Apply varieties of term frequency weightings to a dfm.

tf(x, scheme = c("count", "prop", "propmax", "boolean", "log", "augmented",
"logave"), base = 10, K = 0.5)

## Arguments

x object for which idf or tf-idf will be computed (a document-feature matrix) divisor for the normalization of feature frequencies by document. Valid types include: countdefault, each feature count will remain as feature counts, equivalent to dividing by 1 propfeature proportions within document, equivalent to dividing each term by the total count of features in the document. propmaxfeature proportions relative to the most frequent term of the document, equivalent to dividing term counts by the frequency of the most frequent term in the document. booleanrecode all non-zero counts as 1 logtake the logarithm of 1 + each count, for base base augmentedequivalent to K + (1 - K) * tf(x, "propmax") logave(1 + the log of the counts) / (1 + log of the counts / the average count within document) base for the logarithm when scheme is "log" or logave the K for the augmentation when scheme = "augmented"

## Value

A document feature matrix to which the weighting scheme has been applied.

## Details

tf(x, scheme = "prop") is equivalent to weight(x, "relFreq")).

## References

Manning, C. D., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. https://en.wikipedia.org/wiki/Tf-idf#Term_frequency_2