Loading required package: Rcpp
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Attaching package: ‘inline’
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registerPlugin
rstan (Version 2.5.0, packaged: 2014-11-24 23:24:49 UTC, GitRev: 498f4db1f270)
TRANSLATING MODEL '8schools' FROM Stan CODE TO C++ CODE NOW.
COMPILING THE C++ CODE FOR MODEL '8schools' NOW.
In file included from /usr/local/sage/sage-6.4/local/include/boost/random/detail/large_arithmetic.hpp:19:0,
from /usr/local/sage/sage-6.4/local/include/boost/random/detail/const_mod.hpp:23,
from /usr/local/sage/sage-6.4/local/include/boost/random/linear_congruential.hpp:30,
from /usr/local/sage/sage-6.4/local/lib/R/library/rstan/include//stansrc/stan/model/model_header.hpp:14,
from file2147c9a4659.cpp:8:
/usr/local/sage/sage-6.4/local/include/boost/random/detail/integer_log2.hpp:71:35: warning: always_inline function might not be inlinable [-Wattributes]
BOOST_RANDOM_DETAIL_CONSTEXPR int integer_log2(T t)
^
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Warning message:
In readLines("/tmp/8schools.stan") :
incomplete final line found on '/tmp/8schools.stan'
TRANSLATING MODEL 'schools_code' FROM Stan CODE TO C++ CODE NOW.
COMPILING THE C++ CODE FOR MODEL 'schools_code' NOW.
In file included from /usr/local/sage/sage-6.4/local/include/boost/random/detail/large_arithmetic.hpp:19:0,
from /usr/local/sage/sage-6.4/local/include/boost/random/detail/const_mod.hpp:23,
from /usr/local/sage/sage-6.4/local/include/boost/random/linear_congruential.hpp:30,
from /usr/local/sage/sage-6.4/local/lib/R/library/rstan/include//stansrc/stan/model/model_header.hpp:14,
from file214441ca7aa.cpp:8:
/usr/local/sage/sage-6.4/local/include/boost/random/detail/integer_log2.hpp:71:35: warning: always_inline function might not be inlinable [-Wattributes]
BOOST_RANDOM_DETAIL_CONSTEXPR int integer_log2(T t)
^
SAMPLING FOR MODEL 'schools_code' NOW (CHAIN 1).
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Inference for Stan model: schools_code.
4 chains, each with iter=10000; warmup=5000; thin=1;
post-warmup draws per chain=5000, total post-warmup draws=20000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
mu 7.92 0.08 5.17 -2.38 4.67 7.89 11.19 18.29 4697 1
tau 6.72 0.10 5.61 0.24 2.58 5.45 9.38 20.68 3296 1
eta[1] 0.41 0.01 0.94 -1.50 -0.21 0.43 1.04 2.20 11925 1
eta[2] -0.01 0.01 0.86 -1.70 -0.58 -0.02 0.55 1.70 12148 1
eta[3] -0.19 0.01 0.93 -2.01 -0.81 -0.20 0.42 1.65 12730 1
eta[4] -0.04 0.01 0.88 -1.78 -0.62 -0.05 0.54 1.71 12745 1
eta[5] -0.35 0.01 0.88 -2.04 -0.93 -0.37 0.21 1.48 11157 1
eta[6] -0.21 0.01 0.88 -1.92 -0.79 -0.22 0.36 1.58 11805 1
eta[7] 0.35 0.01 0.89 -1.45 -0.23 0.36 0.94 2.09 11462 1
eta[8] 0.06 0.01 0.94 -1.80 -0.57 0.06 0.69 1.90 13915 1
theta[1] 11.61 0.09 8.49 -2.18 6.04 10.40 15.83 31.93 8104 1
theta[2] 7.79 0.05 6.25 -4.57 3.89 7.74 11.68 20.56 14928 1
theta[3] 6.09 0.08 7.89 -11.51 1.91 6.60 10.86 20.77 10503 1
theta[4] 7.53 0.06 6.66 -6.46 3.63 7.58 11.62 20.73 13667 1
theta[5] 5.05 0.06 6.40 -9.21 1.23 5.53 9.36 16.41 11307 1
theta[6] 6.11 0.06 6.74 -8.66 2.24 6.52 10.37 18.61 11651 1
theta[7] 10.76 0.06 6.94 -1.39 6.09 10.10 14.80 26.25 11604 1
theta[8] 8.44 0.07 7.98 -7.14 3.83 8.17 12.79 25.71 11828 1
lp__ -4.84 0.04 2.62 -10.69 -6.41 -4.59 -2.99 -0.45 4585 1
Samples were drawn using NUTS(diag_e) at Mon Nov 24 23:31:10 2014.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).
Inference for Stan model: 8schools.
4 chains, each with iter=1000; warmup=500; thin=1;
post-warmup draws per chain=500, total post-warmup draws=2000.
mean se_mean sd 2.5% 25% 50% 75% 97.5% n_eff Rhat
mu 7.6 0.2 5.2 -2.4 4.4 7.5 10.7 17.8 512 1
tau 6.3 0.2 5.5 0.2 2.4 4.8 8.8 20.8 565 1
eta[1] 0.4 0.0 0.9 -1.4 -0.2 0.4 1.0 2.1 1264 1
eta[2] 0.0 0.0 0.9 -1.8 -0.6 0.0 0.6 1.9 1265 1
eta[3] -0.2 0.0 1.0 -2.1 -0.9 -0.2 0.5 1.7 1372 1
eta[4] 0.0 0.0 0.9 -1.8 -0.6 0.0 0.6 1.8 1002 1
eta[5] -0.3 0.0 0.9 -2.0 -0.9 -0.4 0.2 1.4 1240 1
eta[6] -0.2 0.0 0.9 -2.0 -0.8 -0.2 0.4 1.6 1337 1
eta[7] 0.4 0.0 0.9 -1.5 -0.2 0.4 1.0 2.1 1181 1
eta[8] 0.0 0.0 0.9 -1.9 -0.6 0.1 0.7 1.8 1355 1
theta[1] 10.9 0.2 7.6 -1.7 5.9 9.9 14.6 28.1 1101 1
theta[2] 7.7 0.1 6.3 -5.5 3.8 7.6 11.5 20.7 1754 1
theta[3] 6.0 0.2 7.5 -11.3 1.8 6.4 10.4 19.9 1388 1
theta[4] 7.3 0.1 6.4 -6.1 3.6 7.4 11.2 20.5 1890 1
theta[5] 4.9 0.1 6.1 -8.6 1.3 5.3 9.1 15.9 1760 1
theta[6] 6.1 0.2 6.8 -8.5 2.3 6.3 10.5 18.9 1187 1
theta[7] 10.6 0.2 7.1 -1.8 6.0 9.9 14.4 26.4 1154 1
theta[8] 8.1 0.2 8.1 -8.4 3.6 7.8 12.4 25.9 1286 1
lp__ -5.1 0.1 2.7 -11.1 -6.7 -4.8 -3.1 -0.5 613 1
Samples were drawn using NUTS(diag_e) at Mon Nov 24 23:29:47 2014.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at
convergence, Rhat=1).