



> Date: Thu, 23 Jun 2011 15:41:25 -0700
> From: jmo101@student.canterbury.ac.nz
> To: r-help@r-project.org
> Subject: Re: [R] Time-series analysis with treatment effects - statistical approach
> 
> 
> Mike Marchywka wrote:
> > 
> >> I discovered a way to do repetitive tasks that can be concisely specified
> >> using
> >> something called a computer.  
> 
> Now that's funny :)

well, there is a point to that and that is that with cheap computations you can
do different analyses than you did in the past. 


> 
> There were not controlled tests. It was a field experiment testing the
> effects that various pavement designs have on underlying soil moisture. Two
> designs incorporated a porous pavement surface course, while two others were
> based on standard impervious concrete pavement...the control was just bare,
> exposed soil. 
> 
> As you can see from the graph, the control responds quickly to rainfall
> events, but dries out quickly as well due to evaporation. The porous
> pavement allows for quick infiltration of precipitation, while the
> impervious pavement eventually allows infiltration of rainfall, but it's
> delayed. 
> 
> My objective is to be able to differentiate between the pavement treatments,
> such that I can state with statistical confidence that porous pavements
> affects underlying soil moisture differently than impervious pavements. 
> 
> I think this is obvious just looking at it, but I wanted to be able to back
> it up with stats. What I'd done previously is to average by week. But as  I
> mentioned, I thought that an anova table with 104 rows relating to each week
> was a poor way of analyzing the data. But that being said, it effectively
> allows me to check for treatment-related differences. 

I don't think we've mentioned R in the past few posts but I guess pointing
people to useful things that R can do is not too big a problem and
if you have ever dealt with "analysis for the sake of rationalization"
you can appreciation that is a huge problem :)  Generally you'd
like to have reproducible results and if you don't have IID ( stationary
parameters of the population you wish to characterize) you
are not even asking a good question about the system.  It may
be helpful as quick check of something but otherwise difficult to
interpret- do your results mean these things are "different" in the
desert? You appear to have data points from a bunch of different situations.
After the fact selection is often helpful but generally stats people frown
on that as "backing up" anything ( unless it supports sponsor's opinion LOL). 

http://www.itl.nist.gov/div898/handbook/prc/section4/prc432.htm


I guess I'd either go with dynamic model or convert into dollars and then
see if you have clinically and statistically significant differences in things of relevance.



> 
> Thanks for the suggestions to date. Maybe the more I explain what I'm trying
> to achieve, the more focussed the suggestions will be. The vaguer the
> question, the broader the response, right?
> 
> Thanks again,
> Justin
> 
> --
> View this message in context: http://r.789695.n4.nabble.com/Time-series-analysis-with-treatment-effects-statistical-approach-tp3615856p3621179.html
> Sent from the R help mailing list archive at Nabble.com.
> 
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