Dealing with Catalina

[coatless]

From running:

@ssp3nc3r

library(rstan); library(brms)

output <- c(0.5237868884927709,0.9487179487179488,0.5494563370710159,0.46386348238482383,0.4458125179443009,0.9830952380952381,0.9685185185185186,0.5118241235888294,0.8824457593688363,0.5644325518178729,0.6317411924119242,0.4893159632500718,0.9416071428571429,0.9145833333333333,0.4509011685482273,0.7682445759368836,0.2748216106014271,0.7207063008130081,0.6109249005373036,0.9156547619047619,0.9011574074074074,0.6936423054070112,0.9012820512820513,0.8805895344886171,0.7585789295392954,0.6982066978384808,0.9988095238095238,0.9606481481481481,0.7225985343632402,0.9347140039447732,0.7936459395174992,0.7401422764227643,0.7547029449161232,0.9897619047619047,0.9571759259259259,0.734383046147752,1.0,0.8722222222222221,0.6707232384823848,0.6119692588491038,1.0,1.0,0.5487621311150723,0.8960552268244577,0.6401885830784912,0.7439871273712737,0.6910643533899349,0.9875,0.9847222222222223,0.5955040602099425,0.9396449704142013,0.5083588175331294,0.6947069783197831,0.5883210286698658,0.9872023809523809,0.9643518518518519)
    categs<-as.factor(rep(c("FOO1","FOO2","FOO3","FOO4","FOO5","FOO6","FOO7","FOO8"),each=7));
    blocks <- c(1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7,1,2,3,4,5,6,7)
df1 <- data.frame(y=output, algorithm=as.integer(categs), block_id=blocks)

post <- stan(model_code = make_stancode(y ~ 1 + (1 | algorithm), data = df1),
                 data = make_standata(y ~ 1 + (1 | algorithm), data = df1), 
                 verbose = TRUE)

I’m getting:

CHECKING DATA AND PREPROCESSING FOR MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW.

COMPILING MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW.

STARTING SAMPLER FOR MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW.

SAMPLING FOR MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW (CHAIN 1).
Chain 1: 
Chain 1: Gradient evaluation took 2.5e-05 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.25 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1: 
Chain 1: 
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Chain 1: 
Chain 1:  Elapsed Time: 0.227319 seconds (Warm-up)
Chain 1:                0.108264 seconds (Sampling)
Chain 1:                0.335583 seconds (Total)
Chain 1: 

SAMPLING FOR MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW (CHAIN 2).
Chain 2: 
Chain 2: Gradient evaluation took 1.4e-05 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2: 
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Chain 2: 
Chain 2:  Elapsed Time: 0.207346 seconds (Warm-up)
Chain 2:                0.114258 seconds (Sampling)
Chain 2:                0.321604 seconds (Total)
Chain 2: 

SAMPLING FOR MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW (CHAIN 3).
Chain 3: 
Chain 3: Gradient evaluation took 1.2e-05 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3: 
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Chain 3: 
Chain 3:  Elapsed Time: 0.193264 seconds (Warm-up)
Chain 3:                0.112713 seconds (Sampling)
Chain 3:                0.305977 seconds (Total)
Chain 3: 

SAMPLING FOR MODEL '4bd718ce37045da5116c1e4a2933dbfb' NOW (CHAIN 4).
Chain 4: 
Chain 4: Gradient evaluation took 1.3e-05 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4: 
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Chain 4: 
Chain 4:  Elapsed Time: 0.184546 seconds (Warm-up)
Chain 4:                0.110832 seconds (Sampling)
Chain 4:                0.295378 seconds (Total)
Chain 4: 
Warning: There were 5 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
Warning: Examine the pairs() plot to diagnose sampling problems

Maybe the data is bad?