R2MLwiN: A program to run the MLwiN multilevel modelling software from within R

Zhengzheng Zhang; Richard Parker; Christopher Charlton; George Leckie; William Browne

doi:10.18637/jss.v072.i10

R2MLwiN: A program to run the MLwiN multilevel modelling software from within R

Zhengzheng Zhang, Richard Parker, Christopher Charlton, George Leckie, William Browne

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Abstract

R2MLwiN is a new package designed to run the multilevel modeling software program MLwiN from within the R environment. It allows for a large range of models to be specified which take account of a multilevel structure, including continuous, binary, proportion, count, ordinal and nominal responses for data structures which are nested, cross-classified and/or exhibit multiple membership. Estimation is available via iterative generalised least squares (IGLS), which yields maximum likelihood estimates, and also via Markov chain Monte Carlo (MCMC) estimation for Bayesian inference. As well as employing MLwiN's own MCMC engine, users can request that MLwiN write BUGS model, data and initial values statements for use with WinBUGS or OpenBUGS (which R2MLwiN automatically calls via rbugs), employing IGLS starting values from MLwiN. Users can also take advantage of MLwiN's graphical user interface: for example to specify models and inspect plots via its interactive equations and graphics windows. R2MLwiN is supported by a large number of examples, reproducing all the analyses conducted in MLwiN's IGLS and MCMC manuals.

Original language	English
Number of pages	43
Journal	Journal of Statistical Software
Volume	72
Issue number	10
DOIs	https://doi.org/10.18637/jss.v072.i10
Publication status	Published - 8 Sep 2016

Structured keywords

Jean Golding

Keywords

R2MLwiN
MLwiN
R
WinBUGS
OpenBUGS
multilevel model
random effects model
mixed model
hierarchical linear model
clustered data
maximum likelihood estimation
Markov chain Monte Carlo estimation

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The use of interactive electronic-books in the teaching and application of modern, quantitative social science methods
Browne, W. J., Goldstein, H., Jones, K., Leckie, G. B., Moreau, L., Parker, R. M. A. & Michaelides, D.
1/10/13 → 30/09/17
Project: Research

Dr Richard M A Parker
- Richard.Parkerbristol.acuk
- Bristol Medical School (PHS) - Senior Research Associate in Applied Statistics / Epidemiology
Person: Academic

Cite this

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title = "R2MLwiN: A program to run the MLwiN multilevel modelling software from within R",

abstract = "R2MLwiN is a new package designed to run the multilevel modeling software program MLwiN from within the R environment. It allows for a large range of models to be specified which take account of a multilevel structure, including continuous, binary, proportion, count, ordinal and nominal responses for data structures which are nested, cross-classified and/or exhibit multiple membership. Estimation is available via iterative generalised least squares (IGLS), which yields maximum likelihood estimates, and also via Markov chain Monte Carlo (MCMC) estimation for Bayesian inference. As well as employing MLwiN's own MCMC engine, users can request that MLwiN write BUGS model, data and initial values statements for use with WinBUGS or OpenBUGS (which R2MLwiN automatically calls via rbugs), employing IGLS starting values from MLwiN. Users can also take advantage of MLwiN's graphical user interface: for example to specify models and inspect plots via its interactive equations and graphics windows. R2MLwiN is supported by a large number of examples, reproducing all the analyses conducted in MLwiN's IGLS and MCMC manuals.",

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author = "Zhengzheng Zhang and Richard Parker and Christopher Charlton and George Leckie and William Browne",

year = "2016",

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doi = "10.18637/jss.v072.i10",

language = "English",

volume = "72",

journal = "Journal of Statistical Software",

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AB - R2MLwiN is a new package designed to run the multilevel modeling software program MLwiN from within the R environment. It allows for a large range of models to be specified which take account of a multilevel structure, including continuous, binary, proportion, count, ordinal and nominal responses for data structures which are nested, cross-classified and/or exhibit multiple membership. Estimation is available via iterative generalised least squares (IGLS), which yields maximum likelihood estimates, and also via Markov chain Monte Carlo (MCMC) estimation for Bayesian inference. As well as employing MLwiN's own MCMC engine, users can request that MLwiN write BUGS model, data and initial values statements for use with WinBUGS or OpenBUGS (which R2MLwiN automatically calls via rbugs), employing IGLS starting values from MLwiN. Users can also take advantage of MLwiN's graphical user interface: for example to specify models and inspect plots via its interactive equations and graphics windows. R2MLwiN is supported by a large number of examples, reproducing all the analyses conducted in MLwiN's IGLS and MCMC manuals.

KW - R2MLwiN

KW - MLwiN

KW - R

KW - WinBUGS

KW - OpenBUGS

KW - multilevel model

KW - random effects model

KW - mixed model

KW - hierarchical linear model

KW - clustered data

KW - maximum likelihood estimation

KW - Markov chain Monte Carlo estimation

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DO - 10.18637/jss.v072.i10

M3 - Article (Academic Journal)

VL - 72

JO - Journal of Statistical Software

JF - Journal of Statistical Software

SN - 1548-7660

IS - 10

ER -

R2MLwiN: A program to run the MLwiN multilevel modelling software from within R

Abstract

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Keywords

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The use of interactive electronic-books in the teaching and application of modern, quantitative social science methods

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Dr Richard M A Parker

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