Package 'statMatchLCM' reference manual

Title:	Statistical Matching Using Latent Class Models
Description:	Tools for statistical matching based on latent class models. The package implements statistical matching procedures based on latent class models. It allows researchers to perform data integration when no unique identifiers are available by modeling the joint distribution of variables through latent categorical structures. The package supports estimation of latent class models, probabilistic matching between donor and recipient data sets, and generation of synthetic linked data under uncertainty. It is particularly useful in survey research and data fusion applications where combining information from multiple sources is required while preserving statistical properties and accounting for measurement error and missing data mechanisms.
Authors:	Alicja Wolny-Dominiak [aut, cre], Israa Lewaaelhamd [aut], Mohammed Ali Ismail [aut]
Maintainer:	Alicja Wolny-Dominiak <[email protected]>
License:	GPL-3
Version:	1.2
Built:	2026-05-15 22:13:01 UTC
Source:	https://github.com/cran/statMatchLCM

Dataset datA

Description

A simple dataset with categorical variables.

Usage

datA
datA

Format

A data frame with 20 observations and 2 variables:

X: Category (e.g., "F", "M")
Y1: Color category (e.g., "blue")

Source

Simulated data

Create stacked A/B dataset for MDC

Description

Creates a joint data set with missing Y1/Z1, required for mass data combination.

Usage

datAB_to_SM(datA, datB)
datAB_to_SM(datA, datB)

Arguments

datA

data.frame A

datB

data.frame B

Value

data.frame with harmonized structure

Examples

data(datA)
data(datB)
datAB_to_SM(datA, datB)
data(datA)
data(datB)
datAB_to_SM(datA, datB)

Dataset datB

Description

A simple dataset with categorical variables.

Usage

datB
datB

Format

A data frame with 15 observations and 2 variables:

X: Category (e.g., "F", "M")
Z1: Color category (e.g., "red", "green", "blue")

Source

Simulated data

Convert factor/character variables to numeric with mapping

Description

Converts all factor or character columns in a data.frame to numeric codes and stores the mapping tables.

Usage

fact_to_num(df)
fact_to_num(df)

Arguments

df

data.frame with factor or character columns

Value

A list with:

data: data.frame with numeric-coded variables
levels: list of factor levels
tables: list of mapping tables (factor → numeric)

Examples

data(datA)
fact_to_num(datA)
data(datA)
fact_to_num(datA)

Convert numeric codes back to factor

Description

Restores a factor variable from numeric codes using a mapping table created by fact_to_num().

Usage

num_to_fact(x, table)
num_to_fact(x, table)

Arguments

x

numeric vector

table

data.frame with columns level_fact and level_num

Value

A factor vector:

levels: Defined by table$level_num.
labels: Defined by table$level_fact.

Quality assessment of synthetic Z1

Description

Evaluates the quality of the synthetic target variable by computing the Hellinger distance between the reference and synthetic distributions. This measure quantifies the degree of similarity between the two distributions, providing an assessment of the accuracy and coherence of the data fusion process

Usage

sm_quality(step1, step2)
sm_quality(step1, step2)

Arguments

step1

output from smc_step1()

step2

output from step 2 method

Value

A list with:

heli_latent: A numeric value representing the Hellinger distance between the reference and synthetic distributions.
ref_distr: A numeric vector or table representing the reference (original) distribution.
synt_distr: A numeric vector or table representing the synthetic (generated) distribution.

SMA step 1: selection of best imputed dataset

Description

Selects the imputed dataset minimizing the Hellinger distance between the reference distribution from dataset A and the synthetic distribution from dataset B, in a three-sample statistical matching framework (A, B, C).

Usage

sma_step1(datA, datB, datC, output_ll)
sma_step1(datA, datB, datC, output_ll)

Arguments

datA

data.frame A

datB

data.frame B

datC

data.frame C

output_ll

list with imputed datasets (impdata)

Value

A list with imputed datasets:

datABC_imp1: The full imputed dataset combining A, B, and C.
datA_imp1: Subset of the imputed data corresponding to dataset A.
datB_imp1: Subset corresponding to dataset B.
datC_imp1: Subset corresponding to dataset C.

References

Lewaa, I., Hafez, M. S., and Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications and Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  # call DPMPM
  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC, nrun = 500, burn = 50, thin = 50,
    K = 80, aalpha = 0.25, balpha = 0.25,
    m = 2, seed = 1234, silent = FALSE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  names(step_first_aii)
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  # call DPMPM
  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC, nrun = 500, burn = 50, thin = 50,
    K = 80, aalpha = 0.25, balpha = 0.25,
    m = 2, seed = 1234, silent = FALSE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  names(step_first_aii)
}

SMA1 – Nearest-neighbour hot deck on shared variables

Description

Second step of the SMA procedure using nearest-neighbour hot deck matching on shared variables (Y1, Z1) to fuse datasets while preserving their joint distribution.

Usage

sma1_step2(step1)
sma1_step2(step1)

Arguments

step1

list returned by the SMA step 1 procedure

Value

A list with:

datA_fused_2: The final fused dataset A.
out_nndd: NNDD matching results.

References

Lewaa, I., Hafez, M. S., Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  # call DPMPM (reduced settings for speed)
  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  step_second_aii1 <- sma1_step2(step_first_aii)

  result_aii1 <- step_second_aii1$datA_fused_2
  head(result_aii1)
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  # call DPMPM (reduced settings for speed)
  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  step_second_aii1 <- sma1_step2(step_first_aii)

  result_aii1 <- step_second_aii1$datA_fused_2
  head(result_aii1)
}

SMA2 – Hot deck on fitted multinomial probabilities

Description

Second step of SMA using multinomial models and nearest-neighbour hot deck on fitted probabilities to improve data fusion accuracy.

Usage

sma2_step2(step1)
sma2_step2(step1)

Arguments

step1

list returned by the SMA step 1 procedure

Value

A list with:

datA_fused_2: A data frame containing the final fused (imputed) version of dataset A.
out_nnd: A list with results from the nearest-neighbour distance matching procedure.
model_form: A formula object specifying the model used.
FitteddatA_imp1: A data frame of fitted values obtained for dataset A.
FitteddatC_imp1: A data frame of fitted values obtained for dataset C.

References

Lewaa, I., Hafez, M. S., Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  step_second_aii2 <- sma2_step2(step_first_aii)

  result_aii2 <- step_second_aii2$datA_fused_2
  head(result_aii2)
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  step_second_aii2 <- sma2_step2(step_first_aii)

  result_aii2 <- step_second_aii2$datA_fused_2
  head(result_aii2)
}

SMA3 – Multinomial simulation approach

Description

Second step of SMA is to generate the target variable using multinomial probability distributions derived from a donor-based model to achieve statistically coherent data fusion

Usage

sma3_step2(step1)
sma3_step2(step1)

Arguments

step1

list returned by the SMA step 1 procedure

Value

A list with:

datA_fused_2: A data frame containing the final fused version of dataset A.
beta_BZ: A numeric vector of estimated model coefficients.
AA_dummy: A matrix or data frame of dummy variables constructed for dataset A.
BB_dummy: A matrix or data frame of dummy variables constructed for dataset B.
prob_Z_all: A numeric matrix of predicted probabilities for each category of variable Z (rows correspond to observations).
zero_one: A binary matrix (one-hot encoded) sampled from prob_Z_all, where each row contains a single 1 indicating the selected category of Z.

References

Lewaa, I., Hafez, M. S., Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  # call DPMPM (reduced settings for speed)
  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  step_second_aii3 <- sma1_step2(step_first_aii)

  result_aii3 <- step_second_aii3$datA_fused_2
  head(result_aii3)
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)
  datC <- data.frame(
    X = datB$X[1:4],
    Y1 = datA$Y1[1:4],
    Z1 = datB$Z1[1:4]
  )

  # adding auxiliary information
  datABC <- rbind(datAB, datC)

  # call DPMPM (reduced settings for speed)
  output_list_AII <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datABC,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first_aii <- sma_step1(datA, datB, datC, output_list_AII)
  step_second_aii3 <- sma1_step2(step_first_aii)

  result_aii3 <- step_second_aii3$datA_fused_2
  head(result_aii3)
}

SMC step 1: selection of best imputed dataset

Description

Identifies and selects the imputed dataset that minimizes the Hellinger distance between the reference and synthetic distributions, thereby achieving the highest level of distributional similarity and improving the statistical consistency of the imputation.

Usage

smc_step1(datA, datB, output_ll)
smc_step1(datA, datB, output_ll)

Arguments

datA

data.frame A

datB

data.frame B

output_ll

list with imputed datasets (impdata)

Value

A list with imputed datasets:

datAB_imp1: The full imputed dataset obtained after combining A and B.
datA_imp1: Subset of datAB_imp1 corresponding to dataset A.
datB_imp1: Subset of datAB_imp1 corresponding to dataset B.

References

Lewaa, I., Hafez, M. S., Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  str(step_first$datA_imp1)
  str(step_first$datB_imp1)
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  str(step_first$datA_imp1)
  str(step_first$datB_imp1)
}

SMC1 - Nearest-neighbour hot deck on original variables

Description

Performs nearest-neighbour hot deck imputation on the selected imputed datasets by matching recipient units in dataset A with donor units in dataset B based on common variables. The procedure transfers the target variable from the nearest donor to construct a statistically coherent fused dataset.

Usage

smc1_step2(step1)
smc1_step2(step1)

Arguments

step1

output from smc_step1()

Value

A list with:

datA_fused_2: The final fused dataset A after step 2 of the procedure.
out_nndd: A list containing nearest-neighbour distance matching results.

References

Lewaa, I., Hafez, M. S., and Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications and Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  step_second <- smc1_step2(step_first)

  result <- step_second$datA_fused_2
  result
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  step_second <- smc1_step2(step_first)

  result <- step_second$datA_fused_2
  result
}

SMC2 – Hot deck on fitted multinomial probabilities

Description

Implements a model-based hot deck data fusion approach by estimating multinomial models on both datasets and matching observations based on their fitted probability distributions

Usage

smc2_step2(step1)
smc2_step2(step1)

Arguments

step1

output from smc_step1()

Value

A list with:

datA_fused_2: A data frame containing the final fused (imputed) version of dataset A.
out_nndd: A list with results from the nearest-neighbour distance matching procedure.
model_form: A formula object used to fit the models.
FitteddatA_imp1: A data frame of fitted values obtained from the model for dataset A.
FitteddatB_imp1: A data frame of fitted values obtained from the model for dataset B.

References

Lewaa, I., Hafez, M. S., and Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications and Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  step_second <- smc2_step2(step_first)

  result <- step_second$datA_fused_2
  result
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  step_second <- smc2_step2(step_first)

  result <- step_second$datA_fused_2
  result
}

SMC3 – Multinomial simulation approach

Description

Generates the target variable using multinomial simulation based on fitted probability distributions to preserve uncertainty and variability

Usage

smc3_step2(step1)
smc3_step2(step1)

Arguments

step1

output from smc_step1()

Value

A list with:

datA_fused_2: A data frame containing the final fused (imputed) version of dataset A.
FitteddatA_imp1: A data frame of fitted values obtained from the model for dataset A.

References

Lewaa, I., Hafez, M. S., and Ismail, M. A. (2023). Mixed Statistical Matching Approaches Using a Latent Class Model: Simulation Studies. Journal of Statistics Applications and Probability, 12(1), 247–265.
D'Orazio, M., Di Zio, M., and Scanu, M. (2006). Statistical Matching: Theory and Practice. John Wiley and Sons.
D'Orazio, M., Di~Zio, M., and Scanu, M. (2019). Auxiliary variable selection in a statistical matching problem.
Zhang, L.-C., and Chambers, R. Analysis of integrated data. CRC/Chapman and Hall, pp. 101–120.
Conti, P. L., Marella, D., and Scanu, M. (2016). Statistical matching analysis for complex survey data with applications. Journal of the American Statistical Association, 111(516), 1715–1725.

Examples

if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  step_second <- smc3_step2(step_first)

  result <- step_second$datA_fused_2
  result
}
if (requireNamespace("NPBayesImputeCat", quietly = TRUE)) {
  data(datA)
  data(datB)

  datAB <- datAB_to_SM(datA, datB)

  # call DPMPM (reduced settings for speed)
  output_list <- NPBayesImputeCat::DPMPM_nozeros_imp(
    X = datAB,
    nrun = 50,
    burn = 10,
    thin = 10,
    K = 20,
    aalpha = 0.25,
    balpha = 0.25,
    m = 2,
    seed = 1234,
    silent = TRUE
  )

  step_first <- smc_step1(datA, datB, output_list)
  step_second <- smc3_step2(step_first)

  result <- step_second$datA_fused_2
  result
}

Package 'statMatchLCM'

Help Index

Dataset datA

Description

Usage

Format

Source

Create stacked A/B dataset for MDC

Description

Usage

Arguments

Value

Examples

Dataset datB

Description

Usage

Format

Source

Convert factor/character variables to numeric with mapping

Description

Usage

Arguments

Value

Examples

Convert numeric codes back to factor

Description

Usage

Arguments

Value

Quality assessment of synthetic Z1

Description

Usage

Arguments

Value

SMA step 1: selection of best imputed dataset

Description

Usage

Arguments

Value

References

Examples

SMA1 – Nearest-neighbour hot deck on shared variables

Description

Usage

Arguments

Value

References

Examples

SMA2 – Hot deck on fitted multinomial probabilities

Description

Usage

Arguments

Value

References

Examples

SMA3 – Multinomial simulation approach

Description

Usage

Arguments

Value

References

Examples

SMC step 1: selection of best imputed dataset

Description

Usage

Arguments

Value

References

Examples

SMC1 - Nearest-neighbour hot deck on original variables

Description

Usage

Arguments

Value

References

Examples

SMC2 – Hot deck on fitted multinomial probabilities

Description

Usage

Arguments