Thanks again
That might be it! Can’t believe it is that simple though. Here is the output:
> fit <- stan(file = "https://raw.githubusercontent.com/stan-dev/example-models/master/misc/eight_schools/eight_schools.stan",
+ data = schools_dat, iter = 1000, chains = 4)
In file included from file10953e894277.cpp:8:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/StanHeaders/include/src/stan/model/model_header.hpp:4:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/StanHeaders/include/stan/math.hpp:4:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/StanHeaders/include/stan/math/rev/mat.hpp:4:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/StanHeaders/include/stan/math/rev/core.hpp:12:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/StanHeaders/include/stan/math/rev/core/gevv_vvv_vari.hpp:5:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/StanHeaders/include/stan/math/rev/core/var.hpp:7:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/BH/include/boost/math/tools/config.hpp:13:
In file included from /Library/Frameworks/R.framework/Versions/3.4/Resources/library/BH/include/boost/config.hpp:39:
/Library/Frameworks/R.framework/Versions/3.4/Resources/library/BH/include/boost/config/compiler/clang.hpp:196:11: warning: 'BOOST_NO_CXX11_RVALUE_REFERENCES' macro redefined [-Wmacro-redefined]
# define BOOST_NO_CXX11_RVALUE_REFERENCES
^
<command line>:6:9: note: previous definition is here
#define BOOST_NO_CXX11_RVALUE_REFERENCES 1
^
1 warning generated.
starting worker pid=4431 on localhost:11545 at 15:40:48.595
starting worker pid=4439 on localhost:11545 at 15:40:48.763
starting worker pid=4447 on localhost:11545 at 15:40:48.929
starting worker pid=4455 on localhost:11545 at 15:40:49.101
SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 1).
Gradient evaluation took 1e-05 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 1000 [ 0%] (Warmup)
Iteration: 100 / 1000 [ 10%] (Warmup)
Iteration: 200 / 1000 [ 20%] (Warmup)
Iteration: 300 / 1000 [ 30%] (Warmup)
Iteration: 400 / 1000 [ 40%] (Warmup)
Iteration: 500 / 1000 [ 50%] (Warmup)
Iteration: 501 / 1000 [ 50%] (Sampling)
Iteration: 600 / 1000 [ 60%] (Sampling)
Iteration: 700 / 1000 [ 70%] (Sampling)
Iteration: 800 / 1000 [ 80%] (Sampling)
Iteration: 900 / 1000 [ 90%] (Sampling)
Iteration: 1000 / 1000 [100%] (Sampling)
Elapsed Time: 0.05585 seconds (Warm-up)
0.024836 seconds (Sampling)
0.080686 seconds (Total)
SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 2).
Gradient evaluation took 1.1e-05 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.11 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 1000 [ 0%] (Warmup)
Iteration: 100 / 1000 [ 10%] (Warmup)
Iteration: 200 / 1000 [ 20%] (Warmup)
Iteration: 300 / 1000 [ 30%] (Warmup)
Iteration: 400 / 1000 [ 40%] (Warmup)
Iteration: 500 / 1000 [ 50%] (Warmup)
Iteration: 501 / 1000 [ 50%] (Sampling)
Iteration: 600 / 1000 [ 60%] (Sampling)
Iteration: 700 / 1000 [ 70%] (Sampling)
Iteration: 800 / 1000 [ 80%] (Sampling)
Iteration: 900 / 1000 [ 90%] (Sampling)
Iteration: 1000 / 1000 [100%] (Sampling)
Elapsed Time: 0.057615 seconds (Warm-up)
0.018443 seconds (Sampling)
0.076058 seconds (Total)
SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 3).
Gradient evaluation took 1e-05 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 1000 [ 0%] (Warmup)
Iteration: 100 / 1000 [ 10%] (Warmup)
Iteration: 200 / 1000 [ 20%] (Warmup)
Iteration: 300 / 1000 [ 30%] (Warmup)
Iteration: 400 / 1000 [ 40%] (Warmup)
Iteration: 500 / 1000 [ 50%] (Warmup)
Iteration: 501 / 1000 [ 50%] (Sampling)
Iteration: 600 / 1000 [ 60%] (Sampling)
Iteration: 700 / 1000 [ 70%] (Sampling)
Iteration: 800 / 1000 [ 80%] (Sampling)
Iteration: 900 / 1000 [ 90%] (Sampling)
Iteration: 1000 / 1000 [100%] (Sampling)
Elapsed Time: 0.054985 seconds (Warm-up)
0.033888 seconds (Sampling)
0.088873 seconds (Total)
SAMPLING FOR MODEL 'eight_schools' NOW (CHAIN 4).
Gradient evaluation took 1e-05 seconds
1000 transitions using 10 leapfrog steps per transition would take 0.1 seconds.
Adjust your expectations accordingly!
Iteration: 1 / 1000 [ 0%] (Warmup)
Iteration: 100 / 1000 [ 10%] (Warmup)
Iteration: 200 / 1000 [ 20%] (Warmup)
Iteration: 300 / 1000 [ 30%] (Warmup)
Iteration: 400 / 1000 [ 40%] (Warmup)
Iteration: 500 / 1000 [ 50%] (Warmup)
Iteration: 501 / 1000 [ 50%] (Sampling)
Iteration: 600 / 1000 [ 60%] (Sampling)
Iteration: 700 / 1000 [ 70%] (Sampling)
Iteration: 800 / 1000 [ 80%] (Sampling)
Iteration: 900 / 1000 [ 90%] (Sampling)
Iteration: 1000 / 1000 [100%] (Sampling)
Elapsed Time: 0.059951 seconds (Warm-up)
0.020852 seconds (Sampling)
0.080803 seconds (Total)
Warning messages:
1: In normalizePath(file) :
path[1]="https://raw.githubusercontent.com/stan-dev/example-models/master/misc/eight_schools/eight_schools.stan": No such file or directory
2: There were 117 divergent transitions after warmup. Increasing adapt_delta above 0.8 may help. See
http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
3: There were 1 chains where the estimated Bayesian Fraction of Missing Information was low. See
http://mc-stan.org/misc/warnings.html#bfmi-low
4: Examine the pairs() plot to diagnose sampling problems
There seem to be a discouraging number of comments/warnings, but I’ll work through the output and try another couple of examples. How can I ensure that pure ASCII is used throughout? Are there certain settings you use or do you just develop an eye for it?