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    <title>topic Re: Proc PLS Regression -- Getting Back To Original Predictors in SAS Programming</title>
    <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819027#M323308</link>
    <description>&lt;P&gt;The variables with high loadings (either positive or negative) are the important variables in that component. There's no real cutoff, the meaning of "high" is relative to the other loadings in that component.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;The loadings are available via the DETAILS option of the PROC PLS statement. You can save the loadings to a SAS data set if you want via&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;ods output xloadings=xloadings;&lt;/CODE&gt;&lt;/PRE&gt;</description>
    <pubDate>Sun, 19 Jun 2022 10:18:24 GMT</pubDate>
    <dc:creator>PaigeMiller</dc:creator>
    <dc:date>2022-06-19T10:18:24Z</dc:date>
    <item>
      <title>Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819017#M323306</link>
      <description>&lt;P&gt;As I'm understanding things, PLS regression comes up with a number of 'components.'&amp;nbsp; But how do these components tell us which of the original predictors is most important?&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Thanks!&lt;/P&gt;
&lt;P&gt;Nicholas Kormanik&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sun, 19 Jun 2022 09:55:33 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819017#M323306</guid>
      <dc:creator>NKormanik</dc:creator>
      <dc:date>2022-06-19T09:55:33Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819024#M323307</link>
      <description>There is VIP plot you can refer to .&lt;BR /&gt;&lt;BR /&gt;ods graphics on/width=1000px height=500px;&lt;BR /&gt;ods output  VariableImportancePlot= VariableImportancePlot;&lt;BR /&gt;proc pls data=class  missing=em   nfac=2 plot=(ParmProfiles VIP) details; * cv=split  cvtest(seed=12345);&lt;BR /&gt; class sex;&lt;BR /&gt; model age=weight height sex;&lt;BR /&gt;output out=x predicted=p;&lt;BR /&gt;run;&lt;BR /&gt;proc sort data=VariableImportancePlot;&lt;BR /&gt; by descending VIP;&lt;BR /&gt;run;</description>
      <pubDate>Sun, 19 Jun 2022 10:15:30 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819024#M323307</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2022-06-19T10:15:30Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819027#M323308</link>
      <description>&lt;P&gt;The variables with high loadings (either positive or negative) are the important variables in that component. There's no real cutoff, the meaning of "high" is relative to the other loadings in that component.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;The loadings are available via the DETAILS option of the PROC PLS statement. You can save the loadings to a SAS data set if you want via&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;ods output xloadings=xloadings;&lt;/CODE&gt;&lt;/PRE&gt;</description>
      <pubDate>Sun, 19 Jun 2022 10:18:24 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819027#M323308</guid>
      <dc:creator>PaigeMiller</dc:creator>
      <dc:date>2022-06-19T10:18:24Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819029#M323309</link>
      <description>&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="Ksharp_0-1655634242257.png" style="width: 400px;"&gt;&lt;img src="https://communities.sas.com/t5/image/serverpage/image-id/72426iB3598A80E2D127EE/image-size/medium?v=v2&amp;amp;px=400" role="button" title="Ksharp_0-1655634242257.png" alt="Ksharp_0-1655634242257.png" /&gt;&lt;/span&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sun, 19 Jun 2022 10:24:03 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819029#M323309</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2022-06-19T10:24:03Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819079#M323328</link>
      <description>&lt;P&gt;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/18408"&gt;@Ksharp&lt;/a&gt;&amp;nbsp;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/10892"&gt;@PaigeMiller&lt;/a&gt;&amp;nbsp; &amp;nbsp;You guys rock.&amp;nbsp; Thanks so much!&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 20 Jun 2022 00:02:06 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/819079#M323328</guid>
      <dc:creator>NKormanik</dc:creator>
      <dc:date>2022-06-20T00:02:06Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/820176#M323707</link>
      <description>&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/18408"&gt;@Ksharp&lt;/a&gt;  Noticed that your labels in the above graph are in Chinese.&lt;BR /&gt;&lt;BR /&gt;How about that....&lt;BR /&gt;</description>
      <pubDate>Fri, 24 Jun 2022 01:19:14 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/820176#M323707</guid>
      <dc:creator>NKormanik</dc:creator>
      <dc:date>2022-06-24T01:19:14Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/820217#M323722</link>
      <description>?? I am from China and running Chinese version SAS. If you are running English version SAS with same code , You would get English result . It is called Variables Important Plot . a.k.a VIP</description>
      <pubDate>Fri, 24 Jun 2022 11:31:49 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/820217#M323722</guid>
      <dc:creator>Ksharp</dc:creator>
      <dc:date>2022-06-24T11:31:49Z</dc:date>
    </item>
    <item>
      <title>Re: Proc PLS Regression -- Getting Back To Original Predictors</title>
      <link>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/820348#M323789</link>
      <description>&lt;P&gt;&lt;a href="https://communities.sas.com/t5/user/viewprofilepage/user-id/18408"&gt;@Ksharp&lt;/a&gt;&amp;nbsp; &amp;nbsp;Yes, I know.&amp;nbsp; Mainland, would be my guess.&amp;nbsp; SAS with everything in Chinese just seemed even more overwhelming than SAS in English.&amp;nbsp; Like, OMG!!&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sat, 25 Jun 2022 06:10:29 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-Programming/Proc-PLS-Regression-Getting-Back-To-Original-Predictors/m-p/820348#M323789</guid>
      <dc:creator>NKormanik</dc:creator>
      <dc:date>2022-06-25T06:10:29Z</dc:date>
    </item>
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