<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" version="2.0">
  <channel>
    <title>topic Re: Proc Glim​mix : Calculate intraclass correlatio​n (ICC) for Generalized linear mixed model (Pois in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266540#M14035</link>
    <description>&lt;P&gt;Thanks a lot LVM for your answer. I've got it.&lt;/P&gt;&lt;P&gt;To be sure to understand well, does it mean that I can't compute ICC with this distribution? Or is there a way to quantify the degree of similarity in the responses of individuals from the same cluster for the outcome (nevents) ?&lt;BR /&gt;Thanks again,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Jude&lt;/P&gt;</description>
    <pubDate>Tue, 26 Apr 2016 20:51:52 GMT</pubDate>
    <dc:creator>JudeRW</dc:creator>
    <dc:date>2016-04-26T20:51:52Z</dc:date>
    <item>
      <title>Proc Glim​mix : Calculate intraclass correlatio​n (ICC) for Generalized linear mixed model (Poisson)</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266515#M14033</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;I would like to compute intraclass correlation&amp;nbsp;using Proc Glimmix for&amp;nbsp;a generalized linear mixed modelling (Poisson)&amp;nbsp;with&amp;nbsp;1&amp;nbsp;fixed effect (intervention) and&amp;nbsp;1 random effect (cluster).&lt;/P&gt;&lt;P&gt;I model proportions with the Poisson model, defining log(n) in the offset term :&lt;/P&gt;&lt;P&gt;&lt;FONT color="#000080" face="Courier New" size="2"&gt;&lt;STRONG&gt;proc&lt;/STRONG&gt;&lt;/FONT&gt; &lt;STRONG&gt;&lt;FONT color="#000080" face="Courier New" size="2"&gt;glimmix&lt;/FONT&gt;&lt;/STRONG&gt; &lt;FONT color="#0000ff" face="Courier New" size="2"&gt;data&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;=test &lt;/FONT&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;method&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;=quad;&lt;/FONT&gt;&lt;/P&gt;&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;class&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt; CLUSTER INTERVENTION&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt;;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;model&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt; NEVENTS = INTERVENTION / &lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;link&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt;=log &lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;dist&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt;=poisson &lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;s&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt; &lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;offset&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt;=logn ; &lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;&lt;P&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;random&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt; intercept&amp;nbsp;/ &lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;subject&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT face="Courier New" size="2"&gt;&lt;FONT face="Courier New" size="2"&gt;=CLUSTER &lt;/FONT&gt;&lt;/FONT&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;G&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt; &lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;&lt;FONT color="#0000ff" face="Courier New" size="2"&gt;solution&lt;/FONT&gt;&lt;/FONT&gt;&lt;/FONT&gt;&lt;/P&gt;&lt;P&gt;I understand that the&amp;nbsp;residual variance is the 'Estimate' listed in the "Covariance Parameter Estimates" table (or in the G matrix with only 1 row)&amp;nbsp;but I have maybe some misunderstanding.&amp;nbsp;&lt;BR /&gt;I can't find the&amp;nbsp;common variance in order to estimate the ICC for this model: does it correspond to the 'Estimate' per cluster in the "Solution for random effects" ?&lt;BR /&gt;The ICC could then be calculated with&amp;nbsp;( common variance / common variance + residual variance ) but I'm not sure&amp;nbsp;this is&amp;nbsp;appropriate here.&lt;/P&gt;&lt;P&gt;I thought maybe others with more knowledge in GLMM and GLIMMIX may be able to enlighten me.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Regards,&lt;/P&gt;&lt;P&gt;Jude&lt;/P&gt;</description>
      <pubDate>Tue, 26 Apr 2016 19:56:26 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266515#M14033</guid>
      <dc:creator>JudeRW</dc:creator>
      <dc:date>2016-04-26T19:56:26Z</dc:date>
    </item>
    <item>
      <title>Re: Proc Glim​mix : Calculate intraclass correlatio​n (ICC) for Generalized linear mixed model (Pois</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266531#M14034</link>
      <description>&lt;P&gt;You are specifying a Poisson distribution. There is &lt;STRONG&gt;no&lt;/STRONG&gt; residual variance term with a Poisson, by definition. The variance estimate you are seeing is the cluster variance. If you fitted the model without a random effect term, you would not get any variance terms in the output.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;The variance of the Poisson conditional distribution that you are using is given by the mean. There would be a different mean for each level of intervention.&lt;/P&gt;</description>
      <pubDate>Tue, 26 Apr 2016 20:26:55 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266531#M14034</guid>
      <dc:creator>lvm</dc:creator>
      <dc:date>2016-04-26T20:26:55Z</dc:date>
    </item>
    <item>
      <title>Re: Proc Glim​mix : Calculate intraclass correlatio​n (ICC) for Generalized linear mixed model (Pois</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266540#M14035</link>
      <description>&lt;P&gt;Thanks a lot LVM for your answer. I've got it.&lt;/P&gt;&lt;P&gt;To be sure to understand well, does it mean that I can't compute ICC with this distribution? Or is there a way to quantify the degree of similarity in the responses of individuals from the same cluster for the outcome (nevents) ?&lt;BR /&gt;Thanks again,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Jude&lt;/P&gt;</description>
      <pubDate>Tue, 26 Apr 2016 20:51:52 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/266540#M14035</guid>
      <dc:creator>JudeRW</dc:creator>
      <dc:date>2016-04-26T20:51:52Z</dc:date>
    </item>
    <item>
      <title>Re: Proc Glim​mix : Calculate intraclass correlatio​n (ICC) for Generalized linear mixed model (Pois</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/379237#M19913</link>
      <description>&lt;P&gt;Totally worthless response from SAS. &amp;nbsp;How does Stata generate ICCs then?&lt;/P&gt;</description>
      <pubDate>Tue, 25 Jul 2017 23:23:20 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Proc-Glim-mix-Calculate-intraclass-correlatio-n-ICC-for/m-p/379237#M19913</guid>
      <dc:creator>jkellog</dc:creator>
      <dc:date>2017-07-25T23:23:20Z</dc:date>
    </item>
  </channel>
</rss>

