<?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: when estimating the same model in proc glimmix and surveyreg:same estimates different pvalues in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/when-estimating-the-same-model-in-proc-glimmix-and-surveyreg/m-p/870275#M43070</link>
    <description>&lt;P&gt;Different procedure =&amp;gt; different computation algorithms, different model assumptions, different treatment of weights.&lt;/P&gt;
&lt;P&gt;Take your pick of any of those.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If all of those similarly named variables like Age56, Age57, Age58, etc are dummy variables then you may want to save yourself some coding and have a single CLASS variable for Age or Eli, health, edu ...&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;BLOCKQUOTE&gt;&lt;HR /&gt;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/441908"&gt;@marjanmaes6594&lt;/a&gt;&amp;nbsp;wrote:&lt;BR /&gt;
&lt;P&gt;dear,&lt;/P&gt;
&lt;P&gt;i estimate the same model firstly by using proc surveyreg and secondly by using proc glimmix. i want robust standard errors (as i work with longitudinal data with repeated observations of individuals "mergeid" .&amp;nbsp; i get the same estimates in surveyreg and in glimmix, but i get however different pvalues. how do you explain that?&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;this is the command in proc surveyreg:&lt;/P&gt;
&lt;P&gt;proc surveyreg data=l (where=(gender=2));&lt;BR /&gt;model retired = age56 age57 age58 age59 age60 age61 age62 age63 age64 age65 eli1 eli2 eli3&lt;BR /&gt;age56*eli1 age57*eli1 age58*eli1 age59*eli1 age60*eli1 age61*eli1 age62*eli1 age62*eli1 age63*eli1&lt;BR /&gt;age64*eli1 age65*eli1 age56*eli2 age57*eli2 age58*eli2 age59*eli2 age60*eli2 age61*eli2 age62*eli2 age62*eli2 age63*eli2&lt;BR /&gt;age64*eli2 age65*eli2 age56*eli3 age57*eli3 age58*eli3 age59*eli3 age60*eli3 age61*eli3 age62*eli3 age62*eli3 age63*eli3&lt;BR /&gt;age64*eli3 age65*eli3 lang edu2 edu3 edu4 health1 health2 health4 health5 married divorce ch001_ maxassim u laggdprate gdprate;&lt;BR /&gt;cluster mergeid;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;this is the command in proc glimmix:&lt;/P&gt;
&lt;P&gt;proc glimmix data=l(where=(gender=2 ));&lt;BR /&gt;class mergeid;&lt;BR /&gt;model retired = age56 age57 age58 age59 age60 age61 age62 age63 age64 age65 eli1 eli2 eli3&lt;BR /&gt;age56*eli1 age57*eli1 age58*eli1 age59*eli1 age60*eli1 age61*eli1 age62*eli1 age62*eli1 age63*eli1&lt;BR /&gt;age64*eli1 age65*eli1 age56*eli2 age57*eli2 age58*eli2 age59*eli2 age60*eli2 age61*eli2 age62*eli2 age62*eli2 age63*eli2&lt;BR /&gt;age64*eli2 age65*eli2 age56*eli3 age57*eli3 age58*eli3 age59*eli3 age60*eli3 age61*eli3 age62*eli3 age62*eli3 age63*eli3&lt;BR /&gt;age64*eli3 age65*eli3 lang edu2 edu3 edu4 health1 health2 health4 health5 married divorce ch001_ maxassim u laggdprate gdprate/solution;&lt;BR /&gt;random residual /subject=mergeid ;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;thanks for helping me!&lt;/P&gt;
&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;
&lt;HR /&gt;&lt;/BLOCKQUOTE&gt;
&lt;P&gt;You can find more that you likely want by reading all the DETAIL sections of the online help for each procedure.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Mon, 17 Apr 2023 23:06:11 GMT</pubDate>
    <dc:creator>ballardw</dc:creator>
    <dc:date>2023-04-17T23:06:11Z</dc:date>
    <item>
      <title>when estimating the same model in proc glimmix and surveyreg:same estimates different pvalues</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/when-estimating-the-same-model-in-proc-glimmix-and-surveyreg/m-p/870269#M43067</link>
      <description>&lt;P&gt;dear,&lt;/P&gt;&lt;P&gt;i estimate the same model firstly by using proc surveyreg and secondly by using proc glimmix. i want robust standard errors (as i work with longitudinal data with repeated observations of individuals "mergeid" .&amp;nbsp; i get the same estimates in surveyreg and in glimmix, but i get however different pvalues. how do you explain that?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;this is the command in proc surveyreg:&lt;/P&gt;&lt;P&gt;proc surveyreg data=l (where=(gender=2));&lt;BR /&gt;model retired = age56 age57 age58 age59 age60 age61 age62 age63 age64 age65 eli1 eli2 eli3&lt;BR /&gt;age56*eli1 age57*eli1 age58*eli1 age59*eli1 age60*eli1 age61*eli1 age62*eli1 age62*eli1 age63*eli1&lt;BR /&gt;age64*eli1 age65*eli1 age56*eli2 age57*eli2 age58*eli2 age59*eli2 age60*eli2 age61*eli2 age62*eli2 age62*eli2 age63*eli2&lt;BR /&gt;age64*eli2 age65*eli2 age56*eli3 age57*eli3 age58*eli3 age59*eli3 age60*eli3 age61*eli3 age62*eli3 age62*eli3 age63*eli3&lt;BR /&gt;age64*eli3 age65*eli3 lang edu2 edu3 edu4 health1 health2 health4 health5 married divorce ch001_ maxassim u laggdprate gdprate;&lt;BR /&gt;cluster mergeid;&lt;BR /&gt;run;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;this is the command in proc glimmix:&lt;/P&gt;&lt;P&gt;proc glimmix data=l(where=(gender=2 ));&lt;BR /&gt;class mergeid;&lt;BR /&gt;model retired = age56 age57 age58 age59 age60 age61 age62 age63 age64 age65 eli1 eli2 eli3&lt;BR /&gt;age56*eli1 age57*eli1 age58*eli1 age59*eli1 age60*eli1 age61*eli1 age62*eli1 age62*eli1 age63*eli1&lt;BR /&gt;age64*eli1 age65*eli1 age56*eli2 age57*eli2 age58*eli2 age59*eli2 age60*eli2 age61*eli2 age62*eli2 age62*eli2 age63*eli2&lt;BR /&gt;age64*eli2 age65*eli2 age56*eli3 age57*eli3 age58*eli3 age59*eli3 age60*eli3 age61*eli3 age62*eli3 age62*eli3 age63*eli3&lt;BR /&gt;age64*eli3 age65*eli3 lang edu2 edu3 edu4 health1 health2 health4 health5 married divorce ch001_ maxassim u laggdprate gdprate/solution;&lt;BR /&gt;random residual /subject=mergeid ;&lt;BR /&gt;run;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;thanks for helping me!&lt;/P&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;</description>
      <pubDate>Mon, 17 Apr 2023 21:52:51 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/when-estimating-the-same-model-in-proc-glimmix-and-surveyreg/m-p/870269#M43067</guid>
      <dc:creator>marjanmaes6594</dc:creator>
      <dc:date>2023-04-17T21:52:51Z</dc:date>
    </item>
    <item>
      <title>Re: when estimating the same model in proc glimmix and surveyreg:same estimates different pvalues</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/when-estimating-the-same-model-in-proc-glimmix-and-surveyreg/m-p/870275#M43070</link>
      <description>&lt;P&gt;Different procedure =&amp;gt; different computation algorithms, different model assumptions, different treatment of weights.&lt;/P&gt;
&lt;P&gt;Take your pick of any of those.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If all of those similarly named variables like Age56, Age57, Age58, etc are dummy variables then you may want to save yourself some coding and have a single CLASS variable for Age or Eli, health, edu ...&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;BLOCKQUOTE&gt;&lt;HR /&gt;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/441908"&gt;@marjanmaes6594&lt;/a&gt;&amp;nbsp;wrote:&lt;BR /&gt;
&lt;P&gt;dear,&lt;/P&gt;
&lt;P&gt;i estimate the same model firstly by using proc surveyreg and secondly by using proc glimmix. i want robust standard errors (as i work with longitudinal data with repeated observations of individuals "mergeid" .&amp;nbsp; i get the same estimates in surveyreg and in glimmix, but i get however different pvalues. how do you explain that?&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;this is the command in proc surveyreg:&lt;/P&gt;
&lt;P&gt;proc surveyreg data=l (where=(gender=2));&lt;BR /&gt;model retired = age56 age57 age58 age59 age60 age61 age62 age63 age64 age65 eli1 eli2 eli3&lt;BR /&gt;age56*eli1 age57*eli1 age58*eli1 age59*eli1 age60*eli1 age61*eli1 age62*eli1 age62*eli1 age63*eli1&lt;BR /&gt;age64*eli1 age65*eli1 age56*eli2 age57*eli2 age58*eli2 age59*eli2 age60*eli2 age61*eli2 age62*eli2 age62*eli2 age63*eli2&lt;BR /&gt;age64*eli2 age65*eli2 age56*eli3 age57*eli3 age58*eli3 age59*eli3 age60*eli3 age61*eli3 age62*eli3 age62*eli3 age63*eli3&lt;BR /&gt;age64*eli3 age65*eli3 lang edu2 edu3 edu4 health1 health2 health4 health5 married divorce ch001_ maxassim u laggdprate gdprate;&lt;BR /&gt;cluster mergeid;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;this is the command in proc glimmix:&lt;/P&gt;
&lt;P&gt;proc glimmix data=l(where=(gender=2 ));&lt;BR /&gt;class mergeid;&lt;BR /&gt;model retired = age56 age57 age58 age59 age60 age61 age62 age63 age64 age65 eli1 eli2 eli3&lt;BR /&gt;age56*eli1 age57*eli1 age58*eli1 age59*eli1 age60*eli1 age61*eli1 age62*eli1 age62*eli1 age63*eli1&lt;BR /&gt;age64*eli1 age65*eli1 age56*eli2 age57*eli2 age58*eli2 age59*eli2 age60*eli2 age61*eli2 age62*eli2 age62*eli2 age63*eli2&lt;BR /&gt;age64*eli2 age65*eli2 age56*eli3 age57*eli3 age58*eli3 age59*eli3 age60*eli3 age61*eli3 age62*eli3 age62*eli3 age63*eli3&lt;BR /&gt;age64*eli3 age65*eli3 lang edu2 edu3 edu4 health1 health2 health4 health5 married divorce ch001_ maxassim u laggdprate gdprate/solution;&lt;BR /&gt;random residual /subject=mergeid ;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;thanks for helping me!&lt;/P&gt;
&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;
&lt;HR /&gt;&lt;/BLOCKQUOTE&gt;
&lt;P&gt;You can find more that you likely want by reading all the DETAIL sections of the online help for each procedure.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 17 Apr 2023 23:06:11 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/when-estimating-the-same-model-in-proc-glimmix-and-surveyreg/m-p/870275#M43070</guid>
      <dc:creator>ballardw</dc:creator>
      <dc:date>2023-04-17T23:06:11Z</dc:date>
    </item>
  </channel>
</rss>

