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Hello Dr. Wolfgang,</div>
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Thank you for your response. I apologize for any confusion. I meant to ask about the theoretical distribution of deviance statistics.</div>
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Additionally, I am wondering, do the residual degrees of freedom account for the heterogeneity parameter (tau)?</div>
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Marimuthu</div>
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<div id="divRplyFwdMsg" dir="ltr"><font face="Calibri, sans-serif" style="font-size:11pt" color="#000000"><b>From:</b> Viechtbauer, Wolfgang (NP) <wolfgang.viechtbauer@maastrichtuniversity.nl><br>
<b>Sent:</b> Wednesday, March 5, 2025 8:36 AM<br>
<b>To:</b> R Special Interest Group for Meta-Analysis <r-sig-meta-analysis@r-project.org><br>
<b>Cc:</b> Marimuthu S <sm@mcmaster.ca><br>
<b>Subject:</b> RE: Deviance and it d.f. in pairwise meta analysis</font>
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<div class="PlainText">[You don't often get email from wolfgang.viechtbauer@maastrichtuniversity.nl. Learn why this is important at
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Caution: External email.<br>
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<br>
Dear Marimuthu,<br>
<br>
You can use df.residual() to obtain these:<br>
<br>
> df.residual(res1)<br>
[1] 12<br>
<br>
I am not sure what you mean by 'its distribution'. The df are not a statistic, so they do not have a distribution.<br>
<br>
By the way, no need for REML=F in fitstats() if the model was fitted with ML (fitstats() automatically then provides the ML values).<br>
<br>
Best,<br>
Wolfgang<br>
<br>
> -----Original Message-----<br>
> From: R-sig-meta-analysis <r-sig-meta-analysis-bounces@r-project.org> On Behalf<br>
> Of Marimuthu S via R-sig-meta-analysis<br>
> Sent: Monday, March 3, 2025 22:12<br>
> To: Marimuthu S via R-sig-meta-analysis <r-sig-meta-analysis@r-project.org><br>
> Cc: Marimuthu S <sm@mcmaster.ca><br>
> Subject: [R-meta] Deviance and it d.f. in pairwise meta analysis<br>
><br>
> Hello Everyone,<br>
><br>
> I fitted meta-analysis model using rma() function. I can extract all the fit<br>
> statistics using fitstats() function. But it doesn't give degrees of freedom<br>
> (d.f) for deviance.<br>
><br>
> Could you please let me know if it's possible to calculate the deviance degrees<br>
> of freedom (d.f.) and derive its distribution in pairwise meta-analysis? If it's<br>
> not feasible, I'd appreciate understanding the reasons behind it. Any insights<br>
> or leads would be greatly appreciated. Thank you for your time!<br>
><br>
> Here is my code:<br>
><br>
> library(metafor)<br>
><br>
> dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)<br>
> ### fit random-effects model<br>
> res1 <- rma(yi, vi, data=dat, method="ML")<br>
><br>
> > fitstats(res1, REML=F)<br>
> ML<br>
> logLik: -12.66508<br>
> deviance: 37.11602<br>
> AIC: 29.33015<br>
> BIC: 30.46005<br>
> AICc: 30.53015<br>
><br>
> Regards,<br>
><br>
> Marimuthu,<br>
> Ph.D. candidate<br>
> McMaster University, Canada<br>
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