similar to: How to write random effect in MCMCglmm

I am working with mapping ActiveRecord onto a legacy database. All is going well (using set_table_name, set_primary_key, etc), but I can''t get my many<=>many relationships to work. My contrived example of the situation: Student has_and_belongs_to_many Teachers. This is mapped via table student2teacher where the foreign keys are studentId and teacherId. The primary key in Student

Hi, I 'm a novice user of R statistics and my hands-on experience with it is minimal. I want to create a table for my MBA course assignment that looks like the ones that SPSS and MS Excel produces ,the data that the table has to include are the following : > table(agec) agec 1 2 3 749 160 32 > x=table(agec) > x agec 1 2 3 749 160 32 > > prop.table(x) agec

> Dear All, > > I have built a survival cox-model, which includes a covariate * time interaction. (non-proportionality detected) > I am now wondering how could I most easily get survival predictions from my model. > > My model was specified: > coxph(formula = Surv(event_time_mod, event_indicator_mod) ~ Sex + > ageC + HHcat_alt + Main_Branch + Acute_seizure +

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Hi everyone, I need to get a between-component variance (e.g. random effects Anova), but using lmer I don't get the same results (variance component) than using random effects Anova. I am using a database of students, clustered on schools (there is not the same number of students by school). According to the ICC1 command, the interclass correlation is .44 > ICC1(anova1) [1] 0.4414491

This question comes up periodically, probably enough to give it a proper thread and maybe point to this thread for reference (similar to the 'conservative anova' thread not too long ago). Moving from lme syntax, which is the function found in the nlme package, to lmer syntax (found in lme4) is not too difficult. It is probably useful to first explain what the differences are between the

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Hello R-Helpers, I was trying to use the lmList function to get the lmList graphic similar to Pinheiro and Bates (pg 33). I did not have a problem creating the graphic when I used the Orthodont data frame or 2 other data sets when there are no missing values. My data has missing values. Do I need to remove the missing values before the lmList function will work? for a small example: > a

Dear list In HLM, one can specify a covariate at one of the "levels". For example, if the data structure are repeated observations nested within students nested within schools, school size might be a covariate that is used at level 3, but not at the other levels. In HLM this is rather easy to do. However, how can one specify a covariate in R for only one of the levels? I have a

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

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

With binomial/binary responses (0|1) running MCMCglmm with family="multinomial" terminates with Error in if (nJ < 1) { : missing value where TRUE/FALSE needed with family="categorical" there are no errors I have not looked in the code, do I need format the responses TRUE/FALSE , or is this just a bug? -- H?vard Wahl Kongsg?rd

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.

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 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

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 =

On 02/05/18 09:53, Michelle Kline wrote: > Hi Bert, > > That was distinctly unhelpful Not if you actually follow Bert's advice. > and your outward hostility to a field you > obviously don't understand reveals a regrettable level of ignorance. I didn't see any hostility to any field. Bert, like many of us, objects to people blithely and arrogantly applying possibly

Hi, my question is probably amateurish but I can't seem to find the answer anywhere. In what metric are the MCMCglmm package's posterior means for family = "categorical"? I suppose that they can't be odds ratios and probabilites as my numbers are outside their bounds. So I'm thinking ? are they just basic regression coefficients conceptually equal to those obtained by

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

I have a question for users of MCMCglmm that have experience implementing the zero-inflated poisson model. I find that the documentation, and previous questions, do not offer a lot of clear guidance on specifying and interpreting the zipoisson model. In particular, I see a lot of zero-inflated poisson examples that use the at.level(trait, x):variableName syntax. Specifically, the MCMCglmm