Hello all, Excellent advice from Nicole Michel. There is a learning curve for GzLM, but it is well worth the effort.
David Schneider http://www.mun.ca/biology/dschneider/b7932/ Quoting "Michel, Nicole L" <[email protected]>: > Hi Alan et al., > > Generalized Linear Mixed Models (not to be confused with General Linear Mixed > Models) are designed for exactly this sort of data. The Generalized form > lets you define the distribution to be whatever you want it to be. With a > count variable like this, you should start out with either a negbin or > poisson distribution and a log link, and use AIC (or AICc, depending on your > sample size) to choose the best-fitting model. However, in recent analyses I > ran using count data as dependent variables, I actually found a log > distribution with either a log or identity (=normal) link to have the best > fit. FYI, if you're using a log link and/or distribution and have any '0' > values, you will need to add 1 to each value prior to running the models to > avoid the log(0) problem. > > SPSS has the capability to run Generalized Linear Mixed Models, as do both > SAS (Proc GLIMMIX) and R. > > Best, > Nicole Michel > > ********************************************************* > Nicole Michel > PhD Candidate > 4060 Stern > Dept. of Ecology and Evolutionary Biology > 400 Boggs > Tulane University > New Orleans, LA 70118 > Fax: 504-862-8706 > ********************************************************* > > ________________________________________ > From: Ecological Society of America: grants, jobs, news > [[email protected]] on behalf of Alan Griffith (agriffit) > [[email protected]] > Sent: Thursday, July 14, 2011 9:39 AM > To: [email protected] > Subject: [ECOLOG-L] Non-parametric statistics > > Hello all, > > I have been searching for some advice on appropriate non-parametric > statistics for the analysis of a dependent variable that fails normality and > homogeneity assumptions under both sqrt and ln transformations. > > First I will describe the dataset. The data are from a field sample. I have > 4 years of data from the same set of ecological populations. The number of > populations varies year to year. The number of individuals sampled in a > population may have varied within and among years. > > Here is a description of the model I would like to implement. Letâs say > the Dependent Variable is # seeds eaten / plant. So, I want to implement > individual plant nested within population (i.e. a mixed model with > population identifier as random variable or SUBJECT(PopID)). YEAR is a > categorical independent variable, Population Size is one continuous > independent variable. Total # Seeds produced / plant is another continuous > independent variable. I would also like to test interactions. > > As I said before, I was not successful in transforming my dependent variable > using my standard choices (ln and sqrt). I had found references to using > rank transformed data in an ANOVA / ANCOVA model, but this was rejected by a > reviewer. I am familiar with simple nonparametric tests like Kruskal-Wallis, > but I do not see how to preserve the complex model with such tests. > > My first hope is to find a method, generally accepted by ecologists, that is > easily implemented in SPSS. If this is not possible, I can explore more > complicated analyses with the help of my campus math / stats consultant. > > Thanks for you advice. > > | / \ | Alan B. Griffith, PhD > \ \ Ì Ì / / Associate Professor > \ \( )/ / Department of Biological Sciences > \ ( ) / University of Mary Washington > /( )\ (540) 654-1422 > / / ( ) \ \ [email protected] > / | ¦¦ | \ > | | >
