| Title: | Record Linkage, Incorporating Imputation for Missing Agreement Patterns, and Modeling Correlation Patterns Between Fields |
| Version: | 1.0.0 |
| Description: | A matrix of agreement patterns and counts for record pairs is the input for the procedure. An EM algorithm is used to impute plausible values for missing record pairs. A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilities that each pair is a true match - i.e. constitutes the same individual. |
| Depends: | R (≥ 3.2.4) |
| License: | CC0 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 5.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2016-10-20 08:57:58 UTC; 0118158S |
| Author: | John Ferguson [aut, cre] |
| Maintainer: | John Ferguson <john.ferguson@nuigalway.ie> |
| Repository: | CRAN |
| Date/Publication: | 2016-10-20 23:17:42 |
corlink: Record linkage, Incorporating Imputation for Missing Agreement Patterns, and Modeling correlation patterns between fields
Description
A matrix of agreement patterns and counts for record pairs is the input for the procedure. An EM algorithm is used to impute plausible values for missing record pairs. A second EM algorithm, incorporating possible correlations between per-field agreement, is used to estimate posterior probabilites that each pair is a true match - i.e. constitutes the same individual.
corlink functions
linkd
Function to simulate agreement patterns for record pairs from a mixture model
Description
Function to simulate agreement patterns for record pairs from a mixture model
Usage
do_sim(cor_match = 0.1, cor_mismatch = 0, nsample = 10^3,
pi_match = 0.01, m_probs, u_probs, missingprobs)
Arguments
cor_match |
correlation in 0/1 agreement fields for record pairs constituting the same individual (matches). |
cor_mismatch |
correlation in 0/1 agreement fields for record pairs not constituting the same individual (true mismatches). |
nsample |
number of record pairs to simulate. |
pi_match |
the probability both records from a randomly selected record pair correspond to the same individual (a true match). |
m_probs |
marginal probabilities of agreement for each field for record pairs constituting true matches. |
u_probs |
marginal probabilities of agreement for each field for record pairs constituting true mismatches. |
missingprobs |
probabilities that each field is missing on at least one record pair. |
Value
A matrix of the simulated agreement patterns, with final column equal to the count of each pattern.
Examples
m_probs <- rep(0.8,6)
u_probs <- rep(0.2,6)
means_match <- -1*qnorm(1-m_probs)
means_mismatch <- -1*qnorm(1-u_probs)
missingprobs <- rep(.2,6)
thedata <- do_sim(cor_match=0.2,cor_mismatch=0,nsample=10^4,
pi_match=.5,m_probs=rep(0.8,5),u_probs=rep(0.2,5),missingprobs=rep(0.4,5))
thedata
Function to impute missing agreement patterns and then to link data
Description
Function to impute missing agreement patterns and then to link data
Usage
linkd(d, initial_m = NULL, initial_u = NULL, p_init = 0.5,
fixed_col = NULL, alg = "m")
Arguments
d |
Matrix of agreement patterns with final column counting the number of times that pattern was observed. See Details |
initial_m |
starting probabilities for per-field agreement in record pairs, both records being generated from the same individual. Defaults to NULL |
initial_u |
starting probabilities for per-field agreement in record pairs, with the two records being generated from differing individuals Defaults to NULL |
p_init |
starting probability that both records for a randomly selected record pair is associated with the same individual |
fixed_col |
vector indicating columns that are not to be updated in initial EM algorithm. Useful if good prior estimates of the mis-match probabilities. See details |
alg |
character; see Details |
Details
d is a numeric matrix with N rows corresponding to N record pairs, and L+1 columns the first L of which show the field agreement patterns observed over the record pairs, and the last column the total number of times that pattern was observed in the database. The code 0 is used for a field that differs for two record, 1 for a field that agrees, and 2 for a missing field. fixed_col indicates the components of the u vector (per field probabilities of agreement for 2 records from differing individuals) that are not to be updated when applying the EM algorithm to estimate components of the Feligi Sunter model. alg has four possible values. The default 'm' fits a log-linear model for the agreement counts only within the record pairs that corresponds to the same individual, 'b' fits differing log-linear models for the 2 clusters, 'i' corresponds to the original Feligi Sunter algorithm, with probabilities estimated via the EM algorithm, 'a' fits all the previously listed models
Value
A list, the first component is a matrix - the posterior probabilities of being a true match is the last column, the second component are the fitted models used to generate the predicted probabilities
Examples
# Simulate data
m_probs <- rep(0.8,6)
u_probs <- rep(0.2,6)
means_match <- -1*qnorm(1-m_probs)
means_mismatch <- -1*qnorm(1-u_probs)
missingprobs <- rep(.2,6)
thedata <- do_sim(cor_match=0.2,cor_mismatch=0,nsample=10^4,pi_match=.5,
m_probs=rep(0.8,5),u_probs=rep(0.2,5),missingprobs=rep(0.4,5))
colnames(thedata) <- c(paste("V",1:5,sep="_"),"count")
output <- linkd(thedata)
output$fitted_probs