quanteda (version 0.99.9)

dfm_trim: trim a dfm using frequency threshold-based feature selection

Description

Returns a document by feature matrix reduced in size based on document and term frequency, usually in terms of a minimum frequencies, but may also be in terms of maximum frequencies. Setting a combination of minimum and maximum frequencies will select features based on a range.

Usage

dfm_trim(x, min_count = 1, min_docfreq = 1, max_count = NULL,
  max_docfreq = NULL, sparsity = NULL,
  verbose = quanteda_options("verbose"))

Arguments

x

a dfm object

min_count, max_count

minimum/maximum count or fraction of features across all documents, below/above which features will be removed

min_docfreq, max_docfreq

minimum/maximum number or fraction of documents in which a feature appears, below/above which features will be removed

sparsity

equivalent to 1 - min_docfreq, included for comparison with tm

verbose

print messages

Value

A dfm reduced in features (with the same number of documents)

See Also

dfm_select, dfm_sample

Examples

# NOT RUN {
(myDfm <- dfm(data_corpus_inaugural[1:5]))

# keep only words occuring >=10 times and in >=2 docs
dfm_trim(myDfm, min_count = 10, min_docfreq = 2) 

# keep only words occuring >=10 times and in at least 0.4 of the documents
dfm_trim(myDfm, min_count = 10, min_docfreq = 0.4)

# keep only words occuring <=10 times and in <=2 docs
dfm_trim(myDfm, max_count = 10, max_docfreq = 2) 

# keep only words occuring <=10 times and in at most 3/4 of the documents
dfm_trim(myDfm, max_count = 10, max_docfreq = 0.75)

# keep only words occuring at least 0.01 times and in >=2 documents
dfm_trim(myDfm, min_count = .01, min_docfreq = 2)

# keep only words occuring 5 times in 1000, and in 2 of 5 of documents
dfm_trim(myDfm, min_docfreq = 0.4, min_count = 0.005)

# }
# NOT RUN {
# compare to removeSparseTerms from the tm package 
if (require(tm)) {
    (tmdtm <- convert(myDfm, "tm"))
    removeSparseTerms(tmdtm, 0.7)
    dfm_trim(td, min_docfreq = 0.3)
    dfm_trim(td, sparsity = 0.7)
}
# }