That is, we evaluate the gradient with gr=(user's function for gradient) and then with Is preceded by a check of the gradient at the starting values supplied using numDeriv. Optimx uses exactly the same code as optim for BFGS. In "optimx", we should probably change this into a "warning" rather than a "stop".ĭivision of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins UniversityĪnd provide commented, minimal, self-contained, reproducible code. I would check this again separately as follows: This can happen when the gradients are very large. ![]() It is likely that you have correctly coded up the gradient, but still there can be significant differences b/w numerical and exact gradients. for all the models.The gradient check in "optimx" checks if the user specified gradient (at starting parameters) is within roughly 1.e-05 * (1 + fval) of the numerically computed gradient. Yes, I've tried summary(model) and it only gives me the mean, median, etc. There must be an easy code for this but none of my friends seem to know what it is. What I've been using to average the top models: attr(Glucose2,"rank.call")īUT for the top model only, how do I get the Estimates, SE, and z- and p-values for Species, Treatment, Age, and their interactions? Glucose2<-dredge(Glucose1,trace=TRUE,rank=AICc,extra="r.squaredGLMM") Glucose1=glmer(GlucoseCon~Species.f * Treatment.f * Age+(1|NestID.f),GlucoseFile,gaussian) However, there is only one model with a delta AICc < 3 so I just want to extract the estimates, SE, z-, p-values from this top model. For my other analyses, I've been averaging the top models (delta AICc < 3) then extracting the estimates, SE, and z- and p-values for each of the fixed effects. ![]() I am running a GLMM (package: MuMIn) with a dependent variable with a gaussian distribution and several fixed effect variables and a random effect.
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