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    <title>topic Re: Fit Multiple non linear Y responses in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44606#M25547</link>
    <description>&lt;P&gt;Hi Bill,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks for your suggestion. I have used TanH based neural network models before, but had not considered it in this context. I will try it out.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks to the other repliers as well!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Frederik&lt;/P&gt;</description>
    <pubDate>Fri, 15 Sep 2017 07:16:28 GMT</pubDate>
    <dc:creator>frederikaidt</dc:creator>
    <dc:date>2017-09-15T07:16:28Z</dc:date>
    <item>
      <title>Fit Multiple non linear Y responses</title>
      <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44505#M25518</link>
      <description>&lt;DIV&gt;Hi all,&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&lt;SPAN&gt;I want to do a Fit model with a least squares regression and I want to include the effect of the dilution of antibody which follows more or less a sigmoid model. I could of course include dilution up to to dilution^4 as effects, but a polynomial model doesn’t really give the output I want since I would like the response plateau from a sigmoid model to be present. Essentially, I want to do the same as a non-linear fit using the fit non-linear platform, but include other terms such as interaction terms. Would this be possible in the context of the least-squares model and if so, how would I go about doing this?&amp;nbsp;&lt;/SPAN&gt;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;Best regards,&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;Frederik&lt;/DIV&gt;</description>
      <pubDate>Thu, 14 Sep 2017 09:47:36 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44505#M25518</guid>
      <dc:creator>frederikaidt</dc:creator>
      <dc:date>2017-09-14T09:47:36Z</dc:date>
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    <item>
      <title>Re: Fit Multiple non linear Y responses</title>
      <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44515#M25522</link>
      <description>&lt;P&gt;You should see Analyze &amp;gt; Specialized Modeling &amp;gt; Fit Curve for a platform that is better suited to the logistic curves. See Help &amp;gt; Books &amp;gt; Predictive and Specialized Modeling for a chapter about Fit Curve. It is loaded with help and examples.&lt;/P&gt;</description>
      <pubDate>Thu, 14 Sep 2017 13:37:02 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44515#M25522</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2017-09-14T13:37:02Z</dc:date>
    </item>
    <item>
      <title>Re: Fit Multiple non linear Y responses</title>
      <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44516#M25523</link>
      <description>&lt;P&gt;My apologies! In my haste, I failed to recognize that your model includes more than one predictor. Yes, you must use a custom model with the Nonlinear platform for that purpose. Use the same book as I mentioned before but see the chapter about Nonlinear.&lt;/P&gt;
&lt;P&gt;Someone else might have an idea about how to use Fit Model to begin with in this case, but I can't think of one at the moment.&lt;/P&gt;</description>
      <pubDate>Thu, 14 Sep 2017 13:42:38 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44516#M25523</guid>
      <dc:creator>Mark_Bailey</dc:creator>
      <dc:date>2017-09-14T13:42:38Z</dc:date>
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    <item>
      <title>Re: Fit Multiple non linear Y responses</title>
      <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44519#M25524</link>
      <description>&lt;P&gt;Without completly understanding your experiment....&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;I might try to fit a non-linear curve (like a 4p or 5p) to each of the samples and the take the parameter for the upper asymptope or the inflection point (ec50) or whatever is relevant and use that as a response in the linear model with the other imputs that were used to effect&amp;nbsp;the dilution curve. &amp;nbsp;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;For example if I was looking at coating, block and wash buffers, and a couple of dilution ranges. I could fit each dilution, get the inflection point and upper plateau from the non-linear fit. and then go to Fit Model and include the four parameters and their interactions as model effects and use the two parameters from the non-linear fits for the responses.&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;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;</description>
      <pubDate>Thu, 14 Sep 2017 14:04:06 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44519#M25524</guid>
      <dc:creator>Byron_JMP</dc:creator>
      <dc:date>2017-09-14T14:04:06Z</dc:date>
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    <item>
      <title>Re: Fit Multiple non linear Y responses</title>
      <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44526#M25527</link>
      <description>&lt;P&gt;Frederik,&lt;/P&gt;
&lt;P&gt;Have you thought about trying Neural Nets? &amp;nbsp;The Tan H function is sigmoidal in nature and you can use as many inputs as you like similar to Fit Model. &amp;nbsp;I would suggest doing a series of fits with different numbers of hidden nodes and then go with the model that is the least complicated, but still meets your prediction/fit criteria.&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;HTH&lt;/P&gt;</description>
      <pubDate>Thu, 14 Sep 2017 17:01:09 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44526#M25527</guid>
      <dc:creator>Bill_Worley</dc:creator>
      <dc:date>2017-09-14T17:01:09Z</dc:date>
    </item>
    <item>
      <title>Re: Fit Multiple non linear Y responses</title>
      <link>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44606#M25547</link>
      <description>&lt;P&gt;Hi Bill,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks for your suggestion. I have used TanH based neural network models before, but had not considered it in this context. I will try it out.&amp;nbsp;&lt;/P&gt;&lt;P&gt;Thanks to the other repliers as well!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Frederik&lt;/P&gt;</description>
      <pubDate>Fri, 15 Sep 2017 07:16:28 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Fit-Multiple-non-linear-Y-responses/m-p/44606#M25547</guid>
      <dc:creator>frederikaidt</dc:creator>
      <dc:date>2017-09-15T07:16:28Z</dc:date>
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