<html><head><meta http-equiv="Content-Type" content="text/html charset=iso-8859-1"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>Hi Kristian,</div><div><br></div>The default is to fit uncorrelated random effects (a diagonal variance-covariance matrix), but it appears that you can fit them with a full variance-covariance matrix using the corStruct argument. I can not testify to how well this has been tested.<div><br></div><div><br></div><div><table summary="R argblock" style="font-family: Times; position: static; z-index: auto; "><tbody><tr valign="top"><td><code>corStruct</code></td><td><p>a string specifying the covariance structure of the random effects vector. Two types of covariance matrices are currently implemented: <code>"diag"</code> (diagonal matrix) and <code>"full"</code> (positive definite matrix with all elements being estimated).</p></td></tr></tbody></table><div><br></div><div>cheers,</div><div>Mollie</div><div><br></div><div apple-content-edited="true">
<div style="color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div style="color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; border-spacing: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; border-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Mollie Brooks, PhD<br>Postdoctoral Researcher, Population Ecology Research Group, Ozgul Lab, <a href="http://www.popecol.org">http://www.popecol.org</a><br>Institute of Evolutionary Biology & Environmental Studies, University of Zürich<br><br><br></div></span></div></span></div></div>
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<br><div><div>On 7 May 2013, at 9:39 AM, Kristian Kleinke <<a href="mailto:kmkleinke@gmail.com">kmkleinke@gmail.com</a>> wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">Hi, <br><br>just a short question I've been trying to find an answer to: <br>am I correct, that glmmadmb() cannot estimate correlations between random effects?<br><br>Thanks, <br>Kristian<br>_______________________________________________<br>Users mailing list<br><a href="mailto:Users@admb-project.org">Users@admb-project.org</a><br>http://lists.admb-project.org/mailman/listinfo/users<br><br></blockquote></div><br></div></body></html>