Hi there, I am running a two level multilevel meta-regression of 170 estimates nested within 3 informants nested within 26 studies. I run the null model to get a pooled estimate with random effects at the informant level and study level.
Then I test a series of potential moderators (one at a time, given small number of studies and adjust p-values for multiple testing). I use: (sum(Model1$sigma2) - sum(Model2$sigma2)) / sum(Model1$sigma2) to compute the proportional reduction in the total variance from here: http://stackoverflow.com/questions/22356450/getting-r-squared-from-a-mixed-effects-multilevel-model-in-metafor For one moderator, I get a negative value for reduced total variance and an unexpected negative coefficient. Based on Wolfgang's response in the link above this is possible "depending on the size of your dataset, those variance components may not be estimated very precisely and that can lead to such counter-intuitive results". I am trying to diagnose why this model is not being estimated properly and why I am getting an unexpected negative result. When I remove the second level from the model and run a single-level random effects models of 170 estimates nested within 26 studies, the coefficient is positive and as we would expect. Does anyone have any suggestions for what might be going on or how I might diagnose the problem with this model? Thanks, Laura Laura Duncan, M.A. Research Coordinator Offord Centre for Child Studies McMaster University Tel: 905 525 9140 x21504 Fax: 905 574 6665 dunca...@mcmaster.ca ontariochildhealthstudy.ca<www.ontariochildhealthstudy.ca> offordcentre.com Mailing Address Courier Address 1280 Main St. W. MIP 201A 175 Longwood Rd. S. MIP 201A Hamilton, Ontario L8S 4K1 Hamilton, Ontario L8P 0A1 [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
