From BRL v0.1.0
0th

Percentile

##### Bayes Estimates of Bipartite Matchings

Bayes point estimates of bipartite matchings that can be obtained in closed form according to Theorems 1, 2 and 3 of Sadinle (2017).

##### Usage
linkRecords(Zchain, n1, lFNM = 1, lFM1 = 1, lFM2 = 2, lR = Inf)
Zchain

matrix as the output $Z of the function bipartiteGibbs, with n2 rows and nIter columns containing a chain of draws from a posterior distribution on bipartite matchings. Each column indicates the records in datafile 1 to which the records in datafile 2 are matched according to that draw. n1 number of records in datafile 1. lFNM individual loss of a false non-match in the loss functions of Sadinle (2017), default lFNM=1. lFM1 individual loss of a false match of type 1 in the loss functions of Sadinle (2017), default lFM1=1. lFM2 individual loss of a false match of type 2 in the loss functions of Sadinle (2017), default lFM2=2. lR individual loss of 'rejecting' to make a decision in the loss functions of Sadinle (2017), default lR=Inf. ##### Details Not all combinations of losses lFNM, lFM1, lFM2, lR are supported. The losses have to be positive numbers and satisfy one of three conditions: 1. Conditions of Theorem 1 of Sadinle (2017): (lR == Inf) & (lFNM <= lFM1) & (lFNM + lFM1 <= lFM2) 2. Conditions of Theorem 2 of Sadinle (2017): ((lFM2 >= lFM1) & (lFM1 >= 2*lR)) | ((lFM1 >= lFNM) & (lFM2 >= lFM1 + lFNM)) 3. Conditions of Theorem 3 of Sadinle (2017): (lFM2 >= lFM1) & (lFM1 >= 2*lR) & (lFNM >= 2*lR) If one of the last two conditions is satisfied, the point estimate might be partial, meaning that there might be some records in datafile 2 for which the point estimate does not include a linkage decision. For combinations of losses not supported here, the linear sum assignment problem outlined by Sadinle (2017) needs to be solved. ##### Value A vector containing the point estimate of the bipartite matching. If lR != Inf the output might be a partial estimate. A number smaller or equal to n1 in entry j indicates the record in datafile 1 to which record j in datafile 2 gets linked, a number n1+j indicates that record j does not get linked to any record in datafile 1, and the value -1 indicates a 'rejection' to link, meaning that the correct linkage decision is not clear. ##### References Mauricio Sadinle (2017). Bayesian Estimation of Bipartite Matchings for Record Linkage. Journal of the American Statistical Association 112(518), 600-612. [Published] [arXiv] ##### Aliases • linkRecords ##### Examples # NOT RUN { data(twoFiles) myCompData <- compareRecords(df1, df2, flds=c("gname", "fname", "age", "occup"), types=c("lv","lv","bi","bi")) chain <- bipartiteGibbs(myCompData) ## discard first 100 iterations of Gibbs sampler ## full estimate of bipartite matching (full linkage) fullZhat <- linkRecords(chain$Z[,-c(1:100)], n1=nrow(df1), lFNM=1, lFM1=1, lFM2=2, lR=Inf)

## partial estimate of bipartite matching (partial linkage), where
## lR=0.5, lFNM=1, lFM1=1 mean that we consider not making a decision for
## a record as being half as bad as a false non-match or a false match of type 1
partialZhat <- linkRecords(chain$Z[,-c(1:100)], n1=nrow(df1), lFNM=1, lFM1=1, lFM2=2, lR=.5) ## for which records the decision is not clear according to this set-up of the losses? undecided <- which(partialZhat == -1) df2[undecided,] ## compute frequencies of link options observed in the chain linkOptions <- apply(chain$Z[undecided, -c(1:100)], 1, table)