similar to: Using at.level() with a MCMCglmm zero-inflated poisson model

When I try to use the following code, I get the error message shown. This is quite confusing to me, insofar as family is a recognized argument for MCMCglmm. Can anyone spot an obvious glitch? model1 <-MCMCglmm(fixed = NoRRVpos ~ Year, random = ~County, family="zipoisson", data=Rabies_Project_Init)Error in MCMCglmm(fixed = NoRRVpos ~ Year, random = ~County, family =

Dear R helpers, running the following code of a glm model of the family poisson, gives predicted values < 0. Why? library(MASS) library(stats) library(mvtnorm) library(pscl) data(bioChemists) poisson_glm <- glm(art ~ fem + mar + kid5 + phd + ment, data = bioChemists, family = poisson) predicted.values = predict(poisson_glm) range(predicted.values) Thank you in advance for any hints.

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

Hi, I've been a bit confused by different wyas we specify random effects in lmer and MCMCglmm i just want to clear something. When I want to look for intersexual genetic correlations in the trait, is it equivalent to treat this trait for opposite sexes as separate traits and include the term idh(trait):animal - to treating this as a single trait and fitting idh(sex):animal? Do these two ways

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

Dear all, I'm using zeroinfl() from the pscl-package for zero inflated Poisson regression. I would like to calculate (aproximate) prediction intervals for the fitted values. The package itself does not provide them. Can this be calculated analyticaly? Or do I have to use bootstrap? What I tried until now is to use bootstrap to estimate these intervals. Any comments on the code are welcome.

Hi to all the people, I'm working with abundance data of some species, but containing too zero values, and the factors are the ones typical in a BACI experiment (Before-and-After-Control-Impact). Thus, these are two fixed factors. As the data does not holds the normality and homogeneity of variances assumptions of clasiccal ANOVA, I'm trying to fit a zero-altered model using the MCMC glmm

Dear MCMCglmm users, I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects. In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for

Dear Jarrod, I have a data set where residual have a heavy-tailed distribution with some extreme residual values and consequently the distribution deviates from the Gaussian one. Is it possible to include an skewed-normal density for the residual in MCMCglmm package? I have done the analysis of this data with both ASReml & MCMCglmm. The results are similar and outcome from MCMCglmm

Dear R-users, I would like to fit a glmm with cross-classified random effects with the function MCMCglmm. Something along the lines: model1<-MCMCglmm(response~pred1, random=~re1+re2, data=data) where re1 and re2 should be crossed random effects. I was wondering whether you could tell me specifying cross-classified random effects in MCMCglmm requires a particular syntax? Are there any

Hi all, I'm hoping I might be able to get some help with some issues specifying priors for MCMCglmm. I'm trying to fit a gaussian glmm using MCMCglmm to a data set with two (correlated) response variables. The response variables are both logit-transformed proportions (there are a few reasons why I've chosen these with gaussian error over binomal glmm, which I won't go into).

Hi all, I am trying to run an lme4 model (logistic regression with mixed effects) in MCMCglmm but am unsure how to implement it properly. Currently, my lme4 model formula looks as follows: "outcome ~ (1 + var1 + var2 | study) + var1 + var2" In English, this means that I am fitting a random effects model, where the intercept, var1 and var2 are jointly distributed according to study.

> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote: > > Hi, > > Thanks in advance for any help on this question. I'm running multinomial > models using the MCMCglmm package. The models have 5 outcome variables > (each with count data), and an additional two random effects built into the > models. The issue is that when I use

Dear R-Helpers, I am having troubles with installing with MCMCglmm package and I get the following error with a package "Matrix" Warning in library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called 'Matrix' Error: package 'Matrix' could not be loaded Execution halted ERROR: lazy loading failed for package

Hi David, Thanks for your comment. I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already know the answer. I will reconsider whether my University's ethics approval would allow me to post the data and update the question if I think

Hi Bert, That was distinctly unhelpful, and your outward hostility to a field you obviously don't understand reveals a regrettable level of ignorance. By the way, my research is Anthropology despite my job title. Michelle On Tue, May 1, 2018 at 2:48 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > 1. (Mainly) Statistical issues are generally off topic on this list. > You

Hi, Thanks in advance for any help on this question. I'm running multinomial models using the MCMCglmm package. The models have 5 outcome variables (each with count data), and an additional two random effects built into the models. The issue is that when I use the following code, the summary only gives me results for four of the outcome variables. Here is the code for my model: m3.random

1. (Mainly) Statistical issues are generally off topic on this list. You might want to try the r-sig-mixed-models list instead. 2. However, I think a better answer is to seek local statistical expertise in order to have an extended discussion about your research intent in order to avoid producing yet more irreproducible psychological research. Cheers, Bert Bert Gunter "The trouble with

Hi R-users: Can anyone give an example of giving starting values for MCMCglmm? I can't find any anywhere. I have 1 random effect (physicians, and there are 50 of them) and family="ordinal"? How can I specify starting values for my fixed effects? It doesn't seem to have the option to do so. Thanks, Ping

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl