stringdist (version 0.9.6)

stringsim: Compute similarity scores between strings

Description

stringsim computes pairwise string similarities between elements of character vectors a and b, where the vector with less elements is recycled. stringsimmatrix computes the string similarity matrix with rows according to a and columns according to b.

Usage

stringsim(
  a,
  b,
  method = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw",
    "soundex"),
  useBytes = FALSE,
  q = 1,
  ...
)
stringsimmatrix(
  a,
  b,
  method = c("osa", "lv", "dl", "hamming", "lcs", "qgram", "cosine", "jaccard", "jw",
    "soundex"),
  useBytes = FALSE,
  q = 1,
  ...
)

Arguments

a

R object (target); will be converted by as.character.

b

R object (source); will be converted by as.character.

method

Method for distance calculation. The default is "osa", see stringdist-metrics.

useBytes

Perform byte-wise comparison, see stringdist-encoding.

q

Size of the \(q\)-gram; must be nonnegative. Only applies to method='qgram', 'jaccard' or 'cosine'.

...

additional arguments are passed on to stringdist and stringdistmatrix respectively.

Value

stringsim returns a vector with similarities, which are values between 0 and 1 where 1 corresponds to perfect similarity (distance 0) and 0 to complete dissimilarity. NA is returned when stringdist returns NA. Distances equal to Inf are truncated to a similarity of 0. stringsimmatrix works the same way but, equivalent to stringdistmatrix, returns a similarity matrix instead of a vector.

Details

The similarity is calculated by first calculating the distance using stringdist, dividing the distance by the maximum possible distance, and substracting the result from 1. This results in a score between 0 and 1, with 1 corresponding to complete similarity and 0 to complete dissimilarity. Note that complete similarity only means equality for distances satisfying the identity property. This is not the case e.g. for q-gram based distances (for example if q=1, anagrams are completely similar). For distances where weights can be specified, the maximum distance is currently computed by assuming that all weights are equal to 1.

Examples

# NOT RUN {

# Calculate the similarity using the default method of optimal string alignment
stringsim("ca", "abc")

# Calculate the similarity using the Jaro-Winkler method
# The p argument is passed on to stringdist
stringsim('MARTHA','MATHRA',method='jw', p=0.1)

# }