Eureqa - repeated measures design - Eureqa Group

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Message from discussion Eureqa - repeated measures design
Date: Wed, 25 Apr 2012 22:06:21 -0700 (PDT)
From: Ted Swiecki <phytoresea...@gmail.com>
To: eureqa-group@googlegroups.com
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Subject: Re: Eureqa - repeated measures design
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What I'm interested in is determining if there are relationships between 
various predictors.  Some of these (e.g., weather related) fluctuate from 
year to year across the entire data set (which is essentially of a network 
of plots spread out across a single location).  Other predictors (e.g., 
soil type) are fixed across years but vary between the plots.  And other 
predictors differ between the plots in a given year and change from year to 
year for individual plots (e.g., fire history - only some plots have burned 
in specific years). There are spatial relationships between the plots 
involved here as well, but I'm not even attempting to deal with that in 
this model.  I have a number of outcome variables, which for simplicity are 
considered one at a time. They are essentially independent outcomes, though 
many of them are likely to be correlated with each other. 

I have run a few models and they seem to be working out, but they tend to 
be complex an difficult to interpret.  [This is a topic for a separate 
thread, but a better way to look at the effects of individual variables 
would be a real boon.  Using sliders that allow you to vary the input 
variable and see the effect on the curve is very handy, and is used by 
various analytical packages, e.g., SAS JMP]

The initial models I ran were based on year to year changes in outcome 
variables (outcome_year n - outcome_year n-1).  For those, I treated each 
year as a separate set of 324 points.  I wasn't sure whether that was the 
best way to do that analysis, but I did end up with some reasonable 
models.  


On Wednesday, April 25, 2012 7:42:16 AM UTC-7, Michael Schmidt wrote:
>
> Yeah this depends, do you want to find a single model that captures each 
> location simultaneously? Or find a possibly different model for each? Or 
> find relationships between location? Perhaps you're looking for a dynamical 
> relationship (e.g. the difference or derivative between years) for each? 
> Maybe the average across location would work here?
>
>
> On Sun, Apr 22, 2012 at 1:02 AM, Ted Swiecki <phytoresea...@gmail.com>wrote:
>
>> I had wondered about that tactic.  Will give it a go and see if it
>> works out. A bit of a pain to format in all the blank rows, though.
>>
>>
>> On Apr 19, 5:59 am, L <lew_clay...@yahoo.com> wrote:
>> > Depends on what you want to do.
>> >
>> > If you separate each of the 324 sets of 11 rows with a blank row, it 
>> will treat each of them as a separate grouping.
>> >
>> > If they are further sorted in chronological order within each set of 
>> 11, the delay and moving average building blocks will operate as expected 
>> (if selected)
>> >
>> > Does that help any, or do I misunderstand your goal?
>> >
>> >
>> >
>>
>> --
>> Eureqa Formulize ( http://www.nutonian.com )
>> -------------------------------------------------
>> Unsubscribe: eureqa-group+unsubscribe@googlegroups.com
>> View Group: http://groups.google.com/group/eureqa-group
>>
>
>
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What I'm interested in is determining if there are relationships between va=
rious predictors.&nbsp; Some of these (e.g., weather related) fluctuate fro=
m year to year across the entire data set (which is essentially of a networ=
k of plots spread out across a single location).&nbsp; Other predictors (e.=
g., soil type) are fixed across years but vary between the plots.&nbsp; And=
 other predictors differ between the plots in a given year and change from =
year to year for individual plots (e.g., fire history - only some plots hav=
e burned in specific years). There are spatial relationships between the pl=
ots involved here as well, but I'm not even attempting to deal with that in=
 this model.&nbsp; I have a number of outcome variables, which for simplici=
ty are considered one at a time. They are essentially independent outcomes,=
 though many of them are likely to be correlated with each other. <br><br>I=
 have run a few models and they seem to be working out, but they tend to be=
 complex an difficult to interpret.&nbsp; [This is a topic for a separate t=
hread, but a better way to look at the effects of individual variables woul=
d be a real boon.&nbsp; Using sliders that allow you to vary the input vari=
able and see the effect on the curve is very handy, and is used by various =
analytical packages, e.g., SAS JMP]<br><br>The initial models I ran were ba=
sed on year to year changes in outcome variables (outcome_year n - outcome_=
year n-1).&nbsp; For those, I treated each year as a separate set of 324 po=
ints.&nbsp; I wasn't sure whether that was the best way to do that analysis=
, but I did end up with some reasonable models.&nbsp; <br><br><br>On Wednes=
day, April 25, 2012 7:42:16 AM UTC-7, Michael Schmidt wrote:<blockquote cla=
ss=3D"gmail_quote" style=3D"margin: 0;margin-left: 0.8ex;border-left: 1px #=
ccc solid;padding-left: 1ex;"><div>Yeah this depends, do you want to find a=
 single model that captures each location simultaneously? Or find a possibl=
y different model for each? Or find relationships between location? Perhaps=
 you're looking for a dynamical relationship (e.g. the difference or deriva=
tive between years) for each? Maybe the average across location would work =
here?<br>

<br><br><div class=3D"gmail_quote">On Sun, Apr 22, 2012 at 1:02 AM, Ted Swi=
ecki <span dir=3D"ltr">&lt;<a href=3D"mailto:phytoresea...@gmail.com" targe=
t=3D"_blank">phytoresea...@gmail.com</a>&gt;</span> wrote:<br><blockquote c=
lass=3D"gmail_quote" style=3D"margin:0 0 0 .8ex;border-left:1px #ccc solid;=
padding-left:1ex">


I had wondered about that tactic. &nbsp;Will give it a go and see if it<br>
works out. A bit of a pain to format in all the blank rows, though.<br>
<div><br>
<br>
On Apr 19, 5:59&nbsp;am, L &lt;<a>lew_clay...@yahoo.com</a>&gt; wrote:<br>
&gt; Depends on what you want to do.<br>
&gt;<br>
&gt; If you separate each of the 324 sets of 11 rows with a blank row, it w=
ill treat each of them as a separate grouping.<br>
&gt;<br>
&gt; If they are further&nbsp;sorted in chronological order within each set=
 of 11, the delay and moving average building blocks will operate as expect=
ed (if selected)<br>
&gt;<br>
&gt; Does that help any, or do I misunderstand your goal?<br>
&gt;<br>
&gt;<br>
&gt;<br>
<br>
</div><div><div>--<br>
Eureqa Formulize ( <a href=3D"http://www.nutonian.com" target=3D"_blank">ht=
tp://www.nutonian.com</a> )<br>
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</div></div></blockquote></div><br></div>
</blockquote>
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