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    <title>topic Ordinal Logistic Fit/Marginal Prediction/CI in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Ordinal-Logistic-Fit-Marginal-Prediction-CI/m-p/600374#M80383</link>
    <description>&lt;P&gt;I performed an ordinal logistical fit of my data then selected “Save &amp;gt; Save Expected Value” option from the red triangle menu, which adds a column of values in my data table labeled “Ord Expected”. &lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;I believe the added values are marginal predictions, that is estimated Ys given my Xs. First, is this true? If so, second, how can I get the confidence bounds about the expected Ys?&lt;/P&gt;</description>
    <pubDate>Thu, 08 Jun 2023 16:36:24 GMT</pubDate>
    <dc:creator>JudithS</dc:creator>
    <dc:date>2023-06-08T16:36:24Z</dc:date>
    <item>
      <title>Ordinal Logistic Fit/Marginal Prediction/CI</title>
      <link>https://community.jmp.com/t5/Discussions/Ordinal-Logistic-Fit-Marginal-Prediction-CI/m-p/600374#M80383</link>
      <description>&lt;P&gt;I performed an ordinal logistical fit of my data then selected “Save &amp;gt; Save Expected Value” option from the red triangle menu, which adds a column of values in my data table labeled “Ord Expected”. &lt;SPAN&gt;&amp;nbsp;&lt;/SPAN&gt;I believe the added values are marginal predictions, that is estimated Ys given my Xs. First, is this true? If so, second, how can I get the confidence bounds about the expected Ys?&lt;/P&gt;</description>
      <pubDate>Thu, 08 Jun 2023 16:36:24 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Ordinal-Logistic-Fit-Marginal-Prediction-CI/m-p/600374#M80383</guid>
      <dc:creator>JudithS</dc:creator>
      <dc:date>2023-06-08T16:36:24Z</dc:date>
    </item>
    <item>
      <title>Re: Ordinal Logistic Fit/Marginal Prediction/CI</title>
      <link>https://community.jmp.com/t5/Discussions/Ordinal-Logistic-Fit-Marginal-Prediction-CI/m-p/600473#M80390</link>
      <description>&lt;P&gt;Hi&amp;nbsp;&lt;a href="https://community.jmp.com/t5/user/viewprofilepage/user-id/46649"&gt;@JudithS&lt;/a&gt;,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Welcome in the Community !&lt;/P&gt;
&lt;P&gt;You'll find an answer about what is "Expected Value" here :&amp;nbsp;&lt;A href="https://www.jmp.com/support/help/en/17.0/#page/jmp/options-for-ordinal-fits.shtml" target="_blank" rel="noopener"&gt;Options for Ordinal Fits (jmp.com)&lt;/A&gt;. It's a linear combination of the fitted probabilities of the class : Expected value = prob(1)x1 + prob(2)x2 + prob(3)x3 + ...&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I have reproduced the calculation on an exemple dataset "Salt in Popcorn" with the formula for Expected Value in the column "Calculation Expected Value" :&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-jsl"&gt;:"Prob[1]"n * 1 + :"Prob[2]"n * 2 + :"Prob[3]"n * 3 + :"Prob[4]"n * 4 + :"Prob[5]"n * 5&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I don't know if it's possible to have confidence intervals for the Y's, but you can save probability formulas for each class, as well as quantiles, and look for confidence intervals for the model's parameters.&lt;/P&gt;
&lt;P&gt;I hope this first answer will help you,&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 13 Feb 2023 17:12:29 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Ordinal-Logistic-Fit-Marginal-Prediction-CI/m-p/600473#M80390</guid>
      <dc:creator>Victor_G</dc:creator>
      <dc:date>2023-02-13T17:12:29Z</dc:date>
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