<?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 cluster data graphical representation in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103427#M5467</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi to everyone, checking this link from SAS about the rpoc mixel model:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;A href="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect006.htm" title="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect006.htm"&gt;http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect006.htm&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I got the next example:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Clustered Data Example&amp;nbsp; &lt;/P&gt;&lt;P&gt; &lt;A class="subjectindex" id="statug.mixed.a0000000040"&gt; &lt;/A&gt;&lt;A class="subjectindex" id="statug.mixed.a0000000041"&gt; &lt;/A&gt;&lt;/P&gt;&lt;P&gt;Consider the following SAS data set as an introductory example: &lt;/P&gt;&lt;P&gt;&lt;A class="subjectindex" id="statug.mixed.a0000000042"&gt; &lt;/A&gt;&lt;/P&gt;&lt;PRE class="jive-pre"&gt;&amp;nbsp;&amp;nbsp; data heights; input Family Gender$ Height @@; datalines; 1 F 67&amp;nbsp;&amp;nbsp; 1 F 66&amp;nbsp;&amp;nbsp; 1 F 64&amp;nbsp;&amp;nbsp; 1 M 71&amp;nbsp;&amp;nbsp; 1 M 72&amp;nbsp;&amp;nbsp; 2 F 63 2 F 63&amp;nbsp;&amp;nbsp; 2 F 67&amp;nbsp;&amp;nbsp; 2 M 69&amp;nbsp;&amp;nbsp; 2 M 68&amp;nbsp;&amp;nbsp; 2 M 70&amp;nbsp;&amp;nbsp; 3 F 63 3 M 64&amp;nbsp;&amp;nbsp; 4 F 67&amp;nbsp;&amp;nbsp; 4 F 66&amp;nbsp;&amp;nbsp; 4 M 67&amp;nbsp;&amp;nbsp; 4 M 67&amp;nbsp;&amp;nbsp; 4 M 69 ; &lt;/PRE&gt;&lt;P&gt;The response variable &lt;SPAN class="variable"&gt;Height&lt;/SPAN&gt; measures the heights (in inches) of 18 individuals. The individuals are classified according to &lt;SPAN class="variable"&gt;Family&lt;/SPAN&gt; and &lt;SPAN class="variable"&gt;Gender&lt;/SPAN&gt;. You can perform a traditional two-way analysis of variance of these data with the following PROC MIXED statements: &lt;/P&gt;&lt;P&gt;&lt;A class="subjectindex" id="statug.mixed.a0000000043"&gt; &lt;/A&gt;&lt;/P&gt;&lt;P&gt;My question is, there are some procedure to visualize graphically this kind of clustering problems, i.e to see which model is fitting better&lt;/P&gt;&lt;P&gt;my raw of data, or see how well separated the cluter of data are, etc, etc.&lt;/P&gt;&lt;P&gt;Thnaks in advance,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;V.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Thu, 03 May 2012 21:39:28 GMT</pubDate>
    <dc:creator>michtka</dc:creator>
    <dc:date>2012-05-03T21:39:28Z</dc:date>
    <item>
      <title>cluster data graphical representation</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103427#M5467</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Hi to everyone, checking this link from SAS about the rpoc mixel model:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;A href="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect006.htm" title="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect006.htm"&gt;http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_mixed_sect006.htm&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I got the next example:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Clustered Data Example&amp;nbsp; &lt;/P&gt;&lt;P&gt; &lt;A class="subjectindex" id="statug.mixed.a0000000040"&gt; &lt;/A&gt;&lt;A class="subjectindex" id="statug.mixed.a0000000041"&gt; &lt;/A&gt;&lt;/P&gt;&lt;P&gt;Consider the following SAS data set as an introductory example: &lt;/P&gt;&lt;P&gt;&lt;A class="subjectindex" id="statug.mixed.a0000000042"&gt; &lt;/A&gt;&lt;/P&gt;&lt;PRE class="jive-pre"&gt;&amp;nbsp;&amp;nbsp; data heights; input Family Gender$ Height @@; datalines; 1 F 67&amp;nbsp;&amp;nbsp; 1 F 66&amp;nbsp;&amp;nbsp; 1 F 64&amp;nbsp;&amp;nbsp; 1 M 71&amp;nbsp;&amp;nbsp; 1 M 72&amp;nbsp;&amp;nbsp; 2 F 63 2 F 63&amp;nbsp;&amp;nbsp; 2 F 67&amp;nbsp;&amp;nbsp; 2 M 69&amp;nbsp;&amp;nbsp; 2 M 68&amp;nbsp;&amp;nbsp; 2 M 70&amp;nbsp;&amp;nbsp; 3 F 63 3 M 64&amp;nbsp;&amp;nbsp; 4 F 67&amp;nbsp;&amp;nbsp; 4 F 66&amp;nbsp;&amp;nbsp; 4 M 67&amp;nbsp;&amp;nbsp; 4 M 67&amp;nbsp;&amp;nbsp; 4 M 69 ; &lt;/PRE&gt;&lt;P&gt;The response variable &lt;SPAN class="variable"&gt;Height&lt;/SPAN&gt; measures the heights (in inches) of 18 individuals. The individuals are classified according to &lt;SPAN class="variable"&gt;Family&lt;/SPAN&gt; and &lt;SPAN class="variable"&gt;Gender&lt;/SPAN&gt;. You can perform a traditional two-way analysis of variance of these data with the following PROC MIXED statements: &lt;/P&gt;&lt;P&gt;&lt;A class="subjectindex" id="statug.mixed.a0000000043"&gt; &lt;/A&gt;&lt;/P&gt;&lt;P&gt;My question is, there are some procedure to visualize graphically this kind of clustering problems, i.e to see which model is fitting better&lt;/P&gt;&lt;P&gt;my raw of data, or see how well separated the cluter of data are, etc, etc.&lt;/P&gt;&lt;P&gt;Thnaks in advance,&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;V.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Thu, 03 May 2012 21:39:28 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103427#M5467</guid>
      <dc:creator>michtka</dc:creator>
      <dc:date>2012-05-03T21:39:28Z</dc:date>
    </item>
    <item>
      <title>Re: cluster data graphical representation</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103428#M5468</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;You can visualize the group distributions of observed and residual values from this model with :&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&amp;nbsp; proc mixed data=heights plots=boxplot(fixed observed);&lt;/P&gt;&lt;P&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; class Family Gender;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp;&amp;nbsp; model Height = Gender Family Family*Gender;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&amp;nbsp; run;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;A kind word of caution: you can learn a lot from SAS documentation, but it cannot replace a course in statistics.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;PG&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 04 May 2012 02:13:19 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103428#M5468</guid>
      <dc:creator>PGStats</dc:creator>
      <dc:date>2012-05-04T02:13:19Z</dc:date>
    </item>
    <item>
      <title>Re: cluster data graphical representation</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103429#M5469</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Thanks PG...I found like I was looking for:&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;&lt;A href="http://support.sas.com/documentation/cdl/en/statugclustering/61759/PDF/default/statugclustering.pdf"&gt;http://support.sas.com/documentation/cdl/en/statugclustering/61759/PDF/default/statugclustering.pdf&lt;/A&gt;&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Fri, 04 May 2012 11:46:26 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/cluster-data-graphical-representation/m-p/103429#M5469</guid>
      <dc:creator>michtka</dc:creator>
      <dc:date>2012-05-04T11:46:26Z</dc:date>
    </item>
  </channel>
</rss>

