Package 'corlink'

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 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,
  fix_p = FALSE,
  fixed_col = NULL,
  alg = "m",
  missingvals = TRUE
)

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
fix_p	logical Is overall proportion of record pairs to be updated on each repetition?
fixed_col	vector indicating columns that where u probabilities are not updated in initial EM algorithm. Useful if good prior estimates of the mis-match probabilities. See details
alg	character; see Details
missingvals	logical Are any record pairs missing on particular fields. If FALSE, initial EM algorithm to impute missingness doesn't need to be run.

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
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

---