Package: sae.prop 0.1.1

M. Rijalus Sholihin

sae.prop: Small Area Estimation using Fay-Herriot Models with Additive Logistic Transformation

Implements Additive Logistic Transformation (alr) for Small Area Estimation under Fay Herriot Model. Small Area Estimation is used to borrow strength from auxiliary variables to improve the effectiveness of a domain sample size. This package uses Empirical Best Linear Unbiased Prediction (EBLUP) estimator. The Additive Logistic Transformation (alr) are based on transformation by Aitchison J (1986). The covariance matrix for multivariate application is base on covariance matrix used by Esteban M, Lombardía M, López-Vizcaíno E, Morales D, and Pérez A <doi:10.1007/s11749-019-00688-w>. The non-sampled models are modified area-level models based on models proposed by Anisa R, Kurnia A, and Indahwati I <doi:10.9790/5728-10121519>, with univariate model using model-3, and multivariate model using model-1. The MSE are estimated using Parametric Bootstrap approach. For non-sampled cases, MSE are estimated using modified approach proposed by Haris F and Ubaidillah A <doi:10.4108/eai.2-8-2019.2290339>.

Authors:M. Rijalus Sholihin [aut, cre], Cucu Sumarni [aut]

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sae.prop.pdf |sae.prop.html✨
sae.prop/json (API)

# Install 'sae.prop' in R:
install.packages('sae.prop', repos = c('https://mrijalussholihin.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/mrijalussholihin/sae.prop/issues

Datasets:
  • datasaem - Data generated based on Multivariate Fay Herriot Model with Additive Logistic Transformation
  • datasaem.ns - Data generated based on Multivariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Cases
  • datasaeu - Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation
  • datasaeu.ns - Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Cases

On CRAN:

8 exports 1.02 score 28 dependencies 1 mentions 232 downloads

Last updated 2 years agofrom:d3882311a2. Checks:OK: 7. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 12 2024
R-4.5-winOKSep 12 2024
R-4.5-linuxOKSep 12 2024
R-4.4-winOKSep 12 2024
R-4.4-macOKSep 12 2024
R-4.3-winOKSep 12 2024
R-4.3-macOKSep 12 2024

Exports:mseFH.mpropmseFH.ns.mpropmseFH.ns.upropmseFH.upropsaeFH.mpropsaeFH.ns.mpropsaeFH.ns.upropsaeFH.uprop

Dependencies:abindclasscliclustercorpcorcrayonDEoptimRdiptestflexmixfpcgluehmskernlablatticelifecyclemagicMASSmclustmodeltoolsnnetpkgconfigprabclusprettyunitsprogressR6rlangrobustbasevctrs

Readme and manuals

Help Manual

Help pageTopics
Data generated based on Multivariate Fay Herriot Model with Additive Logistic Transformationdatasaem
Data generated based on Multivariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Casesdatasaem.ns
Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformationdatasaeu
Data generated based on Univariate Fay Herriot Model with Additive Logistic Transformation with Non-Sampled Casesdatasaeu.ns
Parametric Bootstrap Mean Squared Error of EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic TransformationmseFH.mprop
Parametric Bootstrap Mean Squared Error of EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled DatamseFH.ns.mprop
Parametric Bootstrap Mean Squared Error of EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled DatamseFH.ns.uprop
Parametric Bootstrap Mean Squared Error of EBLUPs based on a Univariate Fay Herriot model with Additive Logistic TransformationmseFH.uprop
sae.prop : Small Area Estimation for Proportion using Fay Herriot Models with Additive Logistic Transformationsae.prop
EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic TransformationsaeFH.mprop
EBLUPs based on a Multivariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled DatasaeFH.ns.mprop
EBLUPs based on a Univariate Fay Herriot model with Additive Logistic Transformation for Non-Sampled DatasaeFH.ns.uprop
EBLUPs based on a Univariate Fay Herriot model with Additive Logistic TransformationsaeFH.uprop