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Hi everyone, I recently re-ran a model that I wrote about 5 months ago. The model is a GLMM of the type : m1 <- glmer(response ~ fixed + random effect, data = d, family = "binomial") At that time, everything ran smoothly, with...

Dear list, I am new to this list and I hope it is ok to post here even though I already posted this question on Cross Validated. I am trying to predict the daily amount of waste per person produced...

Dear List, I am writing this here because I am using glmmTMB to run a zero inflated model, but the question is possibly more general, so please feel free to redirect me to another list/help page. I want to...

Dear List I have posted the question below on Cross Validated as well, where you can find the image of the diagnostics plots. https://stats.stackexchange.com/questions/481109/truncated-negative-binomial-hurdle-model-for-continuous-data-glmmtmb I am...

Dear list, I am fitting the mixed effect model: > lmer(log(percapita_day) ~ Type_residuo + (1|boatID), data=all) where percapita_day is a non-negative continuous response variable (on the log scale to have residuals normally distributed), Type_residuo...

Dear Thierry Thanks for your reply. I read a bit about the prediction for a binomial model with success/failures and I have a couple of questions. If I use the predict function with the model you recommended, I obtain...

Dear list. I wonder if anyone has experience in using the DHARMa package for model diagnostics? I am fitting a poisson GLMM of the type glmer(captured ~ treatment + offset(log(Effort)) + (1|Trip_Code), data=x, family="poisson", glmerControl(optimizer...

Dear Salvador, Thank you for the clarification, that was very helpful. Best wishes Alessandra On Mon, Feb 17, 2020 at 9:27 PM Salvador SANCHEZ COLON < salvadorsanchezcolon at prodigy.net.mx> wrote: > CORRECTION > > > Dear Alessandra, > > > My apologies, I msread the...

Hi All Sorry if I am reposting this, I am not sure it got sent to the list... ---------- Forwarded message --------- From: Alessandra Bielli <bielli.alessandra at gmail.com> Date: Wed, Mar 20, 2019 at 10:18 PM Subject: Fixing singularity...

Dear all First of all apologies for the off-topic question and for not respecting the other points. Second, thanks for your advice and opinion I will definitely consult a statistician. Regards, Alessandra On Sun, May 10, 2020 at 4...

Hi Ben That's a good point, my parameters are not multivariate normal. But then, why was this method used in the Salamander example, where data are zero inflated and so, I assume, not multivariate normal? I haven't simulated...

Dear List I am fitting this model using the lme4 package, in order to obtain catch estimates using the predict function m1 <- glmer(Count ~ CE + offset(log(Effort)) + (1|SetYear) +(1|Season) + (1|Lance.N) + (1|Boat.Name) + (1|Observer...

Thanks for the clarifications. So if I understand right, model diagnostics can tell me if my model parameters are multivariate normal. I think they are, see the dharma diagnostic plot attached. The values you requested: > X.cond[1,] (Intercept) Used...

Dear Ben I am trying to make a prediction for the combination of species (L or G) and treatment (control/experiment). I am still confused about the prediction values. I would like to present results as a success rate for...

Hi Ren? To anwer your questions 1. offset is a continuous variable because it's fishing effort, measured in km of net x day. The experiment has a paired design but within the same set it could happen that the...

Dear list I am fitting a poisson model to estimate the effect of a treatment on emergence success of hatchlings. To estimate emergence success, I use number of emerged and an offset(log(total clutch). However, overdispersion was detected: > overdisp...

Dear Ben Thanks for your quick response. Yes, emergence success is usually between 60 and 80% or higher. I am not sure how to use a binomial, if my data are counts? Can you explain why the approximation doesn't...

Dear list, I am fitting this model to predict the dependent variable at the population mode: m1.dczig <- glmmTMB(Res.dia.pers ~ Tipo + (1|ID) , data=all, zi=~ Tipo , dispformula=~ Tipo , family=ziGamma(link ="log")) where Res.dia.pers is...

Hi Phillip Thank you so much for your explanation. I have a couple more questions 1.In my model, the regression coefficients of each one of the categories of my predictor are correlated, but I just have one categorical predictor...

Dear list, I am trying to predict a value and CI for two different treatments from a glmmTMB fitted model using posterior predictive simulations (mvrnorm function in the MASS package) as in the Salamander example, Brooks 2017 appendix B <https...