Thanks in advance for any help with this hopefully minor issue!

I am trying to fit models from McElreath’s Statistical Rethinking book, using ‘ulam’ with cmdstan. The issue seems to be that when I run the same exact model code twice in the same R session (i.e. run the same compiled RStan program) something hangs and the chains finish unexpectedly. However, if you run essentially the same model but with different parameter names (i.e. change the program but not the model structure), it works fine again.

The problem is that this happens even if you run the same model structure with different numbers of chains, etc., so this would be very annoying if I can’t figure it out!

I expect this is some minor settings issue. Is there an easy fix to this?

Running:

R v 4.1.1 on Mac ARM/M1

Rstan v 2.21.2

Cmdstanr v 0.4.0

Rethinking v 2.13

To illustrate with code (data attached):

library(“rstan”)

options(mc.cores = parallel::detectCores())

rstan_options(auto_write = TRUE)

library(cmdstanr)

library(posterior)

library(bayesplot)

color_scheme_set(“brightblue”)

library(rethinking)

# Model formula/code, which works fine:

m9.1 ← ulam(

alist(

log_gdp_std ~ dnorm(mu, sigma),

mu ← a[cid] + b[cid]*( rugged_std - 0.215 ),

a[cid] ~ dnorm(1, 0.1),

b[cid] ~ dnorm(0, 0.3),

sigma ~ dexp( 1 )

), data=dd_slim, chains=1, cmdstan=TRUE )

precis(m9.1, depth=2)

# It runs fine, and this is the output that I get:

m9.1 ← ulam(

- alist(
- log_gdp_std ~ dnorm(mu, sigma),
- mu ← a[cid] + b[cid]*( rugged_std - 0.215 ),
- a[cid] ~ dnorm(1, 0.1),
- b[cid] ~ dnorm(0, 0.3),
- sigma ~ dexp( 1 )
- ), data=dd_slim, chains=1, cmdstan=TRUE )

Compiling Stan program…

Running MCMC with 1 chain, with 1 thread(s) per chain…

Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)

Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)

Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)

Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)

Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)

Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)

Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)

Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)

Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)

Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)

Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)

Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)

Chain 1 finished in 0.1 seconds.

precis(m9.1, depth=2)

mean sd 5.5% 94.5% n_eff Rhat4

a[1] 0.89 0.01 0.86 0.91 610 1

a[2] 1.05 0.01 1.04 1.07 710 1

b[1] 0.13 0.08 0.01 0.25 777 1

b[2] -0.14 0.06 -0.23 -0.05 712 1

sigma 0.11 0.01 0.10 0.12 734 1

# However, if I run a different model with the same formula, it doesn’t work:

m9.2 ← ulam(

alist(

log_gdp_std ~ dnorm(mu, sigma),

mu ← a[cid] + b[cid]*( rugged_std - 0.215 ),

a[cid] ~ dnorm(1, 0.1),

b[cid] ~ dnorm(0, 0.3),

sigma ~ dexp( 1 )

), data=dd_slim, chains=1, cmdstan=TRUE )

precis(m9.2, depth=2)

# Output:

m9.2 ← ulam(

- alist(
- log_gdp_std ~ dnorm(mu, sigma),
- mu ← a[cid] + b[cid]*( rugged_std - 0.215 ),
- a[cid] ~ dnorm(1, 0.1),
- b[cid] ~ dnorm(0, 0.3),
- sigma ~ dexp( 1 )
- ), data=dd_slim, chains=1, cmdstan=TRUE )

**Compiling Stan program…**

Running MCMC with 1 chain, with 1 thread(s) per chain…

**Warning: Chain 1 finished unexpectedly!**

**Error in rstan::read_stan_csv(cmdstanfit$output_files()) :**

**csvfiles does not contain any CSV file name**

**In addition: Warning message:**

**No chains finished successfully. Unable to retrieve the fit.**

# But, now, if I make a third model that just changes the parameter names, it works fine again:

m9.3 ← ulam(

alist(

log_gdp_std ~ dnorm(mu, sigma),

mu ← x[cid] + y[cid]*( rugged_std - 0.215 ),

x[cid] ~ dnorm(1, 0.1),

y[cid] ~ dnorm(0, 0.3),

sigma ~ dexp( 1 )

), data=dd_slim, chains=1, cmdstan=TRUE )

precis(m9.3, depth=2)

# Output:

m9.3 ← ulam(

- alist(
- log_gdp_std ~ dnorm(mu, sigma),
- mu ← x[cid] + y[cid]*( rugged_std - 0.215 ),
- x[cid] ~ dnorm(1, 0.1),
- y[cid] ~ dnorm(0, 0.3),
- sigma ~ dexp( 1 )
- ), data=dd_slim, chains=1, cmdstan=TRUE )

Compiling Stan program…

Running MCMC with 1 chain, with 1 thread(s) per chain…

Chain 1 Iteration: 1 / 1000 [ 0%] (Warmup)

Chain 1 Iteration: 100 / 1000 [ 10%] (Warmup)

Chain 1 Iteration: 200 / 1000 [ 20%] (Warmup)

Chain 1 Iteration: 300 / 1000 [ 30%] (Warmup)

Chain 1 Iteration: 400 / 1000 [ 40%] (Warmup)

Chain 1 Iteration: 500 / 1000 [ 50%] (Warmup)

Chain 1 Iteration: 501 / 1000 [ 50%] (Sampling)

Chain 1 Iteration: 600 / 1000 [ 60%] (Sampling)

Chain 1 Iteration: 700 / 1000 [ 70%] (Sampling)

Chain 1 Iteration: 800 / 1000 [ 80%] (Sampling)

Chain 1 Iteration: 900 / 1000 [ 90%] (Sampling)

Chain 1 Iteration: 1000 / 1000 [100%] (Sampling)

Chain 1 finished in 0.1 seconds.

precis(m9.3, depth=2)

mean sd 5.5% 94.5% n_eff Rhat4

x[1] 0.89 0.02 0.86 0.91 962 1

x[2] 1.05 0.01 1.03 1.07 722 1

y[1] 0.14 0.08 0.01 0.25 615 1

y[2] -0.14 0.05 -0.23 -0.06 899 1

sigma 0.11 0.01 0.10 0.12 1208 1