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    <title>topic Re: probing interactions in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/294328#M15660</link>
    <description>&lt;P&gt;Is there a technical meaning of "probing interactions?" &amp;nbsp;Or do you just means that you want to understand the interactions?&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If it is helpful, SAS provides various "slice" and "at" options that enable you to graphically display the predicted values at&amp;nbsp;certain values of the explanatory variables. See the article &lt;A href="http://blogs.sas.com/content/iml/2016/06/22/sas-effectplot-statement.html" target="_self"&gt;"Use the EFFECTPLOT statement to visualize regression models in SAS."&lt;/A&gt;&lt;/P&gt;</description>
    <pubDate>Fri, 26 Aug 2016 10:02:59 GMT</pubDate>
    <dc:creator>Rick_SAS</dc:creator>
    <dc:date>2016-08-26T10:02:59Z</dc:date>
    <item>
      <title>probing interactions</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/294250#M15653</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am trying to probe a three way interaction between predictor variables. I used PROC mixed to estimate a three level HLM. I tried to use the lsestimate statement, but it only works if the variables are in the class statement. How would I go about probing a three way interaction with variables listed only in the model statement?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks!&lt;/P&gt;</description>
      <pubDate>Fri, 26 Aug 2016 02:20:13 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/294250#M15653</guid>
      <dc:creator>milo922</dc:creator>
      <dc:date>2016-08-26T02:20:13Z</dc:date>
    </item>
    <item>
      <title>Re: probing interactions</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/294328#M15660</link>
      <description>&lt;P&gt;Is there a technical meaning of "probing interactions?" &amp;nbsp;Or do you just means that you want to understand the interactions?&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If it is helpful, SAS provides various "slice" and "at" options that enable you to graphically display the predicted values at&amp;nbsp;certain values of the explanatory variables. See the article &lt;A href="http://blogs.sas.com/content/iml/2016/06/22/sas-effectplot-statement.html" target="_self"&gt;"Use the EFFECTPLOT statement to visualize regression models in SAS."&lt;/A&gt;&lt;/P&gt;</description>
      <pubDate>Fri, 26 Aug 2016 10:02:59 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/294328#M15660</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2016-08-26T10:02:59Z</dc:date>
    </item>
    <item>
      <title>Re: probing interactions</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/295271#M15733</link>
      <description>&lt;P&gt;Sometimes it is better to put variables into a CLASS statement when it may be the case that nonlinearities are hiding in there someplace. &amp;nbsp;For instance, suppose you had 3 levels of a treatment, plus a control, and for further fun, let's suppose that the treatment is a fertilizer and you are measuring plant growth. &amp;nbsp;Now suppose the response, in some units, are&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Control &amp;nbsp; &amp;nbsp; &amp;nbsp; &amp;nbsp;1&lt;/P&gt;
&lt;P&gt;Low rate &amp;nbsp; &amp;nbsp; 1.5&lt;/P&gt;
&lt;P&gt;Med rate &amp;nbsp; &amp;nbsp; 4&lt;/P&gt;
&lt;P&gt;High rate &amp;nbsp; &amp;nbsp; 2&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;If you fit this as a continuous, linear response, you are going to miss the fact that the high dose actually brings down the response. &amp;nbsp;You can try quadratic and cubic functions, but it is just a lot easier to be semi-parametric, and look at this as a categorical variable. &amp;nbsp;Then with the LSMESTIMATE statement, you could fit polynomial coefficients, if that is what you are interested in. &amp;nbsp;It seems to me to be a lot easier to go that way.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Steve Denham&lt;/P&gt;</description>
      <pubDate>Tue, 30 Aug 2016 18:10:16 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/probing-interactions/m-p/295271#M15733</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2016-08-30T18:10:16Z</dc:date>
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