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    <title>topic Re: Comparing Logistic Regression in SAS Enteprise Miner and SAS Enterprise Guide in SAS Data Science</title>
    <link>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477144#M7178</link>
    <description>&lt;P&gt;Are the parameterization methods the same for your categorical variables?&lt;/P&gt;
&lt;P&gt;The default is GLM though REF is usually what people want.&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/220481"&gt;@jnunner&lt;/a&gt;&amp;nbsp;wrote:&lt;BR /&gt;
&lt;P&gt;I am running a logistic regression to predict a binary outcome with 12 predictors.&amp;nbsp; I ran the logistic regression in Enterprise Guide and then in Enterprise Miner...all fit statistics match, but the intercept and some of the parameter estimates do not match. While some odds ratios in one output can be inverted to match the odds-ratios in the other, I cannot come up with a logical reason of why this is happening. In each I am using the binary outcome=1 as the level to fit model.&amp;nbsp; I attached output for reference.&amp;nbsp; Any help is appreciated!&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;HR /&gt;&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Wed, 11 Jul 2018 16:27:47 GMT</pubDate>
    <dc:creator>Reeza</dc:creator>
    <dc:date>2018-07-11T16:27:47Z</dc:date>
    <item>
      <title>Comparing Logistic Regression in SAS Enteprise Miner and SAS Enterprise Guide</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477137#M7177</link>
      <description>&lt;P&gt;I am running a logistic regression to predict a binary outcome with 12 predictors.&amp;nbsp; I ran the logistic regression in Enterprise Guide and then in Enterprise Miner...all fit statistics match, but the intercept and some of the parameter estimates do not match. While some odds ratios in one output can be inverted to match the odds-ratios in the other, I cannot come up with a logical reason of why this is happening. In each I am using the binary outcome=1 as the level to fit model.&amp;nbsp; I attached output for reference.&amp;nbsp; Any help is appreciated!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 11 Jul 2018 15:57:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477137#M7177</guid>
      <dc:creator>jnunner</dc:creator>
      <dc:date>2018-07-11T15:57:19Z</dc:date>
    </item>
    <item>
      <title>Re: Comparing Logistic Regression in SAS Enteprise Miner and SAS Enterprise Guide</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477144#M7178</link>
      <description>&lt;P&gt;Are the parameterization methods the same for your categorical variables?&lt;/P&gt;
&lt;P&gt;The default is GLM though REF is usually what people want.&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/220481"&gt;@jnunner&lt;/a&gt;&amp;nbsp;wrote:&lt;BR /&gt;
&lt;P&gt;I am running a logistic regression to predict a binary outcome with 12 predictors.&amp;nbsp; I ran the logistic regression in Enterprise Guide and then in Enterprise Miner...all fit statistics match, but the intercept and some of the parameter estimates do not match. While some odds ratios in one output can be inverted to match the odds-ratios in the other, I cannot come up with a logical reason of why this is happening. In each I am using the binary outcome=1 as the level to fit model.&amp;nbsp; I attached output for reference.&amp;nbsp; Any help is appreciated!&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;HR /&gt;&lt;/BLOCKQUOTE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 11 Jul 2018 16:27:47 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477144#M7178</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2018-07-11T16:27:47Z</dc:date>
    </item>
    <item>
      <title>Re: Comparing Logistic Regression in SAS Enteprise Miner and SAS Enterprise Guide</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477147#M7179</link>
      <description>Reeza,&lt;BR /&gt;&lt;BR /&gt;Thanks for your quick reply! When I program in SAS I do use(param=ref ref=first). I am new to EG and thought that because I had binary categorical variables I could 'drag' all variables (binary and quantitative) to the 'Quantitative' list and be ok... evidently dragging binary predictors to the 'categorical' list fixes the problem.&lt;BR /&gt;&lt;BR /&gt;Thanks!!!&lt;BR /&gt;</description>
      <pubDate>Wed, 11 Jul 2018 16:48:42 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477147#M7179</guid>
      <dc:creator>jnunner</dc:creator>
      <dc:date>2018-07-11T16:48:42Z</dc:date>
    </item>
    <item>
      <title>Re: Comparing Logistic Regression in SAS Enteprise Miner and SAS Enterprise Guide</title>
      <link>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477154#M7180</link>
      <description>&lt;P&gt;Glad to hear it's fixed, please mark this as solved. Marking your own answer as the solution here would also be appropriate.&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Cheers!&lt;/P&gt;</description>
      <pubDate>Wed, 11 Jul 2018 17:05:03 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Data-Science/Comparing-Logistic-Regression-in-SAS-Enteprise-Miner-and-SAS/m-p/477154#M7180</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2018-07-11T17:05:03Z</dc:date>
    </item>
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