<?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 Re: Multinomial regression and likelihood ratios in New SAS User</title>
    <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549085#M8606</link>
    <description>Likelihood ratio or odds ratio?</description>
    <pubDate>Sun, 07 Apr 2019 03:06:38 GMT</pubDate>
    <dc:creator>Reeza</dc:creator>
    <dc:date>2019-04-07T03:06:38Z</dc:date>
    <item>
      <title>Multinomial regression and likelihood ratios</title>
      <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549083#M8605</link>
      <description>&lt;P&gt;Hello, I need to recreate the following table, could anyone please guide me for proper code. It says likelihood ratios calculated using multinomial regression.&amp;nbsp;&lt;/P&gt;&lt;P&gt;My outcome variable is binomial : 1 indicating return and 0 means no return&lt;/P&gt;&lt;P&gt;Age and height are continuous variable&lt;/P&gt;&lt;P&gt;while others are categorical with 0 and 1 values.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;When I searched , I get rather results of likelihood ratio tests, I will really appreciate all the help here.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thank you&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;Table 5. Likelihood ratios for all cause return&amp;nbsp;&lt;/STRONG&gt;&lt;/P&gt;&lt;TABLE&gt;&lt;TBODY&gt;&lt;TR&gt;&lt;TD&gt;&lt;P&gt;&lt;STRONG&gt;Parameter&lt;/STRONG&gt;&lt;/P&gt;&lt;/TD&gt;&lt;TD&gt;&lt;P&gt;&lt;STRONG&gt;Likelihood ratio&lt;/STRONG&gt;&lt;/P&gt;&lt;/TD&gt;&lt;/TR&gt;&lt;TR&gt;&lt;TD&gt;&lt;P&gt;Age&lt;/P&gt;&lt;P&gt;Gender&lt;/P&gt;&lt;P&gt;Height&lt;/P&gt;&lt;P&gt;Renal disease&lt;/P&gt;&lt;P&gt;Liver disease&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;/TD&gt;&lt;TD&gt;&lt;P&gt;0.06&lt;/P&gt;&lt;P&gt;0.03&lt;/P&gt;&lt;P&gt;1.09&lt;/P&gt;&lt;P&gt;0.70&lt;/P&gt;&lt;P&gt;0.07&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;/TD&gt;&lt;/TR&gt;&lt;/TBODY&gt;&lt;/TABLE&gt;&lt;P&gt;&lt;STRONG&gt;* Via multinominal regression analysis, significance p&amp;lt;0.05.&lt;/STRONG&gt;&lt;/P&gt;</description>
      <pubDate>Sun, 07 Apr 2019 02:54:54 GMT</pubDate>
      <guid>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549083#M8605</guid>
      <dc:creator>Sakshi13</dc:creator>
      <dc:date>2019-04-07T02:54:54Z</dc:date>
    </item>
    <item>
      <title>Re: Multinomial regression and likelihood ratios</title>
      <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549085#M8606</link>
      <description>Likelihood ratio or odds ratio?</description>
      <pubDate>Sun, 07 Apr 2019 03:06:38 GMT</pubDate>
      <guid>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549085#M8606</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2019-04-07T03:06:38Z</dc:date>
    </item>
    <item>
      <title>Re: Multinomial regression and likelihood ratios</title>
      <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549086#M8607</link>
      <description>&lt;P&gt;Hi Reeza, likelihood ratio as mentioned in Table. I got this as an assignment.&lt;/P&gt;</description>
      <pubDate>Sun, 07 Apr 2019 03:17:26 GMT</pubDate>
      <guid>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549086#M8607</guid>
      <dc:creator>Sakshi13</dc:creator>
      <dc:date>2019-04-07T03:17:26Z</dc:date>
    </item>
    <item>
      <title>Re: Multinomial regression and likelihood ratios</title>
      <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549092#M8609</link>
      <description>Do you have a reference for that then? Is it for when you're comparing models against each other? So fit base model and then fit a model with each covariate and test against base? Or something else? Sorry, my stats are rusty but I can definitely help if I understand the formula.</description>
      <pubDate>Sun, 07 Apr 2019 04:19:30 GMT</pubDate>
      <guid>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549092#M8609</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2019-04-07T04:19:30Z</dc:date>
    </item>
    <item>
      <title>Re: Multinomial regression and likelihood ratios</title>
      <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549136#M8617</link>
      <description>&lt;P&gt;Hello, I did email to my advisor what he actually meant. I am afraid I have no idea, but just have this Table. Would you be able to guide me towards the code for Multinomial regression?&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Sun, 07 Apr 2019 22:11:30 GMT</pubDate>
      <guid>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549136#M8617</guid>
      <dc:creator>Sakshi13</dc:creator>
      <dc:date>2019-04-07T22:11:30Z</dc:date>
    </item>
    <item>
      <title>Re: Multinomial regression and likelihood ratios</title>
      <link>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549162#M8623</link>
      <description>PROC LOGISTIC and see the example section, nominal regression, there's a fully worked example in the docs.</description>
      <pubDate>Mon, 08 Apr 2019 04:02:05 GMT</pubDate>
      <guid>https://communities.sas.com/t5/New-SAS-User/Multinomial-regression-and-likelihood-ratios/m-p/549162#M8623</guid>
      <dc:creator>Reeza</dc:creator>
      <dc:date>2019-04-08T04:02:05Z</dc:date>
    </item>
  </channel>
</rss>

