data_binlogitnorm: Simulated Binomial–Logit-Normal data (area-level)

In hbsaems: Hierarchical Bayes Small Area Estimation Model using 'Stan'

Simulated Binomial–Logit-Normal data (area-level)

Description

The data_binlogitnorm dataset contains simulated data for 50 areas based on a Binomial–Logit-Normal model. It includes area-level covariates, true probability parameters, sample sizes, observed counts, direct estimators, sampling variances, and true latent values.

Usage

data_binlogitnorm

Format

A data frame with 50 rows and 13 variables:

n

Sample size per area

y

Observed success count per area

p

Direct estimator of proportion

x1, x2, x3

Auxiliary area-level covariates

u_true

True area-level random effect

eta_true

True linear predictor (logit scale)

p_true

True probability per area

psi_i

Sampling variance of logit-transformed direct estimator

y_obs

Simulated noisy version of eta (logit scale)

p_obs

Estimated proportion via inverse logit of y_obs

group

Area ID (1–100) for random effects formula specifying the grouping structure in the data.

sre

An optional grouping factor mapping observations to spatial locations.

Details

This dataset is intended for evaluating small area estimation models under Binomial–Logit-Normal assumptions.

Source

Simulated data based on a Binomial–Logit-Normal model

hbsaems documentation built on Aug. 8, 2025, 7:28 p.m.