Hi Philip, Actually, lme() does allow for this, using "control=list(sigma=1)" (so this constrains the scaling factor to 1).
Best, Wolfgang >-----Original Message----- >From: R-sig-ecology [mailto:r-sig-ecology-boun...@r-project.org] On >Behalf Of Dixon, Philip M [STAT] >Sent: Monday, 14 January, 2019 15:52 >To: r-sig-ecology@r-project.org >Subject: [R-sig-eco] Regression when Y has an estimation variance > >Roy, > >One relevant literature is that on meta-regression (a generalization of >meta-analysis). There is a very good handbook by Koricheva, Gurevitch >and Mengerson. Meta analysis mostly deals with Gaussian responses (or >transformable to approximately Gaussian). If there has been any work on >non-gaussian responses, I expect it would be summarized in Koricheva. > >The metafor package is one implementation specifically for meta analysis. >A resource on metafor and other R packages is Schwarzer and Carpenter, >Meta-analysis with R. > >Other programs can also fit the models as mixed models with >heterogenerous specified variances, lme() and lmer() do not let you do >this. lmer() doesn't allow heterogeneous variances; lme() does, but only >of the form k_i*sigma^2 (i.e. variances relative to a common scaling >factor, not absolutely specified variances). For a meta analysis, you >need to specify the absolute variance for each estimate. If someone >knows how to trick lme() to use exactly the error variances that have >been specified, I would love to hear about it. > >Best, >Philip Dixon _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
