I'm looking for some help with data analysis. Briefly, here is the
experimental design. My student wanted to know if the appearance of a
recycling container would affect the likelihood that a person would place a
recyclable bottle in that container. So, she designed two types of
containers (experim
On Mon, 23 Apr 2007 12:50:48 -0400, Lucy <[EMAIL PROTECTED]> wrote:
>I'm working with some percent cover data from plots that have been
measured
>annually for the past five years. In several plots and during some years
>there is little to no vegetation coverage, so the data are heavily skewed;
Yes - this alleviates the assumption of normality (although I am not
sure if I would classify % cover as binomial/logit). This biggest
hurdle for generalized mixed (or the usual mixed models) for Lucy,
though, is how to generate tests of her main effects. I don't know
what SAS is doing these days
The best option in SAS is using 'PROC GLIMMIX' and define an appropriate
'DISTribution' and a related 'LINK' function.
Bahram Momen
Environmental Science & Statistics
1108 H.J. Patterson Hall
Environmental Science & Technology Dept.
University of Maryland
College Park, MD 20742
301 405 1332, [EM
ct: Re: [ECOLOG-L] nonparametric repeated measures
How about to transform the data using LOG?
jiazy
2007-04-24
[EMAIL PROTECTED]
In response to:
I'm working with some percent cover data from plots that have been measured
annually for the past five years. In several plots and during some yea
Hi Lucy - although I would recommend a mixed model for a variety of
reasons (in particular, you can model heteroscedasticity), it does
still assume normality. So, the mixed model does not necessarily
solve issues of "nonparametric data" (I think you mean "nonnormal").
As I see it, you have a coupl
How about to transform the data using LOG?
jiazy
2007-04-24
[EMAIL PROTECTED]
In response to:
I'm working with some percent cover data from plots that have been measured
annually for the past five years. In several plots and during some years
there is little to no vegetation coverage, so the dat
I'm working with some percent cover data from plots that have been measured
annually for the past five years. In several plots and during some years
there is little to no vegetation coverage, so the data are heavily skewed;
the common transformations (log, square root) haven't worked. Is there
