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.ramad...@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 > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
