# R2MLwiN-package {R2MLwiN}

### 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.

MLwiN uses both classical and Bayesian approaches to fitting multilevel models, and can model continuous, binomial, count, multivariate, ordered categorical and unordered categorical responses. The data structures it can model include hierarchical, cross-classified and/or multiple membership.

### Details

Package: | R2MLwiN |

Version: | 0.1-8 |

Date: | 2014-01-07 |

License: | GPL (>= 2) |

To use the package, the following objects should be specified:

(1) | a data.frame object containing the data to be modelled; |

(2) | a model formula; |

(3) | a character (vector) specifying the level ID(s); |

(4) | a list of options used to estimate the model; |

(5) | a vector specifying the distribution to be modelled; |

(6) | a vector specifying BUGS options. If non-null, WinBUGS/OpenBUGS are used, in conjunction |

with MLwiN, for modelling. | |

(7) | a path to the folder where the MLwiN executable is saved; |

(8) | a path to the folder where the output files are to be saved. By default, the temporary directory (`tempdir` ) is used. |

Once these objects are specified, the `runMLwiN`

function can be used to call MLwiN from R. After execution, the estimates and other statistics are returned to R and can be displayed as a table in the RGui, and the outputs of all parameter estimates (the chains, residuals and BUGs outputs) are also returned for further post-processing in R, as appropriate.

### References

A User's Guide to MLwiN Version 2.10. Rasbash, J., Steele, F., Browne, W.J. and Goldstein, H. (2009) Centre for Multilevel Modelling, University of Bristol.

MCMC estimation in MLwiN Version 2.25. Browne, W.J. (2012) Centre for Multilevel Modelling, University of Bristol.

### See Also

`Formula.translate`

,`ws2foreign`

,`read.dta`

,`runMLwiN`

,`MacroScript1`

,`MacroScript2`

,

`caterpillar`

,`predLines`

,`predCurves`

,`sixway`

### Examples

## Not run: library(R2MLwiN) ## Modify the following paths as appropriate. ## MLwiN folder mlwin ="C:/Program Files (x86)/MLwiN v2.29/" ## MLwiN sample worksheet folder wspath=paste(mlwin,"/samples/",sep="") ## MLwiN sample worksheet: tutorial dataset wsfile=paste(wspath,"tutorial.ws",sep="");inputfile=paste(tempdir(),"/tutorial.dta",sep="") ws2foreign(wsfile, foreignfile=inputfile, MLwiNPath=mlwin) library(foreign);indata =read.dta(inputfile) ## Define the model formula="normexam~(0|cons+standlrt)+(2|cons+standlrt)+(1|cons)" levID=c('school','student') ## Choose option(s) for inference estoptions= list(EstM=1) ## Fit the model (mymodel=runMLwiN(formula, levID, D="Normal", indata, estoptions, MLwiNPath=mlwin)) ## The R2MLwiN package includes scripts to replicate all the analyses in ## Browne, W.J. (2009) MCMC estimation in MLwiN Version 2.13. ## Version 2.27 is available online; download from the following link: ## <a href="http://www.bristol.ac.uk/cmm/software/mlwin/download/mcmc-print.pdf<br /> ##" title="http://www.bristol.ac.uk/cmm/software/mlwin/download/mcmc-print.pdf<br /> ##">http://www.bristol.ac.uk/cmm/software/mlwin/download/mcmc-print.pdf<br /> ##</a> Centre for Multilevel Modelling, University of Bristol #Contents #01 Introduction to MCMC Estimation and Bayesian Modelling #02 Single Level Normal Response Modelling #03 Variance Components Models #04 Other Features of Variance Components Models #05 Prior Distributions, Starting Values and Random Number Seeds #06 Random Slopes Regression Models #07 Using the WinBUGS Interface in MLwiN #08 Running a Simulation Study in MLwiN #09 Modelling Complex Variance at Level 1 / Heteroscedasticity #10 Modelling Binary Responses #11 Poisson Response Modelling #12 Unordered Categorical Responses #13 Ordered Categorical Responses #14 Adjusting for Measurement Errors in Predictor Variables #15 Cross Classified Models #16 Multiple Membership Models #17 Modelling Spatial Data #18 Multivariate Normal Response Models and Missing Data #19 Mixed Response Models and Correlated Residuals #20 Multilevel Factor Analysis Modelling #21 Using Structured MCMC #22 Using the Structured MVN framework for models #23 Using Orthogonal fixed effect vectors #24 Parameter expansion #25 Hierarchical Centring ## Take Chapter03 as an example ## To find the location of a demo for Chapter03 file.show(system.file("demo", "Chapter03.R", package="R2MLwiN")) ## To run the demo for Chapter03 demo(Chapter03) ## End(Not run)

Documentation reproduced from package R2MLwiN, version 0.1-8. License: GPL (>= 2)