[R] lm for log log

[(Ted Harding)]

On 20-Jun-10 19:54:02, David Winsemius wrote:
> On Jun 20, 2010, at 1:38 PM, Ekaterina Pek wrote:
>> Hi, Ted.
>> Thanks for your reply. It helped. I have further a bit of questions.
>>
>>> It may be that lm(log(b) ~ log(a)) is, from a substantive point of  
>>> view, a more appropriate model for whetever it is than lm(b ~ a).
>>> Or it may not be. This is a separate question. Again, Spearman's
>>> rho is not definitive.
>>
>> How one determines if one linear model is more appropriate than  
>> another ?
>> And : linear model "log(b) ~ log(a)" is okay ? I hesitated to use such
>> thing from the beginning, because it seemed to me like it would have
>> meant a nonlinear model rather than linear.. (Sorry, if the question
>> is stupid, I'm not that good at statistics)
> 
> Your earlier description of the plots made me think both "a" and "b"  
> were right-skewed. Such a situation (if my interpretation were  
> correct) would seriously undermine the statistical validity of an  
> analysis like lm(a ~ b) .
> -- 
> David Winsemius, MD

That doesn't follow. If b is linearly related to a: b = A + B*a + error,
and if the distribution of a is highly skewed, then so also will be
the distribution of b, even if the error is a nice Gaussian error
with constant variance (and small compared with the dispersion
of a & b).

Ted.

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Date: 21-Jun-10                                       Time: 01:17:34
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