Thanks Scott,
That does help to clarify things.
So if a covariate is highly skewed, extreme values will be more influential.
And this can be reduced through a transformation (which can be justified) or
through other techniques (e.g. bootstrapping).
Cheers
Sam
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Hi again Sam,
I think that you have it. Extreme values will have more influence, due to their placement in covariate space. This is often countered with
transformation (of the covariates) but I tend to think that altering your data for the sake of the model is the wrong way around.
The only time one might always consider using a transformation on a
response is when it's a ratio. There are 2 reasons for this. Firstly, many
methods will favour the part of the ratio that is above 1 since it is
larger. And secondly ratios aren’t symmetric and are dependent on how we
define it
Dear R-sig-ecology,
I have spent some time trawling the internet, and seem to come across slight
conflicting advice regarding the standardisation and transformation of
variables prior to multiple regression analysis (e.g. LM, LME/GLS, GLM, GLMM,
GAM, GAMM). I searched the archives here and I
Dear Sam,
I hear your concern and I sympathise. The reason for the conflicting advice, in my opinion, is partly historical and partly due to academic
heredity. When people first started doing statistical analyses, they didn't have computers and all calculations had to be done by hand. This,
