RecordLinkage (version 0.4-11)

classifySupv: Supervised Classification

Description

Supervised classification of record pairs based on a trained model.

Usage

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

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".

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.

See Also

trainSupv for training of classifiers, classifyUnsup for unsupervised classification.

Examples

# NOT RUN {
# 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)

# }