I am working through Richard McElreath’s Statistically Rethinking.

On week 5 exercise I think I got pretty much the right answer, it certainly looks OK versus the given solution, but for some reason my model does not converge and comes up with lots of horrible warnings, whereas the given solution behaves perfectly.

After some hours poring over this, I can’t see why this would be from the two bits of code…

Given answer:

```
library(rethinking)
data(Wines2012)
d <- Wines2012
dat_list2b <- list(
S = standardize(d$score),
wid = d$wine.amer + 1L,
jid = d$judge.amer + 1L,
fid = ifelse(d$flight=="red",1L,2L)
)
m2b <- ulam(
alist(
S ~ dnorm( mu , sigma ),
mu <- w[wid] + j[jid] + f[fid],
w[wid] ~ dnorm( 0 , 0.5 ),
j[wid] ~ dnorm( 0 , 0.5 ),
f[wid] ~ dnorm( 0 , 0.5 ),
sigma ~ dexp(1)
), data=dat_list2b , chains=4 , cores=4 )
precis( m2b,2 )
```

My answer:

```
dat_list2 <- list(scores_s = standardize(d$score), judge_am = as.integer(d$judge.amer)+1L,
wine_am = as.integer(d$wine.amer)+1L, rw = as.integer(d$flight))
flist <- alist(score_s ~ dnorm(mu, sigma),
mu <- J[judge_am] + W[wine_am] + F[rw],
J[judge_am] ~ dnorm(0, 0.5),
W[wine_am] ~ dnorm(0,0.2),
F[rw] ~ dnorm(0,0.2),
sigma ~ dexp(1))
m2 <- ulam(flist, data = dat_list2, chains = 4, cores = 4)
precis(m2, 2)
```

which results in horror-show below, any ideas why??

Warning messages:

1: In system(paste(CXX, ARGS), ignore.stdout = TRUE, ignore.stderr = TRUE) :

‘C:/rtools40/usr/mingw_/bin/g++’ not found

2: There were 112 divergent transitions after warmup.

to find out why this is a problem and how to eliminate them.

3: There were 1483 transitions after warmup that exceeded the maximum treedepth. Increase max_treedepth above 10.

4: There were 4 chains where the estimated Bayesian Fraction of Missing Information was low.

5: Examine the pairs() plot to diagnose sampling problems

6: The largest R-hat is 3.26, indicating chains have not mixed.

Running the chains for more iterations may help.

7: Bulk Effective Samples Size (ESS) is too low, indicating posterior means and medians may be unreliable.

Running the chains for more iterations may help.

8: Tail Effective Samples Size (ESS) is too low, indicating posterior variances and tail quantiles may be unreliable.

Running the chains for more iterations may help.