https://github.com/cran/RecordLinkage
Tip revision: 91236cabdeb855d9c01a2714a751518c47acdc6b authored by Andreas Borg on 04 March 2019, 14:20:44 UTC
version 0.4-10.2
version 0.4-10.2
Tip revision: 91236ca
compare.rd
\name{compare}
\alias{compare.dedup}
\alias{compare.linkage}
\title{Compare Records}
\description{Builds comparison patterns of record pairs for deduplication or
linkage.}
\usage{
compare.dedup (dataset, blockfld = FALSE, phonetic = FALSE,
phonfun = pho_h, strcmp = FALSE, strcmpfun = jarowinkler, exclude = FALSE,
identity = NA, n_match = NA, n_non_match = NA)
compare.linkage (dataset1, dataset2, blockfld = FALSE,
phonetic = FALSE, phonfun = pho_h, strcmp = FALSE,
strcmpfun = jarowinkler, exclude = FALSE, identity1 = NA, identity2 = NA,
n_match = NA, n_non_match = NA)
}
\arguments{
\item{dataset}{Table of records to be deduplicated. Either a data frame or
a matrix.}
\item{dataset1, dataset2}{Two data sets to be linked.}
\item{blockfld}{Blocking field definition. A list of integer or character vectors
with column indices or \code{FALSE} to disable
blocking. See details and examples.}
\item{phonetic}{Determines usage of a phonetic code. If \code{FALSE}, no
phonetic code will be used; if \code{TRUE}, the phonetic code
will be used for all columns; if a numeric or character vector is given, the
phonetic code will be used for the specified columns.}
\item{phonfun}{Function for phonetic code. See details.}
\item{strcmp}{Determines usage of a string metric. Used in the same manner
as \code{phonetic}}
\item{strcmpfun}{User-defined function for string metric. See details.}
\item{exclude}{Columns to be excluded. A numeric or character vector specifying
the columns
which should be excluded from comparision}
\item{identity, identity1, identity2}{Optional numerical vectors for identifying matches and
non-matches. In a deduplication process, two records \code{dataset[i,]}
and \code{dataset[j,]} are a true match if and only if
\code{identity[i,]==identity[j,]}. In a linkage process, two
records \code{dataset1[i,]} and \code{dataset2[j,]} are a true
match if and only if \cr \code{identity1[i,]==identity2[j,]}.}
\item{n_match, n_non_match}{Number of desired matches and non-matches in
the result.}
}
\value{An object of class \code{RecLinkPairs} with the following components:
\item{data}{Copy of the records, converted to a data frame.}
\item{pairs}{Generated comparison patterns.}
\item{frequencies}{For each column included in \code{pairs}, the average
frequency of values (reciprocal of number of distinct values).}
}
\details{
These functions build record pairs and finally comparison patterns
by which these pairs are later classified as links or non-links. They make up
the initial stage in a Record Linkage process after possibly
normalizing the data. Two general
scenarios are reflected by the two functions: \code{compare.dedup} works on a
single data set which is to be deduplicated, \code{compare.linkage} is intended
for linking two data sets together.
Data sets are represented as data frames or matrices (typically of type
character), each row representing one record, each column representing one
field or attribute (like first name, date of birth\ldots). Row names are not
retained in the record pairs. If an identifier other than row number is
needed, it should be supplied as a designated column and excluded from
comparison (see note on \code{exclude} below).
Each element of \code{blockfld} specifies a set of columns in which two
records must agree to be included in the output. Each blocking definition in
the list is applied individually, the sets obtained
thereby are combined by a union operation.
If \code{blockfld} is \code{FALSE}, no blocking will be performed,
which leads to a large number of record pairs
(\eqn{\frac{n(n-1)}{2}}{n*(n-1)/2} where \eqn{n} is the number of
records).
As an alternative to blocking, a determined number of \code{n_match} matches
and \code{n_non_match} non-matches can be drawn if \code{identity} or
\code{identity1} and \code{identity2} are supplied. This is relevant for
generating training sets for the supervised classificators (see
\code{\link{trainSupv}}).
Fields can be excluded from the linkage process by supplying their column
index in the vector \code{exclude}, which is espacially useful for
external identifiers. Excluded fields can still be used for
blocking, also with phonetic code.
Phonetic codes and string similarity measures are supported for enhanced
detection of misspellings. Applying a phonetic code leads to a binary
values, where 1 denotes equality of the generated phonetic code.
A string comparator leads to a similarity value in the range \eqn{[0,1]}.
String comparison is not allowed on a field for which a phonetic code
is generated. For phonetic encoding functions included in the package,
see \link{phonetics}. For the included string comparators, see
\code{\link{jarowinkler}} and \code{\link{levenshteinSim}}.
Please note that phonetic code and string
metrics can slow down the generation of comparison patterns significantly.
User-defined functions for phonetic code and string comparison can be supplied
via the arguments \code{phonfun} and \code{strcmpfun}. \code{phonfun} is
expected to have a single character argument (the string to be transformed) and must
return a character value with the encoded string.
\code{strcmpfun} must have as arguments the two strings to be compared and
return a similarity value in the range \eqn{[0,1]}, with 0 denoting the lowest
and 1 denoting the highest degree of similarity. Both
functions must be fully vectorized to work on matrices.
}
\seealso{
\code{\link{RecLinkData}} for the format of returned objects,
%- \code{\link{genSamples}} for automatic generation of training data.
}
\author{Andreas Borg, Murat Sariyar}
\examples{
data(RLdata500)
data(RLdata10000)
# deduplication without blocking, use string comparator on names
\dontrun{rpairs=compare.dedup(RLdata500,strcmp=1:4)}
# linkage with blocking on first name and year of birth, use phonetic
# code on first components of first and last name
rpairs=compare.linkage(RLdata500,RLdata10000,blockfld=c(1,7),phonetic=c(1,3))
# deduplication with blocking on either last name or complete date of birth,
# use string comparator on all fields, include identity information
rpairs=compare.dedup(RLdata500, identity=identity.RLdata500, strcmp=TRUE,
blockfld=list(1,c(5,6,7)))
# Draw 100 matches and 1000 non-matches
\dontrun{rpairs=compare.dedup(RLdata10000,identity=identity.RLdata10000,n_match=100,
n_non_match=10000)}
}
\keyword{classif}