Hi
Thanks for your input. I stand corrected.
The causation is not linear.
We wish to find out which is the cause and under what circumstances. i.e. at
what points along a scale for C, is A the cause and when does B become the
cause, if at all.
As a crude analyses, we assumed that the above is not the case (i.e. either
A causes C all the time or B causes C all the time) and obtained correlation
coefficients using lmFit , however as you rightly mentioned, it is not of
much help to us.
We are trying to find out whether ages of proteins(A) or their rates of
evolution(B) influences parameter C.
There is an obvious correlation between A and B which needs to fulfil the
hypothesis as well.
I am checking out wald.test(eba), HypothesisTesting(fBasics), O8.Tests,
O6.LinearModels(limma) amongst others presently.
Thanks
Lalitha
On 5/2/07, Alberto Monteiro <[EMAIL PROTECTED]> wrote:
>
> Lalitha Viswanath wrote:
> >
> > We are trying to find out, which of A or B cause C
> > i.e. We are hypothesising that C is the effect and
> > either A or B, not both is the cause.
> > (...)
> > I would greatly appreciate any inputs on the best
> > statistcal approach to tackle this problem.
> > I am thinking that we can find correlation
> > coefficients between A and C, and between B and C, but
> > I am not sure this answers the question.
> > Also we do not know whether the correlation between
> > them is linear or non linear.
> >
> If the causation (not the correlation) is not linear,
> then the correlation (which is linear, always) may not
> be the best indicator.
>
> Take, as an extreme case, this:
>
> A <- (-50:50) + 100 * rnorm(101)
> B <- abs((-50):50) + 10 * rnorm(101)
> C <- A^2 / 50 + rnorm(101)
> cor(A, C)
> cor(B, C)
>
> A is obviously the "cause" of C, but B (in some cases)
> is better correlated to C than A to C.
>
> Alberto Monteiro
>
>
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