`cluster_label`	define a specific term for clusters identification in keyness plots. Default is "Cluster" or "Cl." depending on the number of groups. If a vector of length > 1, define the cluster labels manually.
`keyness_plot_xlab`	define a specific x label for keyness plots.
`colors`	vector of custom colors for cluster titles and branches (in the order of the clusters)

Value

A gtable object.

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)
res <- rainette(dtm, k = 3, min_segment_size = 15)
rainette_plot(res, dtm)
rainette_plot(
  res,
  dtm,
  cluster_label = c("Assets", "Future", "Values"),
  colors = c("red", "slateblue", "forestgreen")
)


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)
res <- rainette(dtm, k = 3, min_segment_size = 15)
rainette_plot(res, dtm)
rainette_plot(
  res,
  dtm,
  cluster_label = c("Assets", "Future", "Values"),
  colors = c("red", "slateblue", "forestgreen")
)

Generate cluster keyness statistics from a rainette result

Description

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
)
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_rainette` or `cutree_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


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)
res <- rainette(dtm, k = 3, min_segment_size = 15)
groups <- cutree_rainette(res, k = 3)
rainette_stats(groups, dtm)

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)
res <- rainette(dtm, k = 3, min_segment_size = 15)
groups <- cutree_rainette(res, k = 3)
rainette_stats(groups, dtm)

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,
  ...
)
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

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

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)


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)

Complete groups membership with knn classification

Description

Starting with groups membership computed from a rainette2 clustering, every document not assigned to a cluster is reassigned using a k-nearest neighbour classification.

Usage

rainette2_complete_groups(dfm, groups, k = 1, ...)
rainette2_complete_groups(dfm, groups, k = 1, ...)

Arguments

`dfm`	dfm object used for `rainette2` clustering.
`groups`	group membership computed by `cutree` on `rainette2` result.
`k`	number of neighbours considered.
`...`	other arguments passed to `FNN::knn`.

Value

Completed group membership vector.

Shiny gadget for rainette2 clustering exploration

Description

Shiny gadget for rainette2 clustering exploration

Usage

rainette2_explor(res, dtm = NULL, corpus_src = NULL)
rainette2_explor(res, dtm = NULL, corpus_src = NULL)

Arguments

`res`	result object of a `rainette2` clustering
`dtm`	the dfm object used to compute the clustering
`corpus_src`	the quanteda corpus object used to compute the dtm

Value

No return value, called for side effects.

Generate a clustering description plot from a rainette2 result

Description

Generate a clustering description plot from a rainette2 result

Usage

rainette2_plot(
  res,
  dtm,
  k = NULL,
  criterion = c("chi2", "n"),
  complete_groups = FALSE,
  type = c("bar", "cloud"),
  n_terms = 15,
  free_scales = FALSE,
  measure = c("chi2", "lr", "frequency", "docprop"),
  show_negative = FALSE,
  text_size = 10
)
rainette2_plot(
  res,
  dtm,
  k = NULL,
  criterion = c("chi2", "n"),
  complete_groups = FALSE,
  type = c("bar", "cloud"),
  n_terms = 15,
  free_scales = FALSE,
  measure = c("chi2", "lr", "frequency", "docprop"),
  show_negative = FALSE,
  text_size = 10
)

Arguments

`res`	result object of a `rainette2` clustering
`dtm`	the dfm object used to compute the clustering
`k`	number of groups. If NULL, use the biggest number possible
`criterion`	criterion to use to choose the best partition. `chi2` means the partition with the maximum sum of chi2, `n` the partition with the maximum size.
`complete_groups`	if TRUE, documents with NA cluster are reaffected by k-means clustering initialised with current groups centers.
`type`	type of term plots : barplot or wordcloud
`n_terms`	number of terms to display in keyness plots
`free_scales`	if TRUE, all the keyness plots will have the same scale
`measure`	statistics to compute
`show_negative`	if TRUE, show negative keyness features
`text_size`	font size for barplots, max word size for wordclouds

Value

A gtable object.

Remove features from dtm of each group base don cc_test and tsj

Description

Remove features from dtm of each group base don cc_test and tsj

Usage

select_features(m, indices1, indices2, cc_test = 0.3, tsj = 3)
select_features(m, indices1, indices2, cc_test = 0.3, tsj = 3)

Arguments

`m`	global dtm
`indices1`	indices of documents of group 1
`indices2`	indices of documents of group 2
`cc_test`	maximum contingency coefficient value for the feature to be kept in both groups.
`tsj`	minimum feature frequency in the dtm

Details

Internal function, not to be used directly

Value

a list of two character vectors : cols1 is the name of features to keep in group 1, cols2 the name of features to keep in group 2

Split a character string or corpus into segments

Description

Split a character string or corpus into segments, taking into account punctuation where possible

Usage

split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'character'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'Corpus'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'corpus'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'tokens'
split_segments(obj, segment_size = 40, segment_size_window = NULL)
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'character'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'Corpus'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'corpus'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

## S3 method for class 'tokens'
split_segments(obj, segment_size = 40, segment_size_window = NULL)

Arguments

`obj`	character string, quanteda or tm corpus object
`segment_size`	segment size (in words)
`segment_size_window`	window around segment size to look for best splitting point

Value

If obj is a tm or quanteda corpus object, the result is a quanteda corpus.

Examples


require(quanteda)
split_segments(data_corpus_inaugural)

require(quanteda)
split_segments(data_corpus_inaugural)

Switch documents between two groups to maximize chi-square value

Description

Switch documents between two groups to maximize chi-square value

Usage

switch_docs(m, indices, max_index, max_chisq)
switch_docs(m, indices, max_index, max_chisq)

Arguments

`m`	original dtm
`indices`	documents indices orderes by first CA axis coordinates
`max_index`	document index where the split is maximum
`max_chisq`	maximum chi-square value

Details

Internal function, not to be used directly

Value

a list of two vectors indices1 and indices2, which contain the documents indices of each group after documents switching, and a chisq value, the new corresponding chi-square value after switching

Package 'rainette'

Help Index

Split a dtm into two clusters with reinert algorithm

Description

Usage

Arguments

Details

Value

Returns the number of segment of each cluster for each source document

Description

Usage

Arguments

Details

See Also

Examples

Cut a tree into groups

Description

Usage

Arguments

Details

Value

Cut a rainette result tree into groups of documents

Description

Usage

Arguments

Value

Cut a rainette2 result object into groups of documents

Description

Usage

Arguments

Value

See Also

Returns, for each cluster, the number of source documents with at least n segments of this cluster

Description

Usage

Arguments

Details

See Also

Examples

Import a corpus in Iramuteq format

Description

Usage

Arguments

Details

Value

Merges segments according to minimum segment size

Description

Usage

Arguments

Details

Value

return documents indices ordered by CA first axis coordinates

Description

Usage

Arguments

Details

Value

Corpus clustering based on the Reinert method - Simple clustering

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Shiny gadget for rainette clustering exploration

Description

Usage

Arguments

Value

See Also

Examples

Generate a clustering description plot from a rainette result

Description

Usage

Arguments

Value

See Also

Examples