R: Supervised Classification

classifySupv {RecordLinkage} | R Documentation

## Supervised Classification

### Description

Supervised classification of record pairs based on a trained model.

### Usage

      classifySupv(model, newdata, ...)

      ## S4 method for signature 'RecLinkClassif,RecLinkData'
    classifySupv(model, newdata,
        convert.na = TRUE, ...)

      ## S4 method for signature 'RecLinkClassif,RLBigData'
    classifySupv(model, newdata,
        convert.na = TRUE, withProgressBar = (sink.number()==0), ...)

### Arguments

`model` | Object of class `RecLinkClassif`. The calibrated model. See `trainSupv`.
`newdata` | Object of class `"RecLinkData"` or `"RLBigData"`. The data to classify.
`convert.na` | Logical. Whether to convert missing values in the comparison patterns to 0.
`withProgressBar` | Whether to display a progress bar
`...` | Further arguments for the `predict` method.

### Details

The record pairs in `newdata` are classified by calling the appropriate `predict` method for `model$model`.

By default, the `"RLBigDataDedup"` method displays a progress bar unless output is diverted by `sink`, e.g. when processing a Sweave file.

### 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"`.

### Author(s)

Andreas Borg, Murat Sariyar

### See Also

`trainSupv` for training of classifiers, `classifyUnsup` for unsupervised classification.

### Examples

    # Split data into training and validation set, train and classify with rpart
    data(RLdata500)
    pairs=compare.dedup(RLdata500, identity=identity.RLdata500,
                        blockfld=list(1,3,5,6,7))
    l=splitData(pairs, prop=0.5, keep.mprop=TRUE)
    model=trainSupv(l$train, method="rpart", minsplit=5)
    result=classifySupv(model=model, newdata=l$valid)
    summary(result)

[Package RecordLinkage version 0.4-12.2 Index]