R2MLwiN: A Package to Run MLwiN from within R

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 generalized 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.

[1]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[2]  Jarrod Had MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package , 2010 .

[3]  Bradley P. Carlin,et al.  Markov Chain Monte Carlo in Practice: A Roundtable Discussion , 1998 .

[4]  Tx Station Stata Statistical Software: Release 7. , 2001 .

[5]  Peng Xu,et al.  Wine , 2006, A Handbook of Food Processing in Classical Rome.

[6]  M. Plummer,et al.  CODA: convergence diagnosis and output analysis for MCMC , 2006 .

[7]  Bradley P. Carlin,et al.  Structured Markov Chain Monte Carlo , 2000 .

[8]  H. Goldstein Multilevel mixed linear model analysis using iterative generalized least squares , 1986 .

[9]  Harvey Goldstein,et al.  Multiple membership multiple classification (MMMC) models , 2001 .

[10]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[11]  Joseph Hilbe,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2009 .

[12]  Andrew Thomas,et al.  The BUGS project: Evolution, critique and future directions , 2009, Statistics in medicine.

[13]  Jun S. Liu,et al.  Parameter Expansion for Data Augmentation , 1999 .

[14]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[15]  D. Bates,et al.  Linear Mixed-Effects Models using 'Eigen' and S4 , 2015 .

[16]  William J Browne,et al.  MCMC Estimation in MLwiN (Version 2.13) Centre for Multilevel Modelling, University of Bristol , 2009 .

[17]  Andrew Gelman,et al.  R2WinBUGS: A Package for Running WinBUGS from R , 2005 .

[18]  Jarrod D. Hadfield,et al.  MCMC methods for multi-response generalized linear mixed models , 2010 .

[19]  Christopher M J Charlton,et al.  runmlwin : A Program to Run the MLwiN Multilevel Modeling Software from within Stata , 2013 .

[20]  V. Carey,et al.  Mixed-Effects Models in S and S-Plus , 2001 .

[21]  William J. Browne,et al.  Implementation and performance issues in the Bayesian and likelihood fitting of multilevel models , 2000, Comput. Stat..

[22]  R. Godson School matters. , 2007, Community practitioner : the journal of the Community Practitioners' & Health Visitors' Association.

[23]  David J. Lunn,et al.  The BUGS Book: A Practical Introduction to Bayesian Analysis , 2013 .

[24]  Risto Lehtonen,et al.  Multilevel Statistical Models , 2005 .

[25]  William J. Browne,et al.  Bayesian and likelihood-based methods in multilevel modeling 1 A comparison of Bayesian and likelihood-based methods for fitting multilevel models , 2006 .

[26]  Harvey Goldstein,et al.  MCMC algorithms for structured multivariate normal models , 2008 .

[27]  Dani Gamerman,et al.  Sampling from the posterior distribution in generalized linear mixed models , 1997, Stat. Comput..

[28]  Luc Moreau,et al.  Stat-JR version 1.0 , 2013 .

[29]  Gabor Grothendieck,et al.  Lattice: Multivariate Data Visualization with R , 2008 .

[30]  A. Raftery,et al.  How Many Iterations in the Gibbs Sampler , 1991 .

[31]  Harvey Goldstein,et al.  Likelihood methods for fitting multilevel models with complex level-1 variation , 2002 .

[32]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[33]  Robert E. Ployhart,et al.  Hierarchical Linear Models , 2014 .

[34]  Sophia Rabe-Hesketh,et al.  Redundant Overdispersion Parameters in Multilevel Models for Categorical Responses , 2007 .

[35]  H. Pan,et al.  A Multilevel Analysis of School Examination Results , 1993 .

[36]  Achim Zeileis,et al.  Diagnostic Checking in Regression Relationships , 2015 .

[37]  John Cleland,et al.  Bangladesh Fertility Survey 1989. , 1990 .