[R-sig-eco] Regression when Y has an estimation variance

[Viechtbauer, Wolfgang (SP)]

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-bounces using r-project.org] On
>Behalf Of Dixon, Philip M [STAT]
>Sent: Monday, 14 January, 2019 15:52
>To: r-sig-ecology using 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