diyar (version 0.5.1)

predefined_tests: Predefined logical tests in diyar

Description

A collection of predefined logical tests used with sub_criteria objects

Usage

exact_match(x, y)
range_match(x, y, range = 10)
prob_link(
  x,
  y,
  cmp_func,
  attr_threshold,
  score_threshold,
  probabilistic,
  return_weights = FALSE
)
true(x, y)
false(x, y)

Arguments

x

Attribute(s) to be compared against.

y

Attribute(s) to be compared by.

range

Difference between y and x.

cmp_func

Logical tests such as string comparators. See links_wf_probabilistic.

attr_threshold

Matching set of weight thresholds for each result of cmp_func. See links_wf_probabilistic.

score_threshold

Score threshold determining matched or linked records. See links_wf_probabilistic.

probabilistic

If TRUE, matches determined through a score derived base on Fellegi-Sunter model for probabilistic linkage. See links_wf_probabilistic.

return_weights

If TRUE, returns the match-weights and score-thresholds for record pairs.

Details

exact_match() - test that x == y

range_match() - test that x \(\le\) y \(\le\) (x + range)

prob_link() - Test that a record-pair relate to the same entity based on Fellegi and Sunter (1969) model for deciding if two records belong to the same entity.

In summary, record-pairs are created and categorised as matches and non-matches (attr_threshold) with user-defined functions (cmp_func). If probabilistic is TRUE, two probabilities (m and u) are used to calculate weights for matches and non-matches. The m-probability is the probability that matched records are actually from the same entity i.e. a true match, while u-probability is the probability that matched records are not from the same entity i.e. a false match. Record-pairs whose total score are above a certain threshold (score_threshold) are assumed to belong to the same entity.

Agreement (match) and disagreement (non-match) scores are calculated as described by Asher et al. (2020).

For each record pair, an agreement for attribute \(i\) is calculated as;

$$\log_{2}(m_{i}/u_{i})$$

For each record pair, a disagreement score for attribute \(i\) is calculated as;

$$\log_{2}((1-m_{i})/(1-u_{i}))$$

where \(m_{i}\) and \(u_{i}\) are the m and u-probabilities for each value of attribute \(i\).

Note that each probability is calculated as a combined probability for the record pair. For example, if the values of the record-pair have u-probabilities of 0.1 and 0.2 respectively, then the u-probability for the pair will be 0.02.

Missing data (NA) are considered non-matches and assigned a u-probability of 0.

Examples

`exact_match`
exact_match(x = 1, y = 1)
exact_match(x = 1, y = 2)

`range_match`
range_match(x = 10, y = 16, range = 6)
range_match(x = 16, y = 10, range = 6)