<?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: Using estimates from proc FMM to create predicted scores in validation data in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333207#M17598</link>
    <description>&lt;P&gt;I would use the &lt;A href="http://blogs.sas.com/content/iml/2014/02/17/the-missing-value-trick-for-scoring-a-regression-model.html" target="_self"&gt;"missing value trick"&lt;/A&gt; and let PROC FMM generate the predicted values itself.&lt;/P&gt;
&lt;P&gt;The code would look something like this (NOT TESTED):&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;/* 1. Concatenate the original data with the score data */
data C;
set training validation(in=v rename=(y=OrigY));
if v then do;
   y = .;             /* y=. for all obs in validation data */
   type = "Validation";
end;
else
   type = "Training  ";
run;
 
/* 2. Run a regression. The model is fit to the original data. */ 
proc fmm data=C;
model y = ...;
output out=Pred residual pred;
quit;
&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;The scored validation data set is the one WHERE type="Validation";&lt;/P&gt;</description>
    <pubDate>Wed, 15 Feb 2017 22:35:34 GMT</pubDate>
    <dc:creator>Rick_SAS</dc:creator>
    <dc:date>2017-02-15T22:35:34Z</dc:date>
    <item>
      <title>Using estimates from proc FMM to create predicted scores in validation data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333193#M17597</link>
      <description>&lt;P&gt;I&amp;nbsp;used a FMM model for my data with a continuous outcome variable (ordered from 0-1000 with almost 93% valued being 0) using the followng statements:&amp;nbsp;&lt;/P&gt;&lt;P&gt;proc fmm data=training;&lt;BR /&gt;model y&amp;nbsp;= x1 x2 x3 x4/ dist=WEIBULL k=2;&lt;BR /&gt;probmodel &lt;SPAN&gt;x1 x2 x3 x4&lt;/SPAN&gt;&lt;BR /&gt;output out = modelone residual pred;&lt;BR /&gt;run;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am wondering if I can use the beta estimates created using above procedure to calculate predicted scores in the validation data. I have used this method to score estimates from&amp;nbsp;other regression models. So the equation would be:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;data stats;&lt;/P&gt;&lt;P&gt;set validation;&lt;/P&gt;&lt;P&gt;log_y = exp(intercept+b1*x1+b2*x2+b3*x3+b4*x4);&lt;BR /&gt;y&amp;nbsp;= exp(log_y);&amp;nbsp;&lt;/P&gt;&lt;P&gt;run;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Is this a&amp;nbsp;correct method to create predicted scores here? I am new to FMM procedure and after reading lot of articles, it seems to be an appropriate method for 0-inflated data. However, I am not sure how to use that further to create predicted scores and then compare against the observed/actual outcome.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Apprecite your help.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks&lt;/P&gt;&lt;P&gt;Shen&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Wed, 15 Feb 2017 22:06:50 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333193#M17597</guid>
      <dc:creator>ShenBose</dc:creator>
      <dc:date>2017-02-15T22:06:50Z</dc:date>
    </item>
    <item>
      <title>Re: Using estimates from proc FMM to create predicted scores in validation data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333207#M17598</link>
      <description>&lt;P&gt;I would use the &lt;A href="http://blogs.sas.com/content/iml/2014/02/17/the-missing-value-trick-for-scoring-a-regression-model.html" target="_self"&gt;"missing value trick"&lt;/A&gt; and let PROC FMM generate the predicted values itself.&lt;/P&gt;
&lt;P&gt;The code would look something like this (NOT TESTED):&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;/* 1. Concatenate the original data with the score data */
data C;
set training validation(in=v rename=(y=OrigY));
if v then do;
   y = .;             /* y=. for all obs in validation data */
   type = "Validation";
end;
else
   type = "Training  ";
run;
 
/* 2. Run a regression. The model is fit to the original data. */ 
proc fmm data=C;
model y = ...;
output out=Pred residual pred;
quit;
&lt;/CODE&gt;&lt;/PRE&gt;
&lt;P&gt;The scored validation data set is the one WHERE type="Validation";&lt;/P&gt;</description>
      <pubDate>Wed, 15 Feb 2017 22:35:34 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333207#M17598</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2017-02-15T22:35:34Z</dc:date>
    </item>
    <item>
      <title>Re: Using estimates from proc FMM to create predicted scores in validation data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333535#M17615</link>
      <description>&lt;P&gt;Thanks so much! That works.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Just wondering what the equation is to calculate the predicted scores.&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Thu, 16 Feb 2017 19:07:36 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333535#M17615</guid>
      <dc:creator>ShenBose</dc:creator>
      <dc:date>2017-02-16T19:07:36Z</dc:date>
    </item>
    <item>
      <title>Re: Using estimates from proc FMM to create predicted scores in validation data</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333572#M17618</link>
      <description>&lt;P&gt;I don't have time right now, but now that you know the correct predicted values in a data set, you can try various equations until you get the same predictions.&lt;/P&gt;</description>
      <pubDate>Thu, 16 Feb 2017 20:39:17 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Using-estimates-from-proc-FMM-to-create-predicted-scores-in/m-p/333572#M17618</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2017-02-16T20:39:17Z</dc:date>
    </item>
  </channel>
</rss>

