[R] relation in aggregated data

[Petr PIKAL]

Thank you

Actually when I do this myself I always try to make day or week averages 
if possible. However this was done by one of my colleagues and basically 
the aggregation was done on basis of campaigns. There is 4 to 6 campaigns 
per year and sometimes there is apparent relationship in aggregated data 
sometimes is not. My opinion is that I can not say much about exact 
relations until I have other clues or ways like expected underlaying laws 
of physics.

Thanks again

Best regards
Petr



Joris Meys <jorismeys at gmail.com> napsal dne 07.07.2010 17:33:55:

> You examples are pretty extreme... Combining 120 data points in 4
> points is off course never going to give a result. Try :
> 
> fac <- rep(1:8,each=15)
> xprum <- tapply(x, fac, mean)
> yprum <- tapply(y, fac, mean)
> plot(xprum, yprum)
> 
> Relation is not obvious, but visible.
> 
> Yes, you lose information. Yes, your hypothesis changes. But in the
> case you describe, averaging the x-values for every day (so you get an
> average linked to 1 y value) seems like a possibility, given you take
> that into account when formulating the hypothesis. Optimally, you
> should take the standard error on the average into account for the
> analysis, but this is complicated, often not done and in most cases
> ignoring this issue is not influencing the results to that extent it
> becomes important.
> 
> YMMV
> 
> Cheers
> 
> On Wed, Jul 7, 2010 at 4:24 PM, Petr PIKAL <petr.pikal at precheza.cz> 
wrote:
> > Dear all
> >
> > My question is more on statistics than on R, however it can be
> > demonstrated by R. It is about pros and cons trying to find a 
relationship
> > by aggregated data. I can have two variables which can be related and 
I
> > measure them regularly during some time (let say a year) but I can not
> > measure them in a same time - (e.g. I can not measure x and respective
> > value of y, usually I have 3 or more values of x and only one value of 
y
> > per day).
> >
> > I can make a aggregated values (let say quarterly). My questions are:
> >
> > 1.      Is such approach sound? Can I use it?
> > 2.      What could be the problems
> > 3.      Is there any other method to inspect variables which can be
> > related but you can not directly measure them in a same time?
> >
> > My opinion is, that it is not much sound to inspect aggregated values 
and
> > there can be many traps especially if there are only few aggregated
> > values. Below you can see my examples.
> >
> > If you have some opinion on this issue, please let me know.
> >
> > Best regards
> > Petr
> >
> > Let us have a relation x/y
> >
> > set.seed(555)
> > x <- rnorm(120)
> > y <- 5*x+3+rnorm(120)
> > plot(x, y)
> >
> > As you can see there is clear relation which can be seen from plot. 
Now I
> > make a factor for aggregation.
> >
> > fac <- rep(1:4,each=30)
> >
> > xprum <- tapply(x, fac, mean)
> > yprum <- tapply(y, fac, mean)
> > plot(xprum, yprum)
> >
> > Relationship is completely gone. Now let us make other fake data
> >
> > xn <- runif(120)*rep(1:4, each=30)
> > yn <- runif(120)*rep(1:4, each=30)
> > plot(xn,yn)
> >
> > There is no visible relation, xn and yn are independent but related to
> > aggregation factor.
> >
> > xprumn <- tapply(xn, fac, mean)
> > yprumn <- tapply(yn, fac, mean)
> > plot(xprumn, yprumn)
> >
> > Here you can see perfect relation which is only due to aggregation 
factor.
> >
> > ______________________________________________
> > R-help at r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide 
http://www.R-project.org/posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
> >
> 
> 
> 
> -- 
> Joris Meys
> Statistical consultant
> 
> Ghent University
> Faculty of Bioscience Engineering
> Department of Applied mathematics, biometrics and process control
> 
> tel : +32 9 264 59 87
> Joris.Meys at Ugent.be
> -------------------------------
> Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php