[R] lm for log log

[David Winsemius]

On Jun 20, 2010, at 8:17 PM, (Ted Harding) wrote:

> 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).

Yes, but that was not what was suggested in the OP's description of  
the scatterplot of a and b.

-- 

David Winsemius, MD
West Hartford, CT