Hi everyone, I am facing a missing data problem with unobserved Y’s and complete matrix of predictors X. I found this package MICE which is linked to brms. Is there any preferable methodology for this case? I’d like to impute values considering a multilevel structure and also including all the info…

How about simply removing the rows with missing y and then design your model and infer the missing y values from that model? I would argue that is even better than using mice :) When using mice this paper could help you but I really don’t see a need for mice if you follow a principle Bayesian way: …

[image] torkar: In short, we used multiple imputation approaches when Bayesian imputation was infeasible because we didn’t have computation power. Multiple imputation can be fully Bayesian. Chained equations (CE in MICE) doesn’t form proper joint distribution, so that can be considered not to…

I could remove the rows with missing y, but I wouldn’t be able to fit the multilevel model I want, cause for those missing values the combinations of X are unique. Thus, for missing y’s I have unobserved levels and I still want to model variation across clusters. I think that a mixture model, such a…

Aki, why are chained equations not needed? If unobserved Y depend on observed values, can’t you iterate it using imputed y’s, given a cluster specification?

In brms you can also do model imputation, meaning that at each iteration of the MCMC the model will estimate imputed values for your missing variables. The caveat is that it only works for continuous variables [image] avehtari: As Stan doesn’t allow discrete parameters, You can see the " Imp…

[image] Juan_Ignacio_de_Oyarbide: Aki, why are chained equations not needed? Because all X1 and X2 are observed you need just one “equation” for Y|X1,X2 [image] Juan_Ignacio_de_Oyarbide: I could remove the rows with missing y , but I wouldn’t be able to fit the multilevel model I want, c…

[image] avehtari: This is an excellent vignette, and shows how you can give brms the data with NAs in Y. Howver, it doesn’t remove the identifiability problem. My goal is to model variation of X2 across all the levels of X1. So I want to show that there might be heterogeneity in the variance …

Maybe I am missing something (and maybe you resolved your issue in the meantime), but what would be wrong about fitting only the data without missingness and using posterior_predict(fit, data = orig_data %>% filter(is.na(Y), allow_new_levels = TRUE) to estimate the uncertainty you have about the mi…

Hi all! I am doing some missing data imputation in my response Y and a predictor as well but my data is organised in a hierarchical manner. Where each Y observation is nested within a temperature, for each individual for a given sampling session. I followed the vignette on data imputation. I want…

Imputation during model fitting keeps the structure. Whether mice does so depends on what methods you use within mice.

MICE missing values on the response

Modeling

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MICE missing values on the response

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