Type:	Package
Title:	Small Area Estimation Using Model-Assisted Projection Method
Version:	0.1.4
Description:	Combines information from two independent surveys using a model-assisted projection method. Designed for survey sampling scenarios where a large sample collects only auxiliary information (Survey 1) and a smaller sample provides data on both variables of interest and auxiliary variables (Survey 2). Implements a working model to generate synthetic values of the variable of interest by fitting the model to Survey 2 data and predicting values for Survey 1 based on its auxiliary variables (Kim & Rao, 2012) <doi:10.1093/biomet/asr063>.
License:	MIT + file LICENSE
Encoding:	UTF-8
LazyData:	true
URL:	https://github.com/Alfrzlp/sae.projection
BugReports:	https://github.com/Alfrzlp/sae.projection/issues
Imports:	FSelector, glmnet, xgboost, survey, cli, doParallel, dplyr, methods, parsnip, recipes, rlang, rsample, stats, tune, workflows, yardstick, bonsai, ranger, randomForest, themis, lightgbm, caret
RoxygenNote:	7.3.2
Depends:	R (≥ 4.3.0), tidymodels
NeedsCompilation:	no
Repository:	CRAN
Date/Publication:	2025-07-06 14:40:02 UTC
VignetteBuilder:	knitr
Suggests:	knitr, rmarkdown, quarto, testthat (≥ 3.0.0)
Config/testthat/edition:	3
Packaged:	2025-07-06 13:55:38 UTC; alfrz
Author:	Ridson Al Farizal P [aut, cre, cph], Azka Ubaidillah [aut], Silvi Ajeng Larasati [aut], Amelia Rahayu [aut]
Maintainer:	Ridson Al Farizal P <ridsonalfarizal15@gmail.com>

df_svy22

Description

A dataset from a survey conducted at the province level in Indonesia in 2022.

Usage

df_svy22

Format

A data frame with 74.070 rows and 11 variables.

PSU

Primary Sampling Unit

WEIGHT

Weight from survey

PROV

province code

REGENCY

regency/municipality code

STRATA

Strata

income

Income

neet

Not in education employment or training status

sex

sex (1: male, 2: female)

age

age

disability

disability status (0: False, 1: True)

edu

last completed education

df_svy23

Description

A dataset from a survey conducted at the province level in Indonesia in 2023.

Usage

df_svy23

Format

A data frame with 66.245 rows and 11 variables.

PSU

Primary Sampling Unit

WEIGHT

Weight from survey

PROV

province code

REGENCY

regency/municipality code

STRATA

Strata

income

Income

neet

Not in education employment or training status

sex

sex (1: male, 2: female)

age

age

disability

disability status (0: False, 1: True)

edu

last completed education

df_svy_A

Description

A simulation dataset from a small sample survey, presented only at provincial level (Domain 1).

Usage

df_svy_A

Format

A data frame with 2000 rows and 20 variables with 40 domains.

province

Province code

id_ind

Unique identifier for each respondent

num

Sample number

weight

Weight from survey

x1

Predictor variables X1

x2

Predictor variables X2

x3

Predictor variables X3

x4

Predictor variables X4

x5

Predictor variables X5

x6

Predictor variables X6

x7

Predictor variables X7

x8

Predictor variables X8

x9

Predictor variables X9

x10

Predictor variables X10

x11

Predictor variables X11

x12

Predictor variables X12

x13

Predictor variables X13

x14

Predictor variables X14

x15

Predictor variables X15

Y

Target variable (1: Yes, 0: No)

df_svy_B

Description

A simulation dataset from a large sample survey, presented at the regency level (Domain 2).

Usage

df_svy_B

Format

A data frame with 8000 rows and 20 variables with 40 domains.

province

Province code

regency

Regency code

id_ind

Unique identifier for each respondent

num

Sample number

weight

Weight from survey

x1

Predictor variables X1

x2

Predictor variables X2

x3

Predictor variables X3

x4

Predictor variables X4

x5

Predictor variables X5

x6

Predictor variables X6

x7

Predictor variables X7

x8

Predictor variables X8

x9

Predictor variables X9

x10

Predictor variables X10

x11

Predictor variables X11

x12

Predictor variables X12

x13

Predictor variables X13

x14

Predictor variables X14

x15

Predictor variables X15

Model-Assisted Projection Estimator

Description

The function addresses the problem of combining information from two or more independent surveys, a common challenge in survey sampling. It focuses on cases where:

• Survey 1: A large sample collects only auxiliary information.

• Survey 2: A much smaller sample collects both the variables of interest and the auxiliary variables.

The function implements a model-assisted projection estimation method based on a working model. The working models that can be used include several machine learning models that can be seen in the details section

Usage

ma_projection(
  formula,
  cluster_ids,
  weight,
  strata = NULL,
  domain,
  summary_function = "mean",
  working_model,
  data_model,
  data_proj,
  model_metric,
  cv_folds = 3,
  tuning_grid = 10,
  parallel_over = "resamples",
  seed = 1,
  return_yhat = FALSE,
  ...
)

Arguments

Details

The following working models are supported via the parsnip interface:

• linear_reg() – Linear regression

• logistic_reg() – Logistic regression

• linear_reg(engine = "stan") – Bayesian linear regression

• logistic_reg(engine = "stan") – Bayesian logistic regression

• poisson_reg() – Poisson regression

• decision_tree() – Decision tree

• nearest_neighbor() – k-Nearest Neighbors (k-NN)

• naive_bayes() – Naive Bayes classifier

• mlp() – Multi-layer perceptron (neural network)

• svm_linear() – Support vector machine with linear kernel

• svm_poly() – Support vector machine with polynomial kernel

• svm_rbf() – Support vector machine with radial basis function (RBF) kernel

• bag_tree() – Bagged decision tree

• bart() – Bayesian Additive Regression Trees (BART)

• rand_forest(engine = "ranger") – Random forest (via ranger)

• rand_forest(engine = "aorsf") – Accelerated oblique random forest (AORF; Jaeger et al. 2022, 2024)

• boost_tree(engine = "lightgbm") – Gradient boosting (LightGBM)

• boost_tree(engine = "xgboost") – Gradient boosting (XGBoost)

For a complete list of supported models and engines, see Tidy Modeling With R.

Value

A list containing:

• model – The fitted working model object.

• prediction – A vector of predictions from the working model.

• df_result – A data frame with:

  • domain – Domain identifier.

  • ypr – Projection estimator results for each domain.

  • var_ypr – Estimated variance of the projection estimator.

  • rse_ypr – Relative standard error (in \

References

1. Kim, J. K., & Rao, J. N. (2012). Combining data from two independent surveys: a model-assisted approach. Biometrika, 99(1), 85-100.

Examples

## Not run: 
library(sae.projection)
library(dplyr)
library(bonsai)

df_svy22_income <- df_svy22 %>% filter(!is.na(income))
df_svy23_income <- df_svy23 %>% filter(!is.na(income))

# Linear regression
lm_proj <- ma_projection(
  income ~ age + sex + edu + disability,
  cluster_ids = "PSU", weight = "WEIGHT", strata = "STRATA",
  domain = c("PROV", "REGENCY"),
  working_model = linear_reg(),
  data_model = df_svy22_income,
  data_proj = df_svy23_income,
  nest = TRUE
)


df_svy22_neet <- df_svy22 %>% filter(between(age, 15, 24))
df_svy23_neet <- df_svy23 %>% filter(between(age, 15, 24))


# LightGBM regression with hyperparameter tunning
show_engines("boost_tree")
lgbm_model <- boost_tree(
  mtry = tune(), trees = tune(), min_n = tune(),
  tree_depth = tune(), learn_rate = tune(),
  engine = "lightgbm"
)

lgbm_proj <- ma_projection(
  formula = neet ~ sex + edu + disability,
  cluster_ids = "PSU",
  weight = "WEIGHT",
  strata = "STRATA",
  domain = c("PROV", "REGENCY"),
  working_model = lgbm_model,
  data_model = df_svy22_neet,
  data_proj = df_svy23_neet,
  cv_folds = 3,
  tuning_grid = 3,
  nest = TRUE
)

## End(Not run)

Projection Estimator with Random Forest Algorithm

Description

Kim and Rao (2012), the synthetic data obtained through the model-assisted projection method can provide a useful tool for efficient domain estimation when the size of the sample in survey B is much larger than the size of sample in survey A.

The function projects estimated values from a small survey (survey A) onto an independent large survey (survey B) using the random forest classification algorithm. The two surveys are statistically independent, but the projection relies on shared auxiliary variables. The process includes data preprocessing, feature selection, model training, and domain-specific estimation based on survey design principles "two stages one phase". The function automatically selects standard estimation or bias-corrected estimation based on the parameter bias_correction.

bias_correction = TRUE can only be used if there is psu, ssu, strata on the data_model. If it doesn't, then it will automatically be bias_correction = FALSE

Usage

projection_randomforest(
  data_model,
  target_column,
  predictor_cols,
  data_proj,
  domain1,
  domain2,
  psu,
  ssu = NULL,
  strata = NULL,
  weights,
  split_ratio = 0.8,
  feature_selection = TRUE,
  bias_correction = FALSE
)

Arguments

Value

A list containing the following elements:

• model The trained Random Forest model.

• importance Feature importance showing which features contributed most to the model's predictions.

• train_accuracy Accuracy of the model on the training set.

• validation_accuracy Accuracy of the model on the validation set.

• validation_performance Confusion matrix for the validation set, showing performance metrics like accuracy, precision, recall, etc.

• data_proj The projection data with predicted values.

if bias_correction = FALSE:

• Domain1 Estimations for Domain 1, including estimated values, variance, and relative standard error (RSE).

• Domain2 Estimations for Domain 2, including estimated values, variance, and relative standard error (RSE).

if bias_correction = TRUE:

• Direct Direct estimations for Domain 1, including estimated values, variance, and relative standard error (RSE).

• Domain1_corrected_bias Bias-corrected estimations for Domain 1, including estimated values, variance, and relative standard error (RSE).

• Domain2_corrected_bias Bias-corrected estimations for Domain 2, including estimated values, variance, and relative standard error (RSE).

References

1. Kim, J. K., & Rao, J. N. (2012). Combining data from two independent surveys: a model-assisted approach. Biometrika, 99(1), 85-100.

Examples

library(survey)
library(caret)
library(dplyr)

data_A <- df_svy_A
data_B <- df_svy_B

# Get predictor variables from data_model
x_predictors <- data_A %>% select(5:19) %>% names()

# Run projection_randomforest with bias correction
rf_proj_corrected <- projection_randomforest(
                data_model = data_A,
                target_column = "Y",
                predictor_cols = x_predictors,
                data_proj = data_B,
                domain1 = "province",
                domain2 = "regency",
                psu = "num",
                ssu = NULL,
                strata = NULL,
                weights = "weight",
                feature_selection = TRUE,
                bias_correction = TRUE)

rf_proj_corrected$Direct
rf_proj_corrected$Domain1_corrected_bias
rf_proj_corrected$Domain2_corrected_bias

Projection RF Function

Description

This function trains a random forest model and performs domain-level estimation **without bias correction**.

Usage

projection_rf(
  data_model,
  target_column,
  predictor_cols,
  data_proj,
  domain1,
  domain2,
  psu,
  ssu = NULL,
  strata = NULL,
  weights,
  split_ratio = 0.8,
  feature_selection = TRUE
)

Arguments

Value

A list containing the following elements:

• model The trained Random Forest model.

• importance Feature importance showing which features contributed most to the model's predictions.

• train_accuracy Accuracy of the model on the training set.

• validation_accuracy Accuracy of the model on the validation set.

• validation_performance Confusion matrix for the validation set, showing performance metrics like accuracy, precision, recall, etc.

• data_proj The projection data with predicted values.

• Domain1 Estimations for Domain 1, including estimated values, variance, and relative standard error.

• Domain2 Estimations for Domain 2, including estimated values, variance, and relative standard error.

Projection RF with Correction Bias

Description

This function extends projection_rf by incorporating **bias correction** for better domain-level estimation.

Usage

projection_rf_CorrectedBias(
  data_model,
  target_column,
  predictor_cols,
  data_proj,
  domain1,
  domain2,
  psu,
  ssu = NULL,
  strata = NULL,
  weights,
  split_ratio = 0.8,
  feature_selection = TRUE
)

Arguments

Value

A list containing the following elements:

• model The trained Random Forest model.

• importance Feature importance showing which features contributed most to the model's predictions.

• train_accuracy Accuracy of the model on the training set.

• validation_accuracy Accuracy of the model on the validation set.

• validation_performance Confusion matrix for the validation set, showing performance metrics like accuracy, precision, recall, etc.

• data_proj The projection data with predicted values.

• Direct Direct estimations for Domain 1, including estimated values, variance, and relative standard error.

• Domain1_corrected_bias Bias-corrected estimations for Domain 1, including estimated values, variance, and relative standard error (RSE).

• Domain2_corrected_bias Bias-corrected estimations for Domain 2, including estimated values, variance, and relative standard error (RSE).

Projection Estimator with XGBoost Algorithm

Description

Kim and Rao (2012), proposed a model-assisted projection estimation method for two independent surveys, where the first survey (A1) has a large sample that only collects auxiliary variables, while the second survey (A1) has a smaller sample but contains information on both the focal variable and auxiliary variables. This method uses a Working Model (WM) to relate the focal variable to the auxiliary variable based on data from A2, and then predicts the value of the focal variable for A1. A projection estimator is then obtained from the (A2) sample using the resulting synthetic values. This approach produces estimators that are asymptotically unbiased and can improve the efficiency of domain estimation, especially when the sample size in survey 1 is much larger compared to survey 2.

This function applies the XGBoost algorithm to project estimated values from a small survey onto an independent larger survey. While the two surveys are statistically independent, the projection is based on common auxiliary variables. The process in this function involves data preprocessing, feature selection, getting the best model with hyperparameter tuning, and performing domain-specific estimation following survey design principles.

Usage

projection_xgboost(
  target_col,
  data_model,
  data_proj,
  id,
  STRATA = NULL,
  domain1,
  domain2,
  weight,
  task_type,
  test_size = 0.2,
  nfold = 5,
  corrected_bias = FALSE,
  feature_selection = TRUE
)

Arguments

Value

A list containing the following components:

metadata

A list of metadata about the modeling process, including:

• method: Description of the method used (e.g., "Projection Estimator With XGBoost Algorithm"),

• model_type: The type of model, either "classification" or "regression",

• feature_selection_used: Logical, whether feature selection was used,

• corrected_bias_applied: Logical, whether bias correction was applied,

• n_features_used: Number of predictor variables used,

• model_params: The hyperparameters and settings of the final XGBoost model,

• features_selected (optional): Names of features selected, if feature selection was applied.

estimation

A list of projection estimation results, including:

• projected_data: The dataset used for projection (e.g., kabupaten/kota) with predicted values,

• domain1_estimation: Estimated values for domain 1 (e.g., province level), including:

  • Estimation, RSE, var

  ,

• domain2_estimation: Estimated values for domain 2 (e.g., regency level), including:

  • Estimation, RSE, var

performance

(Only if applicable) A list of model performance metrics:

• mean_train_accuracy, final_accuracy, confusion_matrix (for classification),

• mean_train_rmse, final_rmse (for regression).

bias_correction

(Optional) A list of bias correction results, returned only if corrected_bias = TRUE, including:

• direct_estimation: Direct estimation before correction,

• corrected_domain1: Bias-corrected estimates for domain 1,

• corrected_domain2: Bias-corrected estimates for domain 2.

References

1. Kim, J. K., & Rao, J. N. (2012). Combining data from two independent surveys: a model-assisted approach. Biometrika, 99(1), 85-100.

2. Kim and Rao (2012), the synthetic data obtained through the model-assisted projection method can provide a useful tool for efficient domain estimation when the size of the sample in survey 1 is much larger than the size of sample in survey 2.

Examples

library(xgboost)
library(caret)
library(FSelector)
library(glmnet)
library(recipes)

Data_A <- df_svy_A
Data_B <- df_svy_B

hasil <- projection_xgboost(
                            target_col = "Y",
                            data_model = Data_A,
                            data_proj = Data_B,
                            id = "num",
                            STRATA = NULL,
                            domain1 = "province",
                            domain2 = "regency",
                            weight = "weight",
                            nfold = 3,
                            test_size = 0.2 ,
                            task_type = "classification",
                            corrected_bias = TRUE,
                            feature_selection = TRUE)