Author: Ridson Al Farizal P (ridsonap@bps.go.id)
Date: 2026-02-04

# sae.projection: A Model-Assisted Projection Estimator for Combining Independent Surveys

#### Ridson Al Farizal P (ridsonap@bps.go.id)

#### 2026-02-04

## Introduction

The `ma_projection()` function implements a model-assisted projection estimator for combining information from two independent surveys. This method is especially useful in survey sampling scenarios where:

- Survey 1 contains a large sample with only auxiliary variables.
- Survey 2 contains a smaller sample with both the outcome and auxiliary variables.

This vignette illustrates how to use `ma_projection()` for domain-level estimation using various supervised learning models, including machine learning techniques via the `parsnip` interface.

## Method Overview

The approach follows the work of Kim & Rao (2012), where a working model is trained on Survey 2 to predict the outcome variable. Predictions are made for the auxiliary-only Survey 1 data. These predictions are then aggregated by domain to generate small area estimates.

## Required Packages

    library(sae.projection)
    library(dplyr)
    library(tidymodels)
    library(bonsai)  # for modern tree-based models

## Example: Income Estimation Using Linear Regression

    # Filter non-missing values for income
    svy22_income <- df_svy22 %>% filter(!is.na(income))
    svy23_income <- df_svy23 %>% filter(!is.na(income))

    # Fit projection model
    lm_result <- ma_projection(
      income ~ age + sex + edu + disability,
      cluster_ids = "PSU",
      weight = "WEIGHT",
      strata = "STRATA",
      domain = c("PROV", "REGENCY"),
      working_model = linear_reg(),
      data_model = svy22_income,
      data_proj = svy23_income,
      nest = TRUE
    )

    # View results
    head(lm_result$df_result)

## Example: Binary Outcome Using Logistic Regression

    # Filter youth population for NEET classification
    svy22_neet <- df_svy22 %>% filter(between(age, 15, 24))
    svy23_neet <- df_svy23 %>% filter(between(age, 15, 24))

    # Fit logistic regression model
    lr_result <- ma_projection(
      formula = neet ~ sex + edu + disability,
      cluster_ids = ~ PSU,
      weight = ~ WEIGHT,
      strata = ~ STRATA,
      domain = ~ PROV + REGENCY,
      working_model = logistic_reg(),
      data_model = svy22_neet,
      data_proj = svy23_neet,
      nest = TRUE
    )

    # View results
    head(lr_result$df_result)

## Example: LightGBM with Hyperparameter Tuning

    # Define LightGBM model with tuning
    lgbm_model <- boost_tree(
      mtry = tune(), trees = tune(), min_n = tune(),
      tree_depth = tune(), learn_rate = tune(),
      engine = "lightgbm"
    )

    # Fit with cross-validation
    lgbm_result <- ma_projection(
      formula = neet ~ sex + edu + disability,
      cluster_ids = "PSU",
      weight = "WEIGHT",
      strata = "STRATA",
      domain = c("PROV", "REGENCY"),
      working_model = lgbm_model,
      data_model = svy22_neet,
      data_proj = svy23_neet,
      cv_folds = 3,
      tuning_grid = 5,
      nest = TRUE
    )

    # View results
    head(lgbm_result$df_result)

## Supported Models

`ma_projection()` supports many working models using the `parsnip` interface, including:

- `linear_reg()`, `logistic_reg()` (also with Stan engine)
- `poisson_reg()`, `mlp()`, `naive_bayes()`, `nearest_neighbor()`
- Tree-based: `decision_tree()`, `bag_tree()`, `boost_tree()` with LightGBM/XGBoost, `rand_forest()` (ranger, aorsf), `bart()`
- SVM: `svm_linear()`, `svm_poly()`, `svm_rbf()`

## References

Kim, J. K., & Rao, J. N. (2012). Combining data from two independent surveys: a model-assisted approach. Biometrika, 99(1), 85–100. doi:10.1093/biomet/asr063

## Conclusion

`ma_projection()` provides a flexible and robust way to combine survey data using modern modeling tools. It supports a wide range of use cases including socioeconomic indicators, health estimates, and more.