[R] model constant relations

[Spencer Graves]

     Have you looked at the correlation of the parameter estimates? 

      In S-Plus, this is automatically produced by summary(lm(...)).  
There is probably a simple, elegant way to get this in R, but I don't 
know it.  Therefore, I produced it as follows: 

 > DF <- data.frame(x=1:12, y=rep(1:6, each=2))
 > fit <- lm(y~x, DF)
 > sumfit <- summary(fit)
 > cov.fit <- sumfit$cov.unscaled
 > seb <- sqrt(diag(cov.fit))
 > cov.fit / outer(seb, seb)
            (Intercept)         x
(Intercept)    1.000000 -0.883176
x             -0.883176  1.000000

I developed this by looking at attributes(fit), then attributes(sumfit), 
etc. 

Is this helpful?
spencer graves

Kyriakos Kachrimanis wrote:

>Dear list members,
>
> this is not an R question and forgive me for using the list for irrelevant
>questions, but this is the only place I know where I can find some good
>statisticians and I need an expert opinion.
>There is this power law kinetic model of the form:
>M=kt^n
>where t is the time, M is the fraction of drug released, k is the rate
>constant and n is an exponent related to the mechanism of release.
>I fit this equation to 100 different datasets (by linear regression, after
>logarithmic transformation) and then I plot n vs k. What I get is a decay
>pattern of n as k increases.
>QUESTION 1: Is some theoretical reason for this kind of relation. Is it
>expected? Is it "normal" that model parameters (intercept and slope in the
>linearised form) should be correlated?
>QUESTION 2: Is it theoretically sound to compare the magnitude of constants,
>k, that correspond to different exponents, n? (k has dimensions of [time]^-n
>so we are comparing quantities with different physical interpretation).
>Practically speaking, does k=0.2 with an exponent n=0.5 mean that we have
>the same release rate as in the case of k=0.2 but with n=1.0?
>
>Thank you very much in advance.
>
> Kyriakos.
>
>
>
>---
>
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