<?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 Adjusted growing curves - repeated and multi trait model...PROGRAMING in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Adjusted-growing-curves-repeated-and-multi-trait-model/m-p/238024#M12611</link>
    <description>&lt;P&gt;Good night:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I am working in some groiwing models, and as you know there is an famous model called GOMPERTZ, well i'm trying to combine many informations containes in one data set, to obtain a the the weight estimates and dont use many outputs, just one.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;My idea (i hope you to correct me) is compose just one comand with al the variables like this:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;proc nlin data=have plots=all method=marquardt;&lt;BR /&gt;&lt;STRONG&gt;do;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt; parms a0 = 1 b0 = -0.5 = -0.25;&lt;/STRONG&gt;&lt;BR /&gt;&lt;STRONG&gt;where age_in_days &amp;gt;= 0 and age_in_days &amp;lt;= 40;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;end;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;do;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;parms a0 = 2 b0 = -2 = -0.5;&lt;/STRONG&gt;&lt;BR /&gt;&lt;STRONG&gt;where age_in_days &amp;gt;= 40 and age_in_days &amp;lt;= 80;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;end;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;do;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;parms a0 = 3 b0 = -4 = -1;&lt;/STRONG&gt;&lt;BR /&gt;&lt;STRONG&gt;where age_in_days &amp;gt;= 80 and age_in_days &amp;lt;= 120;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;end;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;model weight_in_Kilograms=a0*exp(exp(b0)*(exp(b1*age_in_days)-1)/b1);&lt;BR /&gt;id sample;&lt;BR /&gt;output out=Estim p=pred r=resid parms=a0 b0 b1;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Thank you so much&lt;/P&gt;</description>
    <pubDate>Sun, 06 Dec 2015 22:33:18 GMT</pubDate>
    <dc:creator>jonatan_velarde</dc:creator>
    <dc:date>2015-12-06T22:33:18Z</dc:date>
    <item>
      <title>Adjusted growing curves - repeated and multi trait model...PROGRAMING</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Adjusted-growing-curves-repeated-and-multi-trait-model/m-p/238024#M12611</link>
      <description>&lt;P&gt;Good night:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I am working in some groiwing models, and as you know there is an famous model called GOMPERTZ, well i'm trying to combine many informations containes in one data set, to obtain a the the weight estimates and dont use many outputs, just one.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;My idea (i hope you to correct me) is compose just one comand with al the variables like this:&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;proc nlin data=have plots=all method=marquardt;&lt;BR /&gt;&lt;STRONG&gt;do;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt; parms a0 = 1 b0 = -0.5 = -0.25;&lt;/STRONG&gt;&lt;BR /&gt;&lt;STRONG&gt;where age_in_days &amp;gt;= 0 and age_in_days &amp;lt;= 40;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;end;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;do;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;parms a0 = 2 b0 = -2 = -0.5;&lt;/STRONG&gt;&lt;BR /&gt;&lt;STRONG&gt;where age_in_days &amp;gt;= 40 and age_in_days &amp;lt;= 80;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;end;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;do;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;parms a0 = 3 b0 = -4 = -1;&lt;/STRONG&gt;&lt;BR /&gt;&lt;STRONG&gt;where age_in_days &amp;gt;= 80 and age_in_days &amp;lt;= 120;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;&lt;STRONG&gt;end;&lt;/STRONG&gt;&lt;/P&gt;
&lt;P&gt;model weight_in_Kilograms=a0*exp(exp(b0)*(exp(b1*age_in_days)-1)/b1);&lt;BR /&gt;id sample;&lt;BR /&gt;output out=Estim p=pred r=resid parms=a0 b0 b1;&lt;BR /&gt;run;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;Thank you so much&lt;/P&gt;</description>
      <pubDate>Sun, 06 Dec 2015 22:33:18 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Adjusted-growing-curves-repeated-and-multi-trait-model/m-p/238024#M12611</guid>
      <dc:creator>jonatan_velarde</dc:creator>
      <dc:date>2015-12-06T22:33:18Z</dc:date>
    </item>
    <item>
      <title>Re: Adjusted growing curves - repeated and multi trait model...PROGRAMING</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Adjusted-growing-curves-repeated-and-multi-trait-model/m-p/241256#M12748</link>
      <description>&lt;P&gt;Again, this may be a simple one. &amp;nbsp;Make sure that you have initial values for all of the parameters. &amp;nbsp;In the code here, you have&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;parms a0 = 1 b0 = -0.5 = -0.25;&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;This will lead to problems. &amp;nbsp;I think you want:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;STRONG&gt;parms a0 = 1 b0 = -0.5 &amp;nbsp;b1= -0.25;&lt;/STRONG&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;However, I'm still not sure if this will run. &amp;nbsp;You may need to embed macro code to get to the various sections.&lt;/P&gt;&lt;P&gt;But that still isn't my major concern. &amp;nbsp;This piecewise fitting will not yield a continuous growth curve, as you have no equations/variables designed to maintain the continuity. &amp;nbsp;And without them, it would be a stroke of luck if the model converged. &amp;nbsp;For an example of continuity based equations/variables, look at the first example in the PROC NLIN documentation (Segmented Model). &amp;nbsp;There are both continuity and smoothness conditions presented. &amp;nbsp;You will need to symbolically calculate the necessary additional variables and add them into the code to make this work.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Now if you have defined interventions at the time points, you could fit separate models, &amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Steve Denham&lt;/P&gt;</description>
      <pubDate>Wed, 30 Dec 2015 18:11:02 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Adjusted-growing-curves-repeated-and-multi-trait-model/m-p/241256#M12748</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2015-12-30T18:11:02Z</dc:date>
    </item>
  </channel>
</rss>

