R2MLwiN-package {R2MLwiN} | R Documentation |
Running MLwiN from within R
Description
R2MLwiN is an R command interface to the MLwiN multilevel modelling software package, allowing users to fit multilevel models using MLwiN (and also WinBUGS / OpenBUGS) from within the R environment.
New features in version 0.8-3
Support for model comparison tables via texreg-package
and memisc-package
have been
added to R2MLwiN version 0.8-3. For an example of using texreg-package
see e.g. demo(MCMCGuide04)
.
Important differences between version 0.8-0 and earlier versions
A number of wide-ranging changes, including a new model-fitting syntax more in keeping with that conventionally used in R, were introduced in R2MLwiN version 0.8-0.
The demos, which replicate both the User's Guide to MLwiN (Rasbash et al, 2012) and
MCMC Estimation in MLwiN (Browne, 2012) manuals, provide practical demonstrations of many
of these changes. See demo(package = "R2MLwiN")
for a list of demo titles; to run one
type e.g. demo(UserGuide03)
or view a demo's script via
file.show(system.file("demo", "UserGuide03", package = "R2MLwiN"))
.
The Formula is now specified via a
formula
object (with some differences in specification: seerunMLwiN
). So, for example, previously a 2-level model random intercept model would be specified by e.g.normexam ~ (0|cons + standlrt) + (2|cons) + (1|cons), levID = c('school', 'student')
, withnormexam
the response variable,cons
a constant of ones forming the intercept, which is allowed to vary at level 1 (student
) and level 2 (school
), andstandlrt
included as a predictor in the fixed part of the model. Whilst back-compatibility is preserved (i.e. this specification will currently still work) the same model can now be more parsimoniously specified vianormexam ~ 1 + standlrt + (1 | school) + (1 | student)
. As well examples in the demos, seerunMLwiN
andFormula.translate
for further info.As a means of specifying cross-classified, multiple membership or CAR models,
xclass
is now deprecated. Instead, cross-classified models are specified viaxc = TRUE
, multiple membership models are specified viamm
, and CAR models are specified viacar
, in the list ofestoptions
.mm
andcar
can be a list of variable names, a list of vectors, or a matrix. SeerunMLwiN
for further details.Multiple membership/CAR information can now be specified using matrices.
df2matrix
andmatrix2df
functions have also been added to convert such information betweendata.frame
andmatrix
formats.As a means of specifying common (i.e. the same for each category) or separate (i.e. one for each category) coefficients in ordered multinomial and multivariate response models,
c
(for common) ands
(for separate) have been replaced by the employment of square brackets after the relevant variable to indicate a common coefficient is to be fitted (a separate coefficient will be fitted otherwise). Within these square brackets needs to be placed a numeric identifier indicating the responses for which a common coefficient is to be added (seerunMLwiN
for further details). E.g. what would have been previously specified, within theFormula
object, as... (0s|cons + ravens) + (0c|fluent{1, 0}) ...
would now be specified by... 1 + ravens + fluent[1] ...
.When added as a predictor, a variable encoded as a
factor
is automatically handled as categorical, replacing the previous use of square brackets after the variable name.A number of generic s4 methods have been added to improve compatibility with statistical functions which use them (e.g. see
stats4-package
). So, for example, the addition of alogLik
means a likelihood ratio test can now be conducted on twomlwinfitIGLS-class
objects using thelrtest
function, e.g.lrtest(mymodel1, mymodel2)
. Seehelp(package = "R2MLwiN")
for the index listing these various methods.
References
R2MLwiN
Zhang, Z., Parker, R.M.A., Charlton, C.M.J., Leckie, G. and Browne, W.J. (2016) R2MLwiN: A Package to Run MLwiN from within R. Journal of Statistical Software, 72(10), 1-43. doi:10.18637/jss.v072.i10
MLwiN software and manuals
Browne, W.J. (2012) MCMC Estimation in MLwiN, v2.26. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Charlton, C., Browne, W.J., Healy, M. and Cameron, B. (2009) MLwiN Version 2.1. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Charlton, C. and Pillinger, R. (2012) Manual Supplement to MLwiN v2.26. Centre for Multilevel Modelling, University of Bristol.
Rasbash, J., Steele, F., Browne, W.J. and Goldstein, H. (2012) A User's Guide to MLwiN Version 2.26. Centre for Multilevel Modelling, University of Bristol.
OpenBUGS
Thomas, A., O'Hara, B., Ligges, U. and Sturtz, S. (2006) Making BUGS Open. R News, 6, 12:17.
WinBUGS
Spiegelhalter, D.J., Thomas, A. and Best, N.G. (1999) WinBUGS Version 1.2 User Manual. MRC Biostatistics Unit.
Maintainer
Zhengzheng Zhang zhengzheng236@gmail.com
Author(s)
Zhang, Z., Charlton, C.M.J., Parker, R.M.A., Leckie, G., and Browne, W.J. (2016) Centre for Multilevel Modelling, University of Bristol.
See Also
Useful links:
Examples
## Not run:
library(R2MLwiN)
# NOTE: if MLwiN not saved in location R2MLwiN defaults to, specify path via:
# options(MLwiN_path = 'path/to/MLwiN vX.XX/')
# If using R2MLwiN via WINE, the path may look like this:
# options(MLwiN_path = '/home/USERNAME/.wine/drive_c/Program Files (x86)/MLwiN vX.XX/')
data(tutorial, package = "R2MLwiN")
(mymodel <- runMLwiN(normexam ~ 1 + standlrt + (1 + standlrt | school) + (1 | student),
estoptions = list(EstM = 1), data = tutorial))
## The R2MLwiN package includes scripts to replicate all the analyses in
## Rasbash et al (2012) A User's Guide to MLwiN Version 2.26 and
## Browne, W.J. (2012) MCMC estimation in MLwiN Version 2.26.
## The MLwiN manuals are available online, see:
## http://www.bristol.ac.uk/cmm/software/mlwin/download/manuals.html
## For a list of demo titles
demo(package = 'R2MLwiN')
## Take MCMCGuide03 as an example
## To view file
file.show(system.file('demo', 'MCMCGuide03.R', package='R2MLwiN'))
## To run the demo
demo(MCMCGuide03)
## End(Not run)