For a dfm object, returns a (weighted) document frequency for each term. The default is a simple count of the number of documents in which a feature occurs more than a given frequency threshold. (The default threshold is zero, meaning that any feature occurring at least once in a document will be counted.)

docfreq(x, scheme = c("count", "inverse", "inversemax", "inverseprob",
  "unary"), smoothing = 0, k = 0, base = 10, threshold = 0,
  use.names = TRUE)

Arguments

x

a dfm

scheme

type of document frequency weighting, computed as follows, where \(N\) is defined as the number of documents in the dfm and \(s\) is the smoothing constant:

count

\(df_j\), the number of documents for which \(n_{ij} > threshold\)

inverse

$$\textrm{log}_{base}\left(s + \frac{N}{k + df_j}\right)$$

inversemax

$$\textrm{log}_{base}\left(s + \frac{\textrm{max}(df_j)}{k + df_j}\right)$$

inverseprob

$$\textrm{log}_{base}\left(\frac{N - df_j}{k + df_j}\right)$$

unary

1 for each feature

smoothing

added to the quotient before taking the logarithm

k

added to the denominator in the "inverse" weighting types, to prevent a zero document count for a term

base

the base with respect to which logarithms in the inverse document frequency weightings are computed; default is 10 (see Manning, Raghavan, and Schütze 2008, p123).

threshold

numeric value of the threshold above which a feature will considered in the computation of document frequency. The default is 0, meaning that a feature's document frequency will be the number of documents in which it occurs greater than zero times.

use.names

logical; if TRUE attach feature labels as names of the resulting numeric vector

...

not used

Value

a numeric vector of document frequencies for each feature

References

Manning, C. D., Raghavan, P., & Schütze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.

Examples

mydfm <- dfm(data_corpus_inaugural[1:2]) docfreq(mydfm[, 1:20])
#> fellow-citizens of the senate and #> 1 2 2 1 2 #> house representatives : among vicissitudes #> 1 1 2 1 1 #> incident to life no event #> 1 2 1 1 1 #> could have filled me with #> 1 2 1 2 1
# replication of worked example from # https://en.wikipedia.org/wiki/Tf-idf#Example_of_tf.E2.80.93idf wiki_dfm <- matrix(c(1,1,2,1,0,0, 1,1,0,0,2,3), byrow = TRUE, nrow = 2, dimnames = list(docs = c("document1", "document2"), features = c("this", "is", "a", "sample", "another", "example"))) %>% as.dfm() wiki_dfm
#> Document-feature matrix of: 2 documents, 6 features (33.3% sparse). #> 2 x 6 sparse Matrix of class "dfm" #> features #> docs this is a sample another example #> document1 1 1 2 1 0 0 #> document2 1 1 0 0 2 3
docfreq(wiki_dfm)
#> this is a sample another example #> 2 2 1 1 1 1
docfreq(wiki_dfm, scheme = "inverse")
#> this is a sample another example #> 0.00000 0.00000 0.30103 0.30103 0.30103 0.30103
docfreq(wiki_dfm, scheme = "inverse", k = 1, smoothing = 1)
#> this is a sample another example #> 0.2218487 0.2218487 0.3010300 0.3010300 0.3010300 0.3010300
docfreq(wiki_dfm, scheme = "unary")
#> this is a sample another example #> 1 1 1 1 1 1
docfreq(wiki_dfm, scheme = "inversemax")
#> this is a sample another example #> 0.00000 0.00000 0.30103 0.30103 0.30103 0.30103
docfreq(wiki_dfm, scheme = "inverseprob")
#> this is a sample another example #> 0 0 0 0 0 0