[R] variable scale and transform confusion with glmm

[Bert Gunter]

This list is about R programming not statistics, so your post is OT.
Try stats.stackexchange.com instead.

However, given your admitted statistical ignorance, I think you need a
local consultant to lead you through the statistical wilderness, not a
remote internet list. Note that, e.g. "which base" to use for logs,is
always irrelevant (other than as a matter of convention, possibly).

Cheers
Bert


Bert Gunter

"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Sat, May 13, 2017 at 7:33 PM, Sharada Ramadass
<sharada.ramadass at gmail.com> wrote:
> Hello,
>   I am a complete newbie to GLMM and R. I do understand some bit of
> statistics though I am in no-way a core statistician. So, here are my
> doubts and I would really appreciate if someone can provide some
> inputs.
> I have looked up for prior responses on various lists and could not
> come up with satisfactory results that clear my confusion.
> 1. My problem is an ecological problem and I am trying to model growth
> rate in trees as a response to various predictors (fixed and random).
> So far, so good.
> 2. Literature tells me that people use RGR (relative growth rate) to
> look at growth to account for girth size classes.
> 3. My AGR or RGR are very small values (mathemetically in terms of
> numbers) since my timeline for the data is very short. That is my
> limitation.
> 4. Some predictors have large values (orders of magnitude,
> mathematically) while some other others have smaller values.
> 5. So I have very small values for my growth rate, very large values
> for some predictors and all the other predictors are in a similar
> range of values, mathematically.
>
> Here are my questions:
> 1. Does using AGR (absolute growth rate) introduce any bias or
> inflation in the model if we use AGR instead of RGR? One paper (stoll
> 1990) did mention the use of AGR over RGR to avoid skewness.
> 2. I get 'large variance' errors when running lmer on the model with
> the raw data (both response and predictors). Is that a problem?
> 3.If I had to transform the data, should I transform it for all
> predictors and response (independent of which ones are extreme in
> their values in orders of magnitude)?
> 4. If I did apply some kind of transformation, how do you interpret
> the parameter estimates? Do you need to undo the transformation to get
> correct values? Some posts seem to indicate you need to un-transform
> the results.
> 5. For transformation/scaling, I am confused as to what should be
> done. Some posts suggested simply scaling the variables up/down my
> multiplicative factors. Again should this be done for all predictors?
> If done for only select few, do we need to interpret their parameter
> estimates differently?
> 6. The scale function in R has also been suggested as a way to do the
> scaling. This seems to center the mean and not necessarily have just a
> multiplicative effect? Is this the function to use for transform?
> Again, only for some variables or for all?
> 7. Can the response alone be transformed (log or scale) and results
> interpreted as-is?
> 8. Is there a certain log transform only that should be applied (to
> which base)? Again, some posts indicate you can transform to base 10
> or natural log while others indicate log transform is natural log
> only.
>
> Thanks and Regards,
> Sharada
>
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