MLPowSim executable file.
fife2.txt file.A few talks have been given on the topic of sample size calculations in multilevel models.
ESRC research methods 2006 talk
ESRC research methods 2008 talk
Amsterdam 2009 talk
1. There is a bug in the latest version of MLwiN 2.10 with regard the multivariate normal random number generator that means some MLPOWSIM code will cause MLwiN to crash upon attempted execution. The same code will however work fine in version 2.02. This will affect any files created by MLPOWSIM that contain the command MRAN. Fixed in version 2.11.
2. The 1 level Binomial and Poisson models throw up an error message in MLwiN. This can be cured by replacing the file PRE in the discrete sub-directory of the MLwiN install with the following version PRE. . It has also been observed that on some systems were the user does not have administrator rights the macros run much slower in MLwiN 2.10 for binomial and Poisson models as compared to earlier versions of MLwiN. Fixed in version 2.11
3. In R, the use of the PQL method for non-normal responses has been removed from the lmer function in later version of the function. Thus you may find that the PQL method can therefore not be used and so you will need to choose a different estimation procedure.
The PinT software (Tom Snijders, Roel Bosker, and Henk Guldemond) that is used for comparison in the maunal can be downloaded from here.
The ML-DEs software package (Cools, Van den Noortgate & Onghena) that has been developed independently from MLPowSim but which also uses MLwiN and simulation to calculate power calculations for multilevel designs is available from here. We hope to compare MLPowSim with ML-DEs in further work.
The OD (Optimal Design) software package (Steve Raudenbush and colleagues) also looks at multilevel power calculations and in particular cluster randomized designs. It can be downloaded from here.
MCMC in MLwiN manual.
If you wish to try out chapters 21-25 with the current version of MLwiN the MCMC options menu item is hidden. To access it type MCSH in the command interface window and ignore the error messages.
Using SMCMC for normal response multilevel models.
Simple methods to improve MCMC efficiency in random effect models.
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