select_greedy.cluster_pairs {reclin2}

Select matching pairs enforcing one-to-one linkage

Description

Select matching pairs enforcing one-to-one linkage

Usage

## S3 method for class 'cluster_pairs'
select_greedy(
  pairs,
  variable,
  score,
  threshold,
  preselect = NULL,
  id_x = NULL,
  id_y = NULL,
  ...
)

## S3 method for class 'cluster_pairs'
select_n_to_m(
  pairs,
  variable,
  score,
  threshold,
  preselect = NULL,
  id_x = NULL,
  id_y = NULL,
  ...
)

select_greedy(
  pairs,
  variable,
  score,
  threshold,
  preselect = NULL,
  id_x = NULL,
  id_y = NULL,
  ...
)

## S3 method for class 'pairs'
select_greedy(
  pairs,
  variable,
  score,
  threshold,
  preselect = NULL,
  id_x = NULL,
  id_y = NULL,
  x = attr(pairs, "x"),
  y = attr(pairs, "y"),
  inplace = FALSE,
  include_ties = FALSE,
  n = 1L,
  m = 1L,
  ...
)

select_n_to_m(
  pairs,
  variable,
  score,
  threshold,
  preselect = NULL,
  id_x = NULL,
  id_y = NULL,
  ...
)

## S3 method for class 'pairs'
select_n_to_m(
  pairs,
  variable,
  score,
  threshold,
  preselect = NULL,
  id_x = NULL,
  id_y = NULL,
  x = attr(pairs, "x"),
  y = attr(pairs, "y"),
  inplace = FALSE,
  ...
)

Arguments

pairs

a pairs object, such as generated by pair_blocking

variable

the name of the new variable to create in pairs. This will be a logical variable with a value of TRUE for the selected pairs.

score

name of the score/weight variable of the pairs. When not given and attr(pairs, "score") is defined, that is used.

threshold

the threshold to apply. Pairs with a score above the threshold are selected.

preselect

a logical variable with the same length as pairs has rows, or the name of such a variable in pairs. Pairs are only selected when preselect is TRUE. This interacts with threshold (pairs have to be selected with both conditions).

id_x

a integer vector with the same length as the number of rows in pairs, or the name of a column in x. This vector should identify unique objects in x. When not specified it is assumed that each element in x is unique.

id_y

a integer vector with the same length as the number of rows in pairs, or the name of a column in y. This vector should identify unique objects in y. When not specified it is assumed that each element in y is unique.

...

Used to pass additional arguments to methods

x

data.table with one half of the pairs.

y

data.table with the other half of the pairs.

inplace

logical indicating whether pairs should be modified in place. When pairs is large this can be more efficient.

include_ties

when pairs for a given record have an equal weight, should all pairs be included.

n

an integer. Each element of x can be linked to at most n elements of y.

m

an integer. Each element of y can be linked to at most m elements of x.

Details

Both methods force one-to-one matching. select_greedy uses a greedy algorithm that selects the first pair with the highest weight. select_n_to_m tries to optimise the total weight of all of the selected pairs. In general this will result in a better selection. However, select_n_to_m uses much more memory and is much slower and, therefore, can only be used when the number of possible pairs is not too large.

Note that when include_ties = TRUE the same record can still be selected more than once. In that case the pairs will have an equal weight.

Value

Returns the pairs with the variable given by variable added. This is a logical variable indicating which pairs are selected as matches.

Examples

data("linkexample1", "linkexample2")
pairs <- pair_blocking(linkexample1, linkexample2, "postcode")
pairs <- compare_pairs(pairs, c("lastname", "firstname", "address", "sex"))
model <- problink_em(~ lastname + firstname + address + sex, data = pairs)
pairs <- predict(model, pairs, type = "mpost", add = TRUE, binary = TRUE)

# Select pairs with a mpost > 0.5 and force one-to-one linkage
pairs <- select_n_to_m(pairs, "ntom", "mpost", 0.5)
pairs <- select_greedy(pairs, "greedy", "mpost", 0.5)
table(pairs$ntom, pairs$greedy)

# The same example as above using a cluster;
library(parallel)
cl <- makeCluster(2)

pairs <- cluster_pair_blocking(cl, linkexample1, linkexample2, "postcode")
compare_pairs(pairs, c("lastname", "firstname", "address", "sex"))
model <- problink_em(~ lastname + firstname + address + sex, data = pairs)
predict(model, pairs, type = "mpost", add = TRUE, binary = TRUE)
# Select pairs with a mpost > 0.5 and force one-to-one linkage
# select_n_to_m and select_greedy only work on pairs that are local; 
# therefore we first collect the pairs
select_threshold(pairs, "selected", "mpost", 0.5)
local_pairs <- cluster_collect(pairs, "selected")
local_pairs <- select_n_to_m(local_pairs, "ntom", "mpost", 0.5)
local_pairs <- select_greedy(local_pairs, "greedy", "mpost", 0.5)
table(local_pairs$ntom, local_pairs$greedy)

stopCluster(cl)