I have run the following glmm:
mod<-glmer(data=newdata, total_flr_vis ~ treatment + flr_num + (1|individual), family=poisson)
and get this output from
Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod'] Family: poisson ( log ) Formula: total_flr_vis ~ treatment + flr_num + (1 | individual) Data: newdata AIC BIC logLik deviance df.resid 706.8 718.9 -349.4 698.8 148 Scaled residuals: Min 1Q Median 3Q Max -3.1461 -0.8155 -0.3522 -0.2022 14.1669 Random effects: Groups Name Variance Std.Dev. individual (Intercept) 4.066 2.016 Number of obs: 152, groups: individual, 42 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.610176 0.432666 -3.722 0.000198 *** treatmentR -0.457492 0.121054 -3.779 0.000157 *** flr_num 0.037063 0.005064 7.319 2.5e-13 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Correlation of Fixed Effects: (Intr) trtmnR treatmentR -0.117 flr_num -0.416 0.040
But I get the following plot for
I don't understand why the effects plot shows overlapping error bars while the
summary() output tells me that the effect of
treatment is significant. Is there a problem with the model, or is it the plot? How can I troubleshoot this?
Here is the residual plot (which I got using
I'm not totally sure how to interpret this plot, but it does not look random to me, thus I suspect that there is something wrong with the model.
I am happy to post data if someone can tell me where to do that.
Note: I posted this on Stack Overflow and someone suggested that I am misinterpreting the first figure. This is very likely, but, unfortunately, the user did not suggest an alternative way to interpret the figure. If anyone here can do so, I would be very grateful.
Any help would be very welcome.