| Title: | Multifile Record Linkage and Duplicate Detection |
|---|---|
| Description: | Implementation of the methodology of Aleshin-Guendel & Sadinle (2022) <doi:10.1080/01621459.2021.2013242>. It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates. |
| Authors: | Serge Aleshin-Guendel [aut, cre] |
| Maintainer: | Serge Aleshin-Guendel <[email protected]> |
| License: | GPL-3 |
| Version: | 0.1.1 |
| Built: | 2024-11-23 06:44:28 UTC |
| Source: | CRAN |
Create comparison data for all pairs of records, except for those records in files which are assumed to have no duplicates.
create_comparison_data( records, types, breaks, file_sizes, duplicates, verbose = TRUE )create_comparison_data( records, types, breaks, file_sizes, duplicates, verbose = TRUE )
records |
A |
types |
A |
breaks |
A |
file_sizes |
A |
duplicates |
A |
verbose |
A |
The purpose of this function is to construct comparison vectors for each pair
of records. In order to construct these vectors, one needs to specify the
types and breaks arguments. The types argument specifies
how each field should be compared, and the breaks argument specifies
how to discretize these comparisons.
Currently, the types argument supports three types of field
comparisons: binary, absolute difference, and the normalized Levenshtein
distance. Please contact the package maintainer if you need a new type of
comparison to be supported.
The breaks argument should be a list, with with one element for
each field. If a field is being compared with a binary comparison, i.e.
types[f]="bi", then the corresponding element of breaks should
be NA, i.e. breaks[[f]]=NA. If a field is being compared with a
numeric or string comparison, then the corresponding element of breaks
should be a vector of cut points used to discretize the comparisons. To give
more detail, suppose you pass in cut points
breaks[[f]]=c(cut_1, ...,cut_L). These cut points
discretize the range of the comparisons into L+1 intervals:
. The
raw comparisons, which lie in for numeric comparisons and
for string comparisons, are then replaced with indicators of
which interval the comparisons lie in. The interval corresponds to
the lowest level of disagreement for a comparison, while the interval
corresponds to the highest level of disagreement for a comparison.
a list containing:
record_pairsA data.frame, where each row
contains the pair of records being compared in the corresponding row of
comparisons. The rows are sorted in ascending order according to the
first column, with ties broken according to the second column in ascending
order. For any given row, the first column is less than the second column,
i.e. record_pairs[i, 1] < record_pairs[i, 2] for each row i.
comparisonsA logical matrix, where each row contains
the comparisons for the record pair in the corresponding row of
record_pairs. Comparisons are in the same order as the columns of
records, and are represented by L + 1 columns of
TRUE/FALSE indicators, where L + 1 is the number of
disagreement levels for the field based on breaks.
KThe number of files, assumed to be of class
numeric.
file_sizesA numeric vector of length K,
indicating the size of each file.
duplicatesA numeric vector of length K,
indicating which files are assumed to have duplicates. duplicates[k]
should be 1 if file k has duplicates, and
duplicates[k] should be 0 if file k has no duplicates.
If any files do not have duplicates, we strongly recommend that the largest
such file is organized to be the first file.
field_levelsA numeric vector indicating the number of
disagreement levels for each field.
file_labelsAn integer vector of length
sum(file_sizes), where file_labels[i] indicates which file
record i is in.
fp_matrixAn integer matrix, where
fp_matrix[k1, k2] is a label for the file pair (k1, k2). Note
that fp_matrix[k1, k2] = fp_matrix[k2, k1].
rp_to_fpA logical matrix that indicates which record
pairs belong to which file pairs. rp_to_fp[fp, rp] is TRUE if
the records record_pairs[rp, ] belong to the file pair fp,
and is FALSE otherwise. Note that fp is given by the labeling in
fp_matrix.
abAn integer vector, of length
ncol(comparisons) * K * (K + 1) / 2 that indicates how many record
pairs there are with a given disagreement level for a given field, for each
file pair.
file_sizes_not_includedA numeric vector of 0s.
This element is non-zero when reduce_comparison_data is
used.
ab_not_includedA numeric vector of 0s. This
element is non-zero when reduce_comparison_data is used.
labelsNA. This element is not NA when
reduce_comparison_data is used.
pairs_to_keepNA. This element is not NA when
reduce_comparison_data is used.
cc0. This element is non-zero when
reduce_comparison_data is used.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
## Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) ## Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1))## Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) ## Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1))
A dataset containing 867 simulated records from 3 files with
no duplicate records in each file.
dup_datadup_data
A list with three elements:
A data.frame with the records, containing 7
fields, from all three files, in the format used for input to
create_comparison_data.
The size of each file.
The true partition of the records, represented as an
integer vector of arbitrary labels of length
sum(file_sizes).
Extracted from the datasets used in the simulation study of the paper. The datasets were generated using code from Peter Christen's group https://dmm.anu.edu.au/geco/index.php.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
data(dup_data) # There are 500 entities represented in the records length(unique(dup_data$IDs))data(dup_data) # There are 500 entities represented in the records length(unique(dup_data$IDs))
A dataset containing 96 simulated records from 3 files with
no duplicate records in each file, subset from dup_data.
dup_data_smalldup_data_small
A list with three elements:
A data.frame with the records, containing 7
fields, from all three files, in the format used for input to
create_comparison_data.
The size of each file.
The true partition of the records, represented as an
integer vector of arbitrary labels of length
sum(file_sizes).
Extracted from the datasets used in the simulation study of the paper. The datasets were generated using code from Peter Christen's group https://dmm.anu.edu.au/geco/index.php.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
data(dup_data_small) # There are 96 entities represented in the records length(unique(dup_data_small$IDs))data(dup_data_small) # There are 96 entities represented in the records length(unique(dup_data_small$IDs))
Find the (approximate) Bayes estimate of a partition based on MCMC samples of the partition and a specified loss function.
find_bayes_estimate( partitions, burn_in, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = nrow(partitions), verbose = TRUE )find_bayes_estimate( partitions, burn_in, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = nrow(partitions), verbose = TRUE )
partitions |
Posterior samples of the partition, where each column
is one sample and the partition is represented as an |
burn_in |
The number of samples to discard for burn in. |
L_FNM |
Positive loss for a false non-match. Default is |
L_FM1 |
Positive loss for a type 1 false match. Default is |
L_FM2 |
Positive loss for a type 2 false match. Default is |
L_A |
Positive loss for abstaining from making a decision for a record.
Default is |
max_cc_size |
The maximum allowable connected component size over which
the posterior expected loss is minimized. Default is |
verbose |
A |
A vector, the same length of a column of partitions containing the
(approximate) Bayes estimate of the partition. If !is.infinite(L_A)
the output may be a partial estimate. A positive number l in index
i indicates that record i is in the same cluster as every other
record j with l in index j. A value of -1 in
index i indicates that the Bayes estimate abstained from making a
decision for record i.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Find initialization for the matching (this step is optional) # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42) # Run the Gibbs sampler results <- gibbs_sampler(comparison_list, prior_list, n_iter = 1000, Z_init = Z_init, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12)# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Find initialization for the matching (this step is optional) # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42) # Run the Gibbs sampler results <- gibbs_sampler(comparison_list, prior_list, n_iter = 1000, Z_init = Z_init, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12)
Run a Gibbs sampler to explore the posterior distribution of partitions of records.
gibbs_sampler( comparison_list, prior_list, n_iter = 2000, Z_init = 1:sum(comparison_list$file_sizes), seed = 70, single_likelihood = FALSE, chaperones_info = NA, verbose = TRUE )gibbs_sampler( comparison_list, prior_list, n_iter = 2000, Z_init = 1:sum(comparison_list$file_sizes), seed = 70, single_likelihood = FALSE, chaperones_info = NA, verbose = TRUE )
comparison_list |
The output from a call to
|
prior_list |
The output from a call to |
n_iter |
The number of iterations of the Gibbs sampler to run. |
Z_init |
Initialization of the partition of records, represented as an
|
seed |
The seed to use while running the Gibbs sampler. |
single_likelihood |
A |
chaperones_info |
If |
verbose |
A |
Given the prior specified using specify_prior, this function
runs a Gibbs sampler to explore the posterior distribution of partitions of
records, conditional on the comparison data created using
create_comparison_data or reduce_comparison_data.
a list containing:
mPosterior samples of the m parameters. Each column
is one sample.
uPosterior samples of the u parameters. Each column
is one sample.
partitionsPosterior samples of the partition. Each column
is one sample. Note that the partition is represented as an integer
vector of arbitrary labels of length
sum(comparison_list$file_sizes).
contingency_tablesPosterior samples of the overlap table.
Each column is one sample. This incorporates counts of records determined
not to be candidate matches to any other records using
reduce_comparison_data.
cluster_sizesPosterior samples of the size of each cluster
(associated with an arbitrary label from 1 to
sum(comparison_list$file_sizes)). Each column is one sample.
sampling_timeThe time in seconds it took to run the sampler.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
Jeffrey Miller, Brenda Betancourt, Abbas Zaidi, Hanna Wallach, & Rebecca C. Steorts (2015). Microclustering: When the cluster sizes grow sublinearly with the size of the data set. NeurIPS Bayesian Nonparametrics: The Next Generation Workshop Series. [arXiv]
Brenda Betancourt, Giacomo Zanella, Jeffrey Miller, Hanna Wallach, Abbas Zaidi, & Rebecca C. Steorts (2016). Flexible Models for Microclustering with Application to Entity Resolution. Advances in neural information processing systems. [Published] [arXiv]
# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Find initialization for the matching (this step is optional) # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42) # Run the Gibbs sampler { results <- gibbs_sampler(comparison_list, prior_list, n_iter = 1000, Z_init = Z_init, seed = 42) } # Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler { results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) }# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Find initialization for the matching (this step is optional) # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42) # Run the Gibbs sampler { results <- gibbs_sampler(comparison_list, prior_list, n_iter = 1000, Z_init = Z_init, seed = 42) } # Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler { results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) }
Generate an initialization for the partition in the case when it is assumed there are no duplicates in all files (so that the partition is a matching).
initialize_partition(comparison_list, pairs_to_keep, seed = NA)initialize_partition(comparison_list, pairs_to_keep, seed = NA)
comparison_list |
the output from a call to
|
pairs_to_keep |
A |
seed |
The seed to use to generate the initialization. |
When it is assumed that there are no duplicates in all files, and
reduce_comparison_data is not used to reduce the number of
potential matches, the Gibbs sampler used for posterior inference may
experience slow mixing when using an initialization for the partition where
each record is in its own cluster (the default option for the Gibbs sampler).
The purpose of this function is to provide an alternative initialization
scheme.
To use this initialization scheme, the user passes in a logical vector
that indicates which record pairs are potential matches according to an
indexing method (as in reduce_comparison_data). Note that this
indexing is only used to generate the initialization, it is not used for
inference. The initialization scheme first finds the transitive closure of
the potential matches, which partitions the records into blocks. Within each
block of records, the scheme randomly selects a record from each file, and
these selected records are then placed in the same cluster for the partition
initialization. All other records are placed in their own clusters.
an integer vector of arbitrary labels of length
sum(comparison_list$file_sizes), giving an initialization for the
partition.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Find initialization for the matching # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42)# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Find initialization for the matching # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42)
The multilink package implements the methodology of Aleshin-Guendel & Sadinle (2022). It handles the general problem of multifile record linkage and duplicate detection, where any number of files are to be linked, and any of the files may have duplicates.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242] [arXiv]
# Here we demonstrate an example workflow with the small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Find initialization for the matching (this step is optional) # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42) # Run the Gibbs sampler results <- gibbs_sampler(comparison_list, prior_list, n_iter = 1000, Z_init = Z_init, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # The number of clusters in the full estimate length(unique(full_estimate)) # The number of entities represented in the records length(unique(no_dup_data_small$IDs)) # Find which record pairs are truly coreferent based on IDs true_links <- no_dup_data_small$IDs[comparison_list$record_pairs[, 1]] == no_dup_data_small$IDs[comparison_list$record_pairs[, 2]] # Find which record pairs are in the same clusters in the full estimate full_estimate_links <- full_estimate[comparison_list$record_pairs[, 1]] == full_estimate[comparison_list$record_pairs[, 2]] # Find the number of true matches in the full estimate true_matches <- sum(full_estimate_links & true_links) # Precision of the full estimate true_matches / sum(full_estimate_links) # Recall of the full estimate true_matches / sum(true_links) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # The partial estimate abstains from making decisions for how many records? sum(partial_estimate == -1) # For the records which decisions were made for in the partial estimate, # there are how many clusters? length(unique(partial_estimate)) # Abstain rate of partial_estimate sum(partial_estimate == -1) / length(partial_estimate) # Relabel records where we abstained partial_estimate[which(partial_estimate == -1)] <- length(partial_estimate) + which(partial_estimate == -1) # Find which record pairs are in the same clusters in the full estimate partial_estimate_links <- partial_estimate[comparison_list$record_pairs[, 1]] == partial_estimate[comparison_list$record_pairs[, 2]] # Find the number of true matches in the partial estimate true_matches_A <- sum(partial_estimate_links & true_links) # Precision of the partial estimate true_matches_A / sum(partial_estimate_links) # Here we demonstrate an example workflow with the small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # The number of clusters in the full estimate (including records records # determined not to be candidate matches to any other records using # reduce_comparison_data) length(unique(full_estimate)) + sum(reduced_comparison_list$file_sizes_not_included) # The number of entities represented in the records length(unique(dup_data_small$IDs)) # Find which record pairs are truly coreferent based on IDs true_links <- dup_data_small$IDs[comparison_list$record_pairs[, 1]] == dup_data_small$IDs[comparison_list$record_pairs[, 2]] # Focus on the record pairs that were candidate matches true_links_reduced <- true_links[reduced_comparison_list$pairs_to_keep] # Calculate the number of prior false non-matches based on the indexing # scheme used prior_fnm <- nrow(comparison_list$record_pairs[true_links & (!reduced_comparison_list$pairs_to_keep), ]) # Find which record pairs are in the same clusters in the full estimate full_estimate_links <- full_estimate[reduced_comparison_list$record_pairs[, 1]] == full_estimate[reduced_comparison_list$record_pairs[, 2]] # Find the number of true matches in the full estimate true_matches <- sum(full_estimate_links & true_links_reduced) # Precision of the full estimate true_matches / sum(full_estimate_links) # Recall of the full estimate true_matches / (sum(true_links_reduced) + prior_fnm) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # The partial estimate abstains from making decisions for how many records? sum(partial_estimate == -1) # For the records which decisions were made for in the partial estimate, # there are how many clusters? (including records determined not to be # candidate matches to any other records using reduce_comparison_data) length(unique(partial_estimate)) + sum(reduced_comparison_list$file_sizes_not_included) # Abstain rate of partial_estimat (excluding records determined not # to be candidate matches to any other records using reduce_comparison_data) sum(partial_estimate == -1) / length(partial_estimate) # Relabel records where we abstained partial_estimate[which(partial_estimate == -1)] <- length(partial_estimate) + which(partial_estimate == -1) # Find which record pairs are in the same clusters in the full estimate partial_estimate_links <- partial_estimate[reduced_comparison_list$record_pairs[, 1]] == partial_estimate[reduced_comparison_list$record_pairs[, 2]] # Find the number of true matches in the partial estimate true_matches_A <- sum(partial_estimate_links & true_links_reduced) # Precision of the partial estimate true_matches_A / sum(partial_estimate_links) # Relabel the full and partial Bayes estimates full_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, full_estimate) partial_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, partial_estimate) # Add columns to the records corresponding to their full and partial # Bayes estimates dup_data_small$records <- cbind(dup_data_small$records, full_estimate_id = full_estimate_relabel$link_id, partial_estimate_id = partial_estimate_relabel$link_id)# Here we demonstrate an example workflow with the small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Find initialization for the matching (this step is optional) # The following line corresponds to only keeping pairs of records as # potential matches in the initialization for which neither gname nor fname # disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) Z_init <- initialize_partition(comparison_list, pairs_to_keep, seed = 42) # Run the Gibbs sampler results <- gibbs_sampler(comparison_list, prior_list, n_iter = 1000, Z_init = Z_init, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # The number of clusters in the full estimate length(unique(full_estimate)) # The number of entities represented in the records length(unique(no_dup_data_small$IDs)) # Find which record pairs are truly coreferent based on IDs true_links <- no_dup_data_small$IDs[comparison_list$record_pairs[, 1]] == no_dup_data_small$IDs[comparison_list$record_pairs[, 2]] # Find which record pairs are in the same clusters in the full estimate full_estimate_links <- full_estimate[comparison_list$record_pairs[, 1]] == full_estimate[comparison_list$record_pairs[, 2]] # Find the number of true matches in the full estimate true_matches <- sum(full_estimate_links & true_links) # Precision of the full estimate true_matches / sum(full_estimate_links) # Recall of the full estimate true_matches / sum(true_links) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # The partial estimate abstains from making decisions for how many records? sum(partial_estimate == -1) # For the records which decisions were made for in the partial estimate, # there are how many clusters? length(unique(partial_estimate)) # Abstain rate of partial_estimate sum(partial_estimate == -1) / length(partial_estimate) # Relabel records where we abstained partial_estimate[which(partial_estimate == -1)] <- length(partial_estimate) + which(partial_estimate == -1) # Find which record pairs are in the same clusters in the full estimate partial_estimate_links <- partial_estimate[comparison_list$record_pairs[, 1]] == partial_estimate[comparison_list$record_pairs[, 2]] # Find the number of true matches in the partial estimate true_matches_A <- sum(partial_estimate_links & true_links) # Precision of the partial estimate true_matches_A / sum(partial_estimate_links) # Here we demonstrate an example workflow with the small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # The number of clusters in the full estimate (including records records # determined not to be candidate matches to any other records using # reduce_comparison_data) length(unique(full_estimate)) + sum(reduced_comparison_list$file_sizes_not_included) # The number of entities represented in the records length(unique(dup_data_small$IDs)) # Find which record pairs are truly coreferent based on IDs true_links <- dup_data_small$IDs[comparison_list$record_pairs[, 1]] == dup_data_small$IDs[comparison_list$record_pairs[, 2]] # Focus on the record pairs that were candidate matches true_links_reduced <- true_links[reduced_comparison_list$pairs_to_keep] # Calculate the number of prior false non-matches based on the indexing # scheme used prior_fnm <- nrow(comparison_list$record_pairs[true_links & (!reduced_comparison_list$pairs_to_keep), ]) # Find which record pairs are in the same clusters in the full estimate full_estimate_links <- full_estimate[reduced_comparison_list$record_pairs[, 1]] == full_estimate[reduced_comparison_list$record_pairs[, 2]] # Find the number of true matches in the full estimate true_matches <- sum(full_estimate_links & true_links_reduced) # Precision of the full estimate true_matches / sum(full_estimate_links) # Recall of the full estimate true_matches / (sum(true_links_reduced) + prior_fnm) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # The partial estimate abstains from making decisions for how many records? sum(partial_estimate == -1) # For the records which decisions were made for in the partial estimate, # there are how many clusters? (including records determined not to be # candidate matches to any other records using reduce_comparison_data) length(unique(partial_estimate)) + sum(reduced_comparison_list$file_sizes_not_included) # Abstain rate of partial_estimat (excluding records determined not # to be candidate matches to any other records using reduce_comparison_data) sum(partial_estimate == -1) / length(partial_estimate) # Relabel records where we abstained partial_estimate[which(partial_estimate == -1)] <- length(partial_estimate) + which(partial_estimate == -1) # Find which record pairs are in the same clusters in the full estimate partial_estimate_links <- partial_estimate[reduced_comparison_list$record_pairs[, 1]] == partial_estimate[reduced_comparison_list$record_pairs[, 2]] # Find the number of true matches in the partial estimate true_matches_A <- sum(partial_estimate_links & true_links_reduced) # Precision of the partial estimate true_matches_A / sum(partial_estimate_links) # Relabel the full and partial Bayes estimates full_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, full_estimate) partial_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, partial_estimate) # Add columns to the records corresponding to their full and partial # Bayes estimates dup_data_small$records <- cbind(dup_data_small$records, full_estimate_id = full_estimate_relabel$link_id, partial_estimate_id = partial_estimate_relabel$link_id)
A dataset containing 730 simulated records from 3 files with
no duplicate records in each file.
no_dup_datano_dup_data
A list with three elements:
A data.frame with the records, containing 7
fields, from all three files, in the format used for input to
create_comparison_data.
The size of each file.
The true partition of the records, represented as an
integer vector of arbitrary labels of length
sum(file_sizes).
Extracted from the datasets used in the simulation study of the paper. The datasets were generated using code from Peter Christen's group https://dmm.anu.edu.au/geco/index.php.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242] [arXiv]
data(no_dup_data) # There are 500 entities represented in the records length(unique(no_dup_data$IDs))data(no_dup_data) # There are 500 entities represented in the records length(unique(no_dup_data$IDs))
A dataset containing 71 simulated records from 3 files with
no duplicate records in each file, subset from no_dup_data.
no_dup_data_smallno_dup_data_small
A list with three elements:
A data.frame with the records, containing 7
fields, from all three files, in the format used for input to
create_comparison_data.
The size of each file.
The true partition of the records, represented as an
integer vector of arbitrary labels of length
sum(file_sizes).
Extracted from the datasets used in the simulation study of the paper. The datasets were generated using code from Peter Christen's group https://dmm.anu.edu.au/geco/index.php.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242] [arXiv]
data(no_dup_data_small) # There are 71 entities represented in the records length(unique(no_dup_data_small$IDs))data(no_dup_data_small) # There are 71 entities represented in the records length(unique(no_dup_data_small$IDs))
Use indexing to reduce the number of record pairs that are potential matches.
reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1)reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1)
comparison_list |
The output of a call to
|
pairs_to_keep |
A |
cc |
A |
When using comparison-based record linkage methods, scalability is a concern,
as the number of record pairs is quadratic in the number of records. In
order to address these concerns, it's common to declare certain record pairs
to not be potential matches a priori, using indexing methods. The user is
free to index using any method they like, as long as they can produce a
logical vector that indicates which record pairs are potential matches
according to their indexing method. We recommend, if the user chosen indexing
method does not output potential matches that are transitive, to set the
cc argument to 1. By transitive we mean, for any three records
, , and , if and are potential matches,
and and are potential matches, then and are
potential matches. Non-transitive indexing schemes can lead to poor mixing of
the Gibbs sampler used for posterior inference, and suggests that the
indexing method used may have been too stringent.
If indexing is used, it may be the case that some records are declared to not
be potential matches to any other records. In this case, the indexing method
has made the decision that these records have no matches, and thus we can
remove them from the data set and relabel the remaining records; see the
documentation for labels for information on how to go between the
original labeling and the new labeling.
If indexing is used, comparisons for record pairs that aren't potential matches are still used during inference, where they're used to inform the distribution of comparisons for non-matches.
a list containing:
record_pairsA data.frame, where each row
contains the pair of records being compared in the corresponding row of
comparisons. The rows are sorted in ascending order according to the
first column, with ties broken according to the second column in ascending
order. For any given row, the first column is less than the second column,
i.e. record_pairs[i, 1] < record_pairs[i, 2] for each row i.
If according to pairs_to_keep there are records which are not
potential matches to any other records, the remaining records are
relabeled (see labels).
comparisonsA logical matrix, where each row contains
the comparisons between the record pair in the corresponding row of
record_pairs. Comparisons are in the same order as the columns of
records, and are represented by L + 1 columns of
TRUE/FALSE indicators, where L + 1 is the number of
disagreement levels for the field based on breaks.
KThe number of files, assumed to be of class
numeric.
file_sizesA numeric vector of length K,
indicating the size of each file. If according to pairs_to_keep
there are records which are not potential matches to any other records, the
remaining records are relabeled (see labels), and file_sizes
now represents the sizes of each file after removing such records.
duplicatesA numeric vector of length K,
indicating which files are assumed to have duplicates. duplicates[k]
should be 1 if file k has duplicates, and
duplicates[k] should be 0 if file k has no
duplicates.
field_levelsA numeric vector indicating the number of
disagreement levels for each field.
file_labelsAn integer vector of length
sum(file_sizes), where file_labels[i] indicated which file
record i is in.
fp_matrixAn integer matrix, where
fp_matrix[k1, k2] is a label for the file pair (k1, k2). Note
that fp_matrix[k1, k2] = fp_matrix[k2, k1].
rp_to_fpA logical matrix that indicates which record
pairs belong to which file pairs. rp_to_fp[fp, rp] is TRUE if
the records record_pairs[rp, ] belong to the file pair fp,
and is FALSE otherwise. Note that fp is given by the labeling in
fp_matrix.
abAn integer vector, of length
ncol(comparisons) * K * (K + 1) / 2 that indicates how many record
pairs there are with a given disagreement level for a given field, for each
file pair.
file_sizes_not_includedIf according to pairs_to_keep
there are records which are not potential matches to any other records, the
remaining records are relabeled (see labels), and
file_sizes_not_included indicates, for each file, the number of such
records that were removed.
ab_not_includedFor record pairs not included according to
pairs_to_keep, this is an integer vector, of length
ncol(comparisons) * K * (K + 1) / 2 that indicates how many record
pairs there are with a given disagreement level for a given field, for each
file pair.
labelsIf according to pairs_to_keep
there are records which are not potential matches to any other records, the
remaining records are relabeled. labels provides a dictionary that
indicates, for each of the new labels, which record in the original
labeling the new label corresponds to. In particular, the first column
indicates the record in the original labeling, and the second column
indicates the new labeling.
pairs_to_keepA logical vector, the same length as
comparison_list$record_pairs, indicating which record pairs were
kept as potential matches. This may not be the same as the input
pairs_to_keep if cc was set to 1.
ccA numeric indicator of whether the connected
components of the potential matches are closed under transitivity.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
# Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1)# Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1)
Relabel the Bayes estimate of a partition, for use after using indexing to reduce the number of record pairs that are potential matches.
relabel_bayes_estimate(reduced_comparison_list, bayes_estimate)relabel_bayes_estimate(reduced_comparison_list, bayes_estimate)
reduced_comparison_list |
The output from a call to
|
bayes_estimate |
The output from a call to
|
When the function reduce_comparison_data is used to reduce the
number of record pairs that are potential matches, it may be the case that
some records are declared to not be potential matches to any other records.
In this case, the indexing method has made the decision that these records
have no matches, and thus we can remove them from the data set and relabel
the remaining records; see the documentation for labels in
reduce_comparison_data for information on how to go between the
original labeling and the new labeling. The purpose of this function is to
relabel the output of find_bayes_estimate when the function
reduce_comparison_data is used, so that the user doesn't have
to do this relabeling themselves.
A data.frame, with as many rows as
sum(reduced_comparison_list$file_sizes +
reduced_comparison_list$file_sizes_not_included), i.e. the number of
records originally input to create_comparison_data, before
indexing occurred. This data.frame has two columns,
"original_labels" and "link_id". Given row i of
records originally input to create_comparison_data,
the linkage id according to bayes_estimate is given by the ith
row of the link_id column. See the documentation for
find_bayes_estimate for information on how to interpret this
linkage id.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242][arXiv]
# Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler { results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # Relabel the full and partial Bayes estimates full_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, full_estimate) partial_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, partial_estimate) # Add columns to the records corresponding to their full and partial # Bayes estimates dup_data_small$records <- cbind(dup_data_small$records, full_estimate_id = full_estimate_relabel$link_id, partial_estimate_id = partial_estimate_relabel$link_id) }# Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA) # Run the Gibbs sampler { results <- gibbs_sampler(reduced_comparison_list, prior_list, n_iter = 1000, seed = 42) # Find the full Bayes estimate full_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = Inf, max_cc_size = 50) # Find the partial Bayes estimate partial_estimate <- find_bayes_estimate(results$partitions, burn_in = 100, L_FNM = 1, L_FM1 = 1, L_FM2 = 2, L_A = 0.1, max_cc_size = 12) # Relabel the full and partial Bayes estimates full_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, full_estimate) partial_estimate_relabel <- relabel_bayes_estimate(reduced_comparison_list, partial_estimate) # Add columns to the records corresponding to their full and partial # Bayes estimates dup_data_small$records <- cbind(dup_data_small$records, full_estimate_id = full_estimate_relabel$link_id, partial_estimate_id = partial_estimate_relabel$link_id) }
Specify the prior distributions for the and parameters of the
models for comparison data among matches and non-matches, and the partition.
specify_prior( comparison_list, mus = NA, nus = NA, flat = 0, alphas = NA, dup_upper_bound = NA, dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = NA, n_prior_pars = NA )specify_prior( comparison_list, mus = NA, nus = NA, flat = 0, alphas = NA, dup_upper_bound = NA, dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = NA, n_prior_pars = NA )
comparison_list |
the output from a call to
|
mus, nus
|
The hyperparameters of the Dirichlet priors for the |
flat |
A |
alphas |
The hyperparameters for the Dirichlet-multinomial overlap table
prior, a positive |
dup_upper_bound |
A |
dup_count_prior_family |
A |
dup_count_prior_pars |
A |
n_prior_family |
A |
n_prior_pars |
Currently set to |
The purpose of this function is to specify prior distributions for all
parameters of the model. Please note that if
reduce_comparison_data is used to the reduce the number of
record pairs that are potential matches, then the output of
reduce_comparison_data (not
create_comparison_data) should be used as input.
For the hyperparameters of the Dirichlet priors for the
and parameters for the comparisons among matches and non-matches,
respectively, we recommend using a flat prior. This is accomplished by
setting mus=NA and nus=NA. Informative prior specifications
are possible, but in practice they will be overwhelmed by the large number of
comparisons.
For the prior for partitions, we do not recommend using a flat prior. Instead
we recommend using our structure prior for partitions. By setting
flat=0 and the remaining arguments to NA, one obtains the
default specification for the structured prior that we have found to perform
well in simulation studies. The structured prior for partitions is specified
as follows:
Specify a prior for n, the number of clusters represented in
the records. Note that this includes records determined not to be potential
matches to any other records using reduce_comparison_data.
Currently, a uniform prior and a scale prior for n are supported.
Our default specification uses a uniform prior.
Specify a prior for the overlap table (see the documentation for
alphas for more information). Currently a Dirichlet-multinomial
prior is supported. Our default specification sets all hyperparameters of
the Dirichlet-multinomial prior to 1.
For each file, specify a prior for the number of duplicates in each
cluster. As a part of this prior, we specify the maximum number of records
in a cluster for each file, through dup_upper_bound. When there
are assumed to be no duplicates in a file, the maximum number of records in
a cluster for that file is set to 1. When there are assumed to be
duplicates in a file, we recommend setting the maximum number of records in
a cluster for that file to be less than the file size, if prior knowledge
allows. Currently, a Poisson prior for the the number of duplicates in
each cluster is supported. Our default specification uses a Poisson prior
with mean 1.
Please contact the package maintainer if you need new prior families
for n or the number of duplicates in each cluster to be supported.
a list containing:
musThe hyperparameters of the Dirichlet priors for the
m parameters for the comparisons among matches.
nusThe hyperparameters of the Dirichlet priors for the
u parameters for the comparisons among non-matches. Includes data
from comparisons of record pairs that were declared to not be potential
matches using reduce_comparison_data.
flatA numeric indicator of whether a flat prior for
partitions should be used. flat is 1 if a flat prior is used,
and flat is 0 if a structured prior is used.
no_dupsA numeric indicator of whether no duplicates
are allowed in all of the files.
alphasThe hyperparameters for the Dirichlet-multinomial
overlap table prior, a positive numeric vector of length
2 ^ comparison_list$K, where the first element is 0.
alpha_0The sum of alphas.
dup_upper_boundA numeric vector indicating the
maximum number of duplicates, from each file, allowed in each cluster. For
a given file k, dup_upper_bound[k] should be between 1
and comparison_list$file_sizes[k], i.e. even if you don't want to
impose an upper bound, you have to implicitly place an upper bound: the
number of records in a file.
log_dup_count_priorA list containing the log density
of the prior distribution for the number of duplicates in each cluster, for
each file.
log_n_priorA numeric vector containing the log
density of the prior distribution for the number of clusters represented in
the records.
nus_specifiedThe nus before data from comparisons of
record pairs that were declared to not be potential matches using
reduce_comparison_data are added. Used for input checking.
Serge Aleshin-Guendel & Mauricio Sadinle (2022). Multifile Partitioning for Record Linkage and Duplicate Detection. Journal of the American Statistical Association. [doi:10.1080/01621459.2021.2013242] [arXiv]
# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA)# Example with small no duplicate dataset data(no_dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(no_dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = no_dup_data_small$file_sizes, duplicates = c(0, 0, 0)) # Specify the prior prior_list <- specify_prior(comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(1, 1, 1), dup_count_prior_family = NA, dup_count_prior_pars = NA, n_prior_family = "uniform", n_prior_pars = NA) # Example with small duplicate dataset data(dup_data_small) # Create the comparison data comparison_list <- create_comparison_data(dup_data_small$records, types = c("bi", "lv", "lv", "lv", "lv", "bi", "bi"), breaks = list(NA, c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), c(0, 0.25, 0.5), NA, NA), file_sizes = dup_data_small$file_sizes, duplicates = c(1, 1, 1)) # Reduce the comparison data # The following line corresponds to only keeping pairs of records for which # neither gname nor fname disagree at the highest level pairs_to_keep <- (comparison_list$comparisons[, "gname_DL_3"] != TRUE) & (comparison_list$comparisons[, "fname_DL_3"] != TRUE) reduced_comparison_list <- reduce_comparison_data(comparison_list, pairs_to_keep, cc = 1) # Specify the prior prior_list <- specify_prior(reduced_comparison_list, mus = NA, nus = NA, flat = 0, alphas = rep(1, 7), dup_upper_bound = c(10, 10, 10), dup_count_prior_family = c("Poisson", "Poisson", "Poisson"), dup_count_prior_pars = list(c(1), c(1), c(1)), n_prior_family = "uniform", n_prior_pars = NA)