Adding log_lik=true to ulam() fails

Hello,
I am using Rstudio, cmdrstan and rethinking package to run bayesian models and I’d like to compare various models. As suggested in rethinking documentation one has to add log_lik=TRUE to the ulam() but when I add that it then I get an error. I’ve tried without imputation too (just removing NAs from my dataset and that doesn’t work). Any help would be appreciated!

My model setup:

diverse_f1 <-ulam(alist(

  • grossProd ~ dnorm(mu, sigma),
  • mu ← a + b*(zScore),
  • zScore~dnorm(mu_x1, sigma_x1),
  • a ~ dnorm(2, 30),
  • b ~ dnorm(0, 100),
  • mu_x1~dnorm(0,100),
  • sigma_x1~dcauchy(0,2),
  • sigma ~ dcauchy(0,2)
  • ),data=d_full_f1, log_lik=TRUE, chains=4 , cores=4 ,start=list( B_impute = rep(0.5,26)))

And I get the error:

Found 26 NA values in grossProd and attempting imputation.
starting worker pid=13941 on localhost:11138 at 12:16:25.468
starting worker pid=13955 on localhost:11138 at 12:16:25.788
starting worker pid=13969 on localhost:11138 at 12:16:26.110
starting worker pid=13983 on localhost:11138 at 12:16:26.473
no parameter log_lik; sampling not done
no parameter log_lik; sampling not done
no parameter log_lik; sampling not done
no parameter log_lik; sampling not done
here are whatever error messages were returned
[[1]]
Stan model ‘anon_model’ does not contain samples.

[[2]]
Stan model ‘anon_model’ does not contain samples.

[[3]]
Stan model ‘anon_model’ does not contain samples.

[[4]]
Stan model ‘anon_model’ does not contain samples.

Stan model ‘anon_model’ does not contain samples.
Error in validObject(.Object) :
invalid class “ulam” object: invalid object for slot “coef” in class “ulam”: got class “NULL”, should be or extend class “numeric”
In addition: Warning message:
In .local(object, …) :
some chains had errors; consider specifying chains = 1 to debug

As this is an issue with ulam, you should open an issue over on their github for support: GitHub - rmcelreath/rethinking: Statistical Rethinking course and book package

thank you @andrjohns, I’ll do that