[R-meta] meta-analysis with proportion data and nested random terms
Jen@@Jo@ch|n@k| @end|ng |rom UGent@be
Mon May 6 14:46:01 CEST 2019
I ran into some problems with a meta-analysis, and would really appreciate some help. I have posted a question on CrossValidated (https://stats.stackexchange.com/questions/405178/r-package-for-meta-analysis-of-beta-distributed-data), but received so far no reply and was advised to ask in this mailing list for help.
In short, I have searched for studies that measure insect diapause under multiple day lengths and extracted the raw data. This data takes a logit-form and is bounded between 0 and 100%. Depending on the study, there were 4-21 data points per curve available (mean 7), so the data is quite sparse. I used an MCMC approach to fit a logit-curve through this data, and report some properties of these curves along with a credible interval. In a second step these reaction norm properties are then correlated with climate data, for example, I correlated the inflection point of the curves with latitude of origin (https://stats.stackexchange.com/questions/402631/use-of-nested-random-terms-in-meta-analysis-with-a-moderator).<https://stats.stackexchange.com/questions/402631/use-of-nested-random-terms-in-meta-analysis-with-a-moderator> One of the properties is, however, a proportion (proportion of variance within environments : variance among environments), and I do not know how to analyse it properly. Using the glmmTMB function with a beta family does not work when the dispersion parameter is set to 0, and metafor does (to my knowledge) not support beta-distributed data. Does anyone know a meta-analysis package that can model beta distributed data, or is there an alternative approach for my data?
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