so I conducted an experiment in which I am trying to model the relationship between my response yield [dt/ha] and the predictors soil moisture [%] + weed coverage [%]+ treatment + distance + date and site/plot as random effects. Weed_coverage and soil_moisture are continuous, treatment,distance and date are categorical.
I used three different models:
- a generalized linear mixed model with a beta response
- a linear mixed model
- a generalized linear mixed model with a gamma response.
When I look at the output, all models show significant effects of weed_coverage and treatment b, but the estimates differ in several ways and I don't understand why. I know that the GLMMs use a link function that transform the response in a certain way, but why is the estimate for weed coverage negative for the linear mixed model and positive for the two GLMMs? Also, the Linear Mixed Models has very low estimates in general and the GLMMs do not. Futhermore, the estimates of weed_coverage are much higher for the Gamma-GLMM compared to the Beta_GLMM. Why is that?
lmer(yield ~ soil_moisture+ weed_coverage + distance +
treatment + date+ (1|kettlehole/plot) , data = Korn_beta)-> lmmod
glmmTMB(yield ~ soil_moisture + weed_coverage + distance+
treatment + date + (1|kettlehole/plot) , family = "beta_family",
data = Korn_beta) -> glmm_beta
glmer (yield ~ soil_moisture + weed_coverage + distance +
treatment + date + (1|kettlehole/plot) , family = "Gamma",
data = Korn_beta) -> glmm_gamma
I have no statistical background, so it would be great if someone could explain it to me as easy as possible and without math. :) Thanks a lot
glmmTMB
formula (which includeskettlehole
instead ofdistance
) ? $\endgroup$