ProbabilisticLinkage | R Documentation |
Probabilistic Record Linkage of two data sets using distance-based or probabilistic methods.
ProbabilisticLinkage(IDA, dataA, IDB, dataB, blocking = NULL, similarity)
IDA |
A character vector or integer vector containing the IDs of the first data.frame. |
dataA |
A data.frame containing the data to be linked and all linking variables as specified in |
IDB |
A character vector or integer vector containing the IDs of the second data.frame. |
dataB |
A data.frame containing the data to be linked and all linking variables as specified in |
blocking |
Optional blocking variables. See |
similarity |
Variables used for linking and their respective linkage methods as specified in |
To call the Probabilistic Linkage function it is necessary to set up linking variables and methods. Using blocking variables is optional. Further options are available in SelectBlockingFunction
and SelectSimilarityFunction
. Using this method, the Fellegi-Sunter model is used, with the EM algorithm estimating the weights (Winkler 1988).
A data.frame containing pairs of IDs, their corresponding similarity value and the match status as determined by the linkage procedure.
Christen, P. (2012): Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer.
Schnell, R., Bachteler, T., Reiher, J. (2004): A toolbox for record linkage. Austrian Journal of Statistics 33(1-2): 125-133.
Winkler, W. E. (1988): Using the EM algorithm for weight computation in the Fellegi-Sunter model of record linkage. Proceedings of the Section on Survey Research Methods Vol. 667, American Statistical Association: 671.
CreateBF
,
CreateCLK
,
DeterministicLinkage
,
SelectBlockingFunction
,
SelectSimilarityFunction
,
StandardizeString
# load test data testFile <- file.path(path.package("PPRL"), "extdata/testdata.csv") testData <- read.csv(testFile, head = FALSE, sep = "\t", colClasses = "character") # define year of birth (V3) as blocking variable bl <- SelectBlockingFunction("V3", "V3", method = "exact") # Select first name and last name as linking variables, # to be linked using the Jaro-Winkler similarity measure (first name) # and levenshtein distance (last name) l1 <- SelectSimilarityFunction("V7", "V7", method = "jw") l2 <- SelectSimilarityFunction("V8", "V8", method = "lv") # Link the data as specified in bl and l1/l2 # (in this small example data is linked to itself) res <- ProbabilisticLinkage(testData$V1, testData, testData$V1, testData, similarity = c(l1, l2), blocking = bl)
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