saeml_projsvy: An Example Large-Survey Dataset for Producing Projection...

In saeproj.multilevel: Small Area Estimation Using a Projection Estimator with a Multilevel Regression Model

An Example Large-Survey Dataset for Producing Projection Estimates

Description

A simulated large-survey dataset used to demonstrate projection-based small area estimation with a linear multilevel regression model.

The dataset is one fixed replication from the simulation design used in the package examples. It contains auxiliary variables and survey design variables, but does not contain the target variable.

It is intended to be used as data_proj in sae_ml_linear().

Usage

saeml_projsvy

Format

A data frame with 15,000 rows, 10 variables, and 50 domains.

prov

Province identifier.

kab_kota

District or city identifier used as the domain variable.

id_individu

Unique sampled-unit identifier. It is not a PSU or cluster identifier.

Z1

First area-level auxiliary variable.

Z2

Second area-level auxiliary variable.

X1

First unit-level auxiliary variable.

X2

Binary unit-level auxiliary variable.

X3

Third unit-level auxiliary variable.

X4

Fourth unit-level auxiliary variable.

WEIND

Survey sampling weight.

Details

Simulated projection-survey data for saeproj.multilevel.

The dataset contains 300 sampled units in each of 50 domains. It is drawn from the same fixed simulated population as saeml_modelsvy, but does not contain the target variable Y.

It is used to generate unit-level predictions and domain-level synthetic estimates.

The simulated survey design does not include a separate PSU or cluster identifier. Therefore, package examples use cluster_ids = ~1.

Source

Simulated data generated from the package study-simulation design.

See Also

saeml_modelsvy and sae_ml_linear.

Examples

data(saeml_projsvy)

dim(saeml_projsvy)
head(saeml_projsvy)
table(saeml_projsvy$kab_kota)
"Y" %in% names(saeml_projsvy)

saeproj.multilevel documentation built on Sept. 8, 2026, 1:10 a.m.