Hi,

I am using mixed models to test for differences between a number of
variables. For all variables, I have repeated measurements from individuals
(multiple seasons) and 2 different study sites, so I am using a mixed-model
approach with individual as subject (random effect) to test for differences
between sites and seasons.  Furthermore, I have unbalanced data, since not
all individuals are present each season.

My problem is that I have 2 variables that are cosines – which means that
they are in the interval -1 to 1. One of the variables have many ties (many
1s) whereas the other doesn’t – however, no matter how I transform the data,
it is not normal. Should I use a nonparametric test? And if so, which one
resembles the mixed model? I have been looking online, and the only one that
I find comes close is the Friedman’s test. But here I can’t use individual
as random effect. Can anyone help me?

Thanks,

Lene

 

 

 

___________________________



Lene Jung Kjær

Trekronervej 9A, 1.tv., Rostved

8410 Rønde, Danmark

Tlf: 29 86 96 14

email: ljkjar@hotmail.com 




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