automatedRecLin (version 1.0.1)

predict.rec_lin_model: Predict Matches Based on a Given Record Linkage Model

Description

Predicts matches between records in two datasets based on a given record linkage model, using the maximum entropy classification (MEC) algorithm (see Lee et al. (2022)).

Usage

# S3 method for rec_lin_model
predict(
  object,
  newdata_A,
  newdata_B,
  duplicates_in_A = FALSE,
  set_construction = c("size", "flr", "mmr"),
  fixed_method = "Newton",
  target_rate = 0.03,
  tol = 0.005,
  max_iter = 50,
  data_type = c("data.frame", "data.table", "matrix"),
  true_matches = NULL,
  ...
)

Value

Returns a list containing:

• M_est -- a data.table with predicted matches,

• set_construction -- a method for constructing the predicted set of matches,

• n_M_est -- estimated classification set size,

• flr_est -- estimated false link rate (FLR),

• mmr_est -- estimated missing match rate (MMR),

• iter -- the number of iterations in the bisection procedure,

• eval_metrics -- standard metrics for quality assessment, if true_matches is provided,

• confusion -- confusion matrix, if true_matches is provided.

Arguments

object

A rec_lin_model object from the train_rec_lin or custom_rec_lin_model functions.

newdata_A

A duplicate-free data.frame or data.table.

newdata_B

A duplicate-free data.frame or data.table.

duplicates_in_A

Logical indicating whether to allow A to have duplicate records.

set_construction

A method for constructing the predicted set of matches ("size", "flr" or "mmr").

fixed_method

A method for solving fixed-point equations using the FixedPoint function.

target_rate

A target false link rate (FLR) or missing match rate (MMR) (used only if set_construction == "flr" or set_construction == "mmr").

tol

Error tolerance in the bisection procedure (used only if set_construction == "flr" or set_construction == "mmr").

max_iter

A maximum number of iterations for the bisection procedure (used only if set_construction == "flr" or set_construction == "mmr").

data_type

Data type for predictions with a custom ML model ("data.frame", "data.table" or "matrix"; used only if object is from the custom_rec_lin_model function).

true_matches

A data.frame or data.table indicating true matches.

...

Additional controls passed to the predict function for custom ML model (used only if the object is from the custom_rec_lin_model function).

Author

Adam Struzik

Details

The predict function estimates the probability/density ratio between matches and non-matches for pairs in given datasets, based on a model obtained using the train_rec_lin or custom_rec_lin_model functions. Then, it estimates the number of matches and returns the predicted matches, using the maximum entropy classification (MEC) algorithm (see Lee et al. (2022)).

The predict function allows the construction of the predicted set of matches using its estimated size or the bisection procedure, described in Lee et al. (2022), based on a target False Link Rate (FLR) or missing match rate (MMR). To use the second option, set set_construction = "flr" or set_construction = "mmr" and specify a target error rate using the target_rate argument.

By default, the function assumes that the datasets newdata_A and newdata_B contain no duplicate records. This assumption might be relaxed by allowing newdata_A to have duplicates. To do so, set duplicates_in_A = TRUE.

References

Lee, D., Zhang, L.-C. and Kim, J. K. (2022). Maximum entropy classification for record linkage. Survey Methodology, Statistics Canada, Catalogue No. 12-001-X, Vol. 48, No. 1.

Vo, T. H., Chauvet, G., Happe, A., Oger, E., Paquelet, S., and Garès, V. (2023). Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system. Computational Statistics & Data Analysis, 179, 107656.

Sugiyama, M., Suzuki, T., Nakajima, S. et al. Direct importance estimation for covariate shift adaptation. Ann Inst Stat Math 60, 699–746 (2008). tools:::Rd_expr_doi("10.1007/s10463-008-0197-x")

Examples

df_1 <- data.frame(
  "name" = c("James", "Emma", "William", "Olivia", "Thomas",
  "Sophie", "Harry", "Amelia", "George", "Isabella"),
  "surname" = c("Smith", "Johnson", "Brown", "Taylor", "Wilson",
  "Davis", "Clark", "Harris", "Lewis", "Walker")
)
 df_2 <- data.frame(
  "name" = c("James", "Ema", "Wimliam", "Olivia", "Charlotte",
  "Henry", "Lucy", "Edward", "Alice", "Jack"),
  "surname" = c("Smith", "Johnson", "Bron", "Tailor", "Moore",
  "Evans", "Hall", "Wright", "Green", "King")
)
comparators <- list("name" = jarowinkler_complement(),
                    "surname" = jarowinkler_complement())
matches <- data.frame("a" = 1:4, "b" = 1:4)
methods <- list("name" = "continuous_nonparametric",
                "surname" = "continuous_nonparametric")
model <- train_rec_lin(A = df_1, B = df_2, matches = matches,
                       variables = c("name", "surname"),
                       comparators = comparators,
                       methods = methods)

df_new_1 <- data.frame(
  "name" = c("John", "Emily", "Mark", "Anna", "David"),
  "surname" = c("Smith", "Johnson", "Taylor", "Williams", "Brown")
)
df_new_2 <- data.frame(
  "name" = c("John", "Emely", "Mark", "Michael"),
  "surname" = c("Smitth", "Johnson", "Tailor", "Henders")
)
predict(model, df_new_1, df_new_2)