Package 'sae.projection'

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

---

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

---

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

formula	A model formula. All variables used must exist in both data_model and data_proj.
cluster_ids	Column name (character) or formula specifying cluster identifiers from highest to lowest level. Use ~0 or ~1 if there are no clusters.
weight	Column name in data_proj representing the survey weights.
strata	Column name for stratification; use NULL if no strata are used.
domain	Character vector specifying domain variable names in both datasets.
summary_function	A function to compute domain-level estimates (default: "mean", "total", "variance").
working_model	A parsnip model object specifying the working model (see ⁠@details⁠).
data_model	Data frame (small sample) containing both target and auxiliary variables.
data_proj	Data frame (large sample) containing only auxiliary variables.
model_metric	A yardstick::metric_set() function, or NULL to use default metrics.
cv_folds	Number of folds for k-fold cross-validation.
tuning_grid	Either a data frame with tuning parameters or a positive integer specifying the number of grid search candidates.
parallel_over	Specifies parallelization mode: "resamples", "everything", or NULL. If "resamples", then tuning will be performed in parallel over resamples alone. Within each resample, the preprocessor (i.e. recipe or formula) is processed once, and is then reused across all models that need to be fit. If "everything", then tuning will be performed in parallel at two levels. An outer parallel loop will iterate over resamples. Additionally, an inner parallel loop will iterate over all unique combinations of preprocessor and model tuning parameters for that specific resample. This will result in the preprocessor being re-processed multiple times, but can be faster if that processing is extremely fast.
seed	Integer seed for reproducibility.
return_yhat	Logical; if TRUE, returns predicted y values for data_model.
...	Additional arguments passed to svydesign.

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

data_model	The training dataset, consisting of auxiliary variables and the target variable.
target_column	The name of the target column in the data_model.
predictor_cols	A vector of predictor column names.
data_proj	The data for projection (prediction), which needs to be projected using the trained model. It must contain the same auxiliary variables as the data_model
domain1	Domain variables for survey estimation (e.g., "province")
domain2	Domain variables for survey estimation (e.g., "regency")
psu	Primary sampling units, representing the structure of the sampling frame.
ssu	Secondary sampling units, representing the structure of the sampling frame (default is NULL).
strata	Stratification variable, ensuring that specific subgroups are represented (default is NULL).
weights	Weights used for the direct estimation from data_model and indirect estimation from data_proj.
split_ratio	Proportion of data used for training (default is 0.8, meaning 80 percent for training and 20 percent for validation).
feature_selection	Selection of predictor variables (default is TRUE)
bias_correction	Logical; if TRUE, then bias correction is applied, if FALSE, then bias correction is not applied. Default is FALSE.

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 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

target_col	The name of the column that contains the target variable in the data_model.
data_model	A data frame or a data frame extension (e.g., a tibble) representing the training dataset, which consists of auxiliary variables and the target variable. This dataset is characterized by a smaller sample size and provides information on both the variable of interest and the auxiliary variables.
data_proj	A data frame or a data frame extension (e.g., a tibble) representing the projection dataset, which is characterized by a larger sample size that collects only auxiliary information or general-purpose variables. This dataset must contain the same auxiliary variables as the data_model and is used for making predictions based on the trained model.
id	Column name specifying cluster ids from the largest level to the smallest level, where ~0 or ~1 represents a formula indicating the absence of clusters.
STRATA	The name of the column that specifies the strata; set to NULL if no stratification is required.#' @param test_size Proportion of data used for training (default is 0.8, meaning 80% for training and 20% for validation).
domain1	Domain variables for higher-level survey estimation. (e.g., "province")
domain2	Domain variables for more granular survey estimation at a lower administrative level. (e.g., "regency")
weight	The name of the column in data_proj that represents the survey weight, usually used for the purpose of indirect estimation .
task_type	A string that specifies the modeling objective, indicating whether the task is for classification or regression. Use "classification" for tasks where the goal is to categorize data into discrete classes, such as predicting whether an email is spam or not. Use "regression" for tasks where the goal is to predict a continuous outcome, such as forecasting sales revenue or predicting house prices.
test_size	The proportion of data used for testing, with the remaining data used for training.
nfold	The number of data partitions used for cross-validation (n-fold validation).
corrected_bias	A logical value indicating whether to apply bias correction to the estimation results from the modeling process. When set to TRUE, this parameter ensures that the estimates are adjusted to account for any systematic biases, leading to more accurate and reliable predictions.
feature_selection	Selection of predictor variables (default is TRUE)

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)

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