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. Let’s 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 \ \ ̗ ̖ / / Associate Professor \ \( )/ / Department of Biological Sciences \ ( ) / University of Mary Washington /( )\ (540) 654-1422 / / ( ) \ \ [email protected] / | ¦¦ | \ | |
