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Hi Chris, thanks so much for the quick answer. Your comment regarding mlwinsettings(optimat) solved my problem. This option needs to be specified in the model that is estimated with maximum likelihood to generate the initial values and the model that estimates the DIC. In fact, also not specifying t...

Hi there, To compare the model-fit of a single-level and a multilevel mode, I would like to use MCMC estimation for a single-level model to obtian a DIC estimate. Specifically, my model is runmlwin COUNT X, /// level1(gid) /// discrete(distribution(poisson) offset(lexposure)) nopause mlwinsettings(o...

For the sake of completeness, here is my solution. As shown by Ma et al. (2003) and Feng et al. (2005), the likelihood function of Cox models with normal random effects is proportional to the likelihood of such random effects Poisson models. Specifically, Cox models with normal random effects can be...

Hi there,

Is it possible to fit survival models using R2MLwiN? I haven't found any examples.

Many thanks in advance and kind regards,

Benjamin

Thank you so much! This: worked for me.

Hey there, I'm sorry to bother again. Estimating this model: fit <- runMLwiN(y ~ 1 + cx + gx + (1 | id_c) + (1 | p) + (1 | id_g), D = "Normal", data = data.wide, estoptions = list(EstM = 1, debugmode = F, mm = list(NA, list(mmvar = list("p", "p2", "p3", "...

Thanks a lot. That indeed solved the problem. For the sake of completeness, this code works: data.wide <- readRDS("mm.rds") runMLwiN(y ~ 1 + country_x + gov_het_sum + (1 | id_country) + (1 | p) + (1 | id_gov), estoptions = list(EstM = 1, debugmode = T, mm = list(NA, list(mmvar = list("...

Hey there, I want to fit a multiple membership model using the R2MLwiN package and get the error in R: Error in combchains[, FP.names, drop = FALSE] : subscript out of bounds The model is estimated correctly in MLwiN as far as I can see (using debugmode = T). Hence, I get the feeling it has to with ...