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    <title>topic Re: Stepwise Selection: Exclude variables if they are of the unexpected sign in SAS Programming</title>
    <link>https://communities.sas.com/t5/SAS-Programming/Stepwise-Selection-Exclude-variables-if-they-are-of-the/m-p/748749#M235178</link>
    <description>&lt;P&gt;This is a known drawback of stepwise (and non-stepwise as well). It is caused by multi-collinearity among your X variables. Many techinques, such as PROC REG, PROC GLM, PROC LOGISTIC and so on can be very sensitive to multi-collinearity and can produce coefficients with the wrong sign.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;An alternative is PROC PLS, which is surprisingly robust to multi-collinearity (and doesn't generally produce coefficients with the wrong sign). The lead programmer of PROC PLS wrote &lt;A href="https://support.sas.com/rnd/app/stat/papers/pls.pdf" target="_self"&gt;this paper&lt;/A&gt;, in which he fits a model with 1000 highly correlated x variables, does not bother with the step of variable selection, and still gets a useful model from PLS (Note: the syntax in the paper is very old and doesn't work in the current PROC PLS).&lt;/P&gt;</description>
    <pubDate>Fri, 18 Jun 2021 10:36:01 GMT</pubDate>
    <dc:creator>PaigeMiller</dc:creator>
    <dc:date>2021-06-18T10:36:01Z</dc:date>
    <item>
      <title>Stepwise Selection: Exclude variables if they are of the unexpected sign</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Stepwise-Selection-Exclude-variables-if-they-are-of-the/m-p/748748#M235177</link>
      <description>&lt;P&gt;Hi! I am using the stepwise (and forward) selection criteria just to narrow down the variables that I use in the formal regression analysis that I use. However, a frequent problem that I have been running into is having variables in the final given model from the stepwise selection criteria with the unexpected sign, creating statistically insignificant results. With this, I was wondering if there is a way to code for this, so if the parameter estimate has a variable with the unexpected sign, it can be taken out of the model, and replaced with others.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am using the "proc reg" procedure for this.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thank you!&lt;/P&gt;</description>
      <pubDate>Thu, 17 Jun 2021 19:41:28 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Stepwise-Selection-Exclude-variables-if-they-are-of-the/m-p/748748#M235177</guid>
      <dc:creator>bmm0628</dc:creator>
      <dc:date>2021-06-17T19:41:28Z</dc:date>
    </item>
    <item>
      <title>Re: Stepwise Selection: Exclude variables if they are of the unexpected sign</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Stepwise-Selection-Exclude-variables-if-they-are-of-the/m-p/748749#M235178</link>
      <description>&lt;P&gt;This is a known drawback of stepwise (and non-stepwise as well). It is caused by multi-collinearity among your X variables. Many techinques, such as PROC REG, PROC GLM, PROC LOGISTIC and so on can be very sensitive to multi-collinearity and can produce coefficients with the wrong sign.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;An alternative is PROC PLS, which is surprisingly robust to multi-collinearity (and doesn't generally produce coefficients with the wrong sign). The lead programmer of PROC PLS wrote &lt;A href="https://support.sas.com/rnd/app/stat/papers/pls.pdf" target="_self"&gt;this paper&lt;/A&gt;, in which he fits a model with 1000 highly correlated x variables, does not bother with the step of variable selection, and still gets a useful model from PLS (Note: the syntax in the paper is very old and doesn't work in the current PROC PLS).&lt;/P&gt;</description>
      <pubDate>Fri, 18 Jun 2021 10:36:01 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Stepwise-Selection-Exclude-variables-if-they-are-of-the/m-p/748749#M235178</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2021-06-18T10:36:01Z</dc:date>
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