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 doesnt 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 Friedmans test. But here I cant 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: [email protected] [[alternative HTML version deleted]]
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