Parameter estimates change sign. Hi, any help/advice on this would be greatly appreciated. I counted the number of bats present at a site and want to see if any weather variables correlate with number of bats counted. I expect the parameters most likely to predict number of bats to be wind and temperature and the interaction between wind and temperature. Approximately 70% the count data consists of zeroes which are nights when no bats were counted. I expect number of bats to be high on nights with low winds and high temperature, to be lower during low winds and low temperatures and also lower during high winds and high temperatures. I ran simple Poisson GLMMs with Glimmix with year as a random variable and the overdispersion term _residual_ in the random statement due to a large proportion of zeroes in my count data. When I run a simple Bat Count = Wind model, wind is not significant (p=0.8) and the parameter estimate for wind is negative (as expected). When I run the model with Bat Count = Temperature, Temperature is not significant (p=0.2) and the parameter estimate for Temperature is positive (as expected). When I run the model Bat Count = Wind + Temperature, Wind (p=0.9) and Temperature (p=0.2) are not significant and their parameter estimates are negative and positive respectively (as expected). However, when I run the model Bat Count = Wind + Temperature + Wind*Temperature, Wind (p=0.008), Temperature (p=0.003) and Wind*Temperature (p=0.01) are significant but the parameter estimate for wind is now positive and the parameter estimate for the interaction is negative. However, when I plot the predicted count values of this model against the values for wind, the relationship between bat count and wind is negative even though the parameter estimate is positive. I do not understand why the parameter estimate changes from negative (expected) to positive (not expected) and why Wind and Temperature alone were not significant. Could someone please enlighten me? Thank you
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