<?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 Creating DOE (and fitting) for non-linear response in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Creating-DOE-and-fitting-for-non-linear-response/m-p/756730#M93824</link>
    <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am still new to JMP but I'm trying to determine the best way to model a non-linear system (generating DOE + model fitting).&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Context: Long story short, I have a large experimental space that I am trying to model (10 discrete variables + 10 continuous variables). Before I jump into my full experiment I'm working with a subset (1 discrete variable + 5 continuous variables) to make sure I'm modeling my system correctly before expanding my experimental space.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I started by generating a DOE with a simple model containing main + 2nd Interaction terms + 2nd Power terms. After running the DOE I have a model that fit nicely where all effects are significant.&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_0-1716234401980.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64418iB1E1564BB9188DF2/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_0-1716234401980.png" alt="EndogenousCame1_0-1716234401980.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;As I play with the `Prediction Profiler` I know my model is over fitting certain sections of the data which are causing predictions that are not physically possible. My goal is to make a model that's as accurate as possible over my entire experimental space. Please see my screen shots below explaining some edge cases that are not being modeled accurately:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_0-1716236507266.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64423i727CCDC80C49D3CD/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_0-1716236507266.png" alt="EndogenousCame1_0-1716236507266.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_1-1716236515894.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64424i81F07AD0F12256DD/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_1-1716236515894.png" alt="EndogenousCame1_1-1716236515894.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_2-1716236527212.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64425i6D8637D2E54FAF2E/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_2-1716236527212.png" alt="EndogenousCame1_2-1716236527212.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Summary of where I currently stand:&lt;/P&gt;&lt;P&gt;- I know there is statistically significant curvature in my response (based on my first DOE)&lt;/P&gt;&lt;P&gt;- I know from physics that the response should asymptotically approach 0 as my continuous variables go to infinity (impossible for response to be negative)&lt;/P&gt;&lt;P&gt;- There are possibly some interaction terms but I'm wondering if those terms are also being used to overfit the curvature in my response. I need a bit more evidence before I believe there are significant interaction terms.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;My current model is accurate in 90% of scenarios, but I know there are some edge cases being generated because quadratic terms can't accurately model an asymptotic response.&amp;nbsp;I assume that if I added more data points near the minima in the quadratic region the model would smooth out and more closely match reality but I want to minimize the number of data points required to get a good model (especially before I add more variables).&amp;nbsp;What is the correct way to generate a DOE for an asymptotic response and once the DOE is generated, how do you model this non-linear response properly?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks!&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
    <pubDate>Mon, 20 May 2024 20:22:15 GMT</pubDate>
    <dc:creator>EndogenousCame1</dc:creator>
    <dc:date>2024-05-20T20:22:15Z</dc:date>
    <item>
      <title>Creating DOE (and fitting) for non-linear response</title>
      <link>https://community.jmp.com/t5/Discussions/Creating-DOE-and-fitting-for-non-linear-response/m-p/756730#M93824</link>
      <description>&lt;P&gt;Hello,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I am still new to JMP but I'm trying to determine the best way to model a non-linear system (generating DOE + model fitting).&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Context: Long story short, I have a large experimental space that I am trying to model (10 discrete variables + 10 continuous variables). Before I jump into my full experiment I'm working with a subset (1 discrete variable + 5 continuous variables) to make sure I'm modeling my system correctly before expanding my experimental space.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I started by generating a DOE with a simple model containing main + 2nd Interaction terms + 2nd Power terms. After running the DOE I have a model that fit nicely where all effects are significant.&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_0-1716234401980.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64418iB1E1564BB9188DF2/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_0-1716234401980.png" alt="EndogenousCame1_0-1716234401980.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;As I play with the `Prediction Profiler` I know my model is over fitting certain sections of the data which are causing predictions that are not physically possible. My goal is to make a model that's as accurate as possible over my entire experimental space. Please see my screen shots below explaining some edge cases that are not being modeled accurately:&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_0-1716236507266.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64423i727CCDC80C49D3CD/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_0-1716236507266.png" alt="EndogenousCame1_0-1716236507266.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_1-1716236515894.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64424i81F07AD0F12256DD/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_1-1716236515894.png" alt="EndogenousCame1_1-1716236515894.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&lt;span class="lia-inline-image-display-wrapper lia-image-align-inline" image-alt="EndogenousCame1_2-1716236527212.png" style="width: 400px;"&gt;&lt;img src="https://community.jmp.com/t5/image/serverpage/image-id/64425i6D8637D2E54FAF2E/image-size/medium?v=v2&amp;amp;px=400" role="button" title="EndogenousCame1_2-1716236527212.png" alt="EndogenousCame1_2-1716236527212.png" /&gt;&lt;/span&gt;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Summary of where I currently stand:&lt;/P&gt;&lt;P&gt;- I know there is statistically significant curvature in my response (based on my first DOE)&lt;/P&gt;&lt;P&gt;- I know from physics that the response should asymptotically approach 0 as my continuous variables go to infinity (impossible for response to be negative)&lt;/P&gt;&lt;P&gt;- There are possibly some interaction terms but I'm wondering if those terms are also being used to overfit the curvature in my response. I need a bit more evidence before I believe there are significant interaction terms.&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;My current model is accurate in 90% of scenarios, but I know there are some edge cases being generated because quadratic terms can't accurately model an asymptotic response.&amp;nbsp;I assume that if I added more data points near the minima in the quadratic region the model would smooth out and more closely match reality but I want to minimize the number of data points required to get a good model (especially before I add more variables).&amp;nbsp;What is the correct way to generate a DOE for an asymptotic response and once the DOE is generated, how do you model this non-linear response properly?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks!&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Mon, 20 May 2024 20:22:15 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Creating-DOE-and-fitting-for-non-linear-response/m-p/756730#M93824</guid>
      <dc:creator>EndogenousCame1</dc:creator>
      <dc:date>2024-05-20T20:22:15Z</dc:date>
    </item>
  </channel>
</rss>

