(This question is somehow related to my previous one)
My aim is to find out about which effect several predictors have on my response variable, I am interested in the direction and magnitude of the effect. I am wondering how I should evaluate the effect and if I can ignore the p-values for the estimates for that matter.
My model is a GLMM (Gamma family with inverse link):
Response ~ P1 + P2 + P3 + P4 + P... + (1|Plot) + (1|Year) + (1|Plant ID)
Now to get an idea of the marginal effects of the predictors I plot them using
Where do I go from here?
Am I okay to ignore p-values from the model and consider the direction and effect of each predictor in order to find out how my response variable is influenced? I. e. can I safely interpret that an increment in P1 or reduction in P2 will increase the response (all other predictors held constant at their means)? How would I deal with and communicate the uncertainty (large confidence intervals)?