I’ve estimated a multivariate model with multiple monotonic effects and imputation for three covariates. The model converges well, but I am currently unable to estimate WAIC or extract fitted values and predictions from it with brms functions. Here is a minimal example suggesting that this may reflect general behavior about the inclusion of monotonic effects in imputation models.

data<-

data.frame(x=sample(1:4, size=20, replace=TRUE), #ordinal predictor

y=sample(1:5, size=20, replace=TRUE), #ordinal response

z=c(rnorm(10,0,1), rep(NA, 10))) #missing covariate

impute.m<-

bf(z|mi() ~ mo(x) + mo(y))+gaussian()

response.m<-

bf(y ~ mo(x) + mi(z))+cumulative()

model<-

brm(impute.m + response.m, data=data)

Although I can extract posterior samples from the model, I get the following error when attempting to calculate WAIC or generate predictions

brms::WAIC(model, resp=“y”)

Error in seq_len(sdata$Jmo[i]) :

argument must be coercible to non-negative integer

fitted(model, resp=“y”)

Error in seq_len(sdata$Jmo[i]) :

argument must be coercible to non-negative integer

In addition: Warning message:

In seq_len(sdata$Jmo[i]) : first element used of ‘length.out’ argument

The same issue occurs for predict(). How can I avoid these errors?

Thanks for your time!

Please also provide the following information in addition to your question:

- Operating System: Windows 10 x64
- brms Version: 2.7.0