emWeights.
Based on user-defined thresholds or predefined error rates.emClassify(rpairs, threshold.upper = Inf,
threshold.lower = threshold.upper, my = Inf, ny = Inf, ...)
## S3 method for class 'RecLinkData,ANY,ANY':
emClassify(rpairs, threshold.upper = Inf,
threshold.lower = threshold.upper, my = Inf, ny = Inf)
## S3 method for class 'RLBigData,ANY,ANY':
emClassify(rpairs, threshold.upper = Inf,
threshold.lower = threshold.upper, my = Inf, ny = Inf,
withProgressBar = (sink.number()==0))RecLinkData object with weight information."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 ".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
$w\geq \textit{threshold.upper}$, as a possible link if
$\textit{threshold.upper}\geq w\geq \textit{threshold.lower}$ and as a non-link if $w<\textit{threshold.lower}$. if="" 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.getPairs to produce output from which thresholds can
be determined conveniently.