Dear All, I am working with an ecological data set wherein I am trying to compare the growth rate of seedlings in plots where an invasive species is present or absent. Repeated measures on seedlings were made every two months across 40 plots of which 20 had the invasive species while the remaining 20 did not. Seedling growth is measured as log(proportion increment in height) per month. I am also interested in looking at how rainfall received between two consecutive growth measurements and seedling habitat preferences affect growth. I came up with the following mixed effects model growth ~ invasive density (2 levels) + seedling habitat preference (3 levels) + rainfall (mm) + all two-way interactions + random intercept on repeatedly measured plots The residuals on this model are highly overdispersed and do not meet the normality criteria, so i decided to use nonparametric bootstrapping (refitting the model with 10000 random subsets of data) to obtain 95% CI on all the fixed effects parameters estimated (I assumed that the CIs non-overlapping with zero indicated significant fixed effects). Apart from the 'intercept', the 'rain' term and the 'invasive density' term, 95% Cis of all other parameters included zero. I am interested in graphically representing only these effects. Since the normality assumption of residuals is not satisfied, is it appropriate to simplify the model using anova (with REML = F)? Or can I create a new, simpler model with just the terms of interest, generate 95% CI for these parameters and use these for graphical representation? Thank you in advance for your help. Geetha Geetha Ramaswami PhD Student Centre for Ecological Sciences, Indian Institute of Science, Bangalore 560012, India
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