R: Weight-based Classification of Data Pairs

emClassify {RecordLinkage} | R Documentation

## Weight-based Classification of Data Pairs

### Description

Classifies data pairs to which weights were assigned by `emWeights`. Based on user-defined thresholds or predefined error rates.

### Usage

      emClassify(rpairs, threshold.upper = Inf,
        threshold.lower = threshold.upper, my = Inf, ny = Inf, ...)

      ## S4 method for signature 'RecLinkData,ANY,ANY'
    emClassify(rpairs, threshold.upper = Inf,
        threshold.lower = threshold.upper, my = Inf, ny = Inf)

      ## S4 method for signature 'RLBigData,ANY,ANY'
    emClassify(rpairs, threshold.upper = Inf,
        threshold.lower = threshold.upper, my = Inf, ny = Inf,
        withProgressBar = (sink.number()==0))

### Arguments

`rpairs` | `RecLinkData` object with weight information.
`my` | A probability. Error bound for false positives.
`ny` | A probability. Error bound for false negatives.
`threshold.upper` | A numeric value. Threshold for links.
`threshold.lower` | A numeric value. Threshold for possible links.
`withProgressBar` | Whether to display a progress bar
`...` | Placeholder for method-specific arguments.

### Details

Two general approaches are implemented. The classical procedure by Fellegi and Sunter (see references) minimizes the number of possible links with given error levels for false links (`my`) and false non-links (`ny`).

The second approach requires thresholds for links and possible links to be set by the user. A pair with weight w is classified as a link if w>=\textit{threshold.upper}, as a possible link if threshold.upper>=w>= treshold.lower and as a non-link if w<threshold.lower.

If `threshold.upper` or `threshold.lower` is given, the threshold-based approach is used, otherwise, if one of the error bounds is given, the Fellegi-Sunter model. If only `my` is supplied, links are chosen to meet the error bound and all other pairs are classified as non-links (the equivalent case holds if only `ny` is specified). If no further arguments than `rpairs` are given, a single threshold of 0 is used.

### Value

For the `"RecLinkData"` method, a S3 object of class `"RecLinkResult"` that represents a copy of `newdata` with element `rpairs$prediction`, which stores the classification result, as addendum.

For the `"RLBigData"` method, a S4 object of class `"RLResult"`.

### Note

The quality of classification of the Fellegi-Sunter method relies strongly on reasonable estimations of m- and u-probabilities. The results should be evaluated critically.

### Author(s)

Andreas Borg, Murat Sariyar

### References

Ivan P. Fellegi, Alan B. Sunter: A Theory for Record Linkage, in: Journal of the American Statistical Association Vol. 64, No. 328 (Dec., 1969), pp. 1183–1210.

### See Also

`getPairs` to produce output from which thresholds can be determined conveniently.

[Package RecordLinkage version 0.4-12.2 Index]