Running ‘testthat.R’ [30m/31m]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > # Part of the rstanarm package for estimating model parameters
  > # Copyright (C) 2015 Trustees of Columbia University
  > # 
  > # This program is free software; you can redistribute it and/or
  > # modify it under the terms of the GNU General Public License
  > # as published by the Free Software Foundation; either version 3
  > # of the License, or (at your option) any later version.
  > # 
  > # This program is distributed in the hope that it will be useful,
  > # but WITHOUT ANY WARRANTY; without even the implied warranty of
  > # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
  > # GNU General Public License for more details.
  > # 
  > # You should have received a copy of the GNU General Public License
  > # along with this program; if not, write to the Free Software
  > # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301, USA.
  > 
  > library(testthat)
  > library(rstanarm)
  Loading required package: Rcpp
  rstanarm (Version 2.14.1, packaged: 2017-01-16 18:47:11 UTC)
  - Do not expect the default priors to remain the same in future rstanarm versions.
  Thus, R scripts should specify priors explicitly, even if they are just the defaults.
  - For execution on a local, multicore CPU with excess RAM we recommend calling
  options(mc.cores = parallel::detectCores())
  > Sys.unsetenv("R_TESTS")
  > example(example_model)
  
  exmpl_> example_model <- 
  exmpl_+   stan_glmer(cbind(incidence, size - incidence) ~ size + period + (1|herd),
  exmpl_+              data = lme4::cbpp, family = binomial,
  exmpl_+              # this next line is only to keep the example small in size!
  exmpl_+              chains = 2, cores = 1, seed = 12345, iter = 500)
  
  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 1).
  
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   Elapsed Time: 1.04 seconds (Warm-up)
                 0.67 seconds (Sampling)
                 1.71 seconds (Total)
  
  
  SAMPLING FOR MODEL 'binomial' NOW (CHAIN 2).
  
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   Elapsed Time: 0.89 seconds (Warm-up)
                 0.82 seconds (Sampling)
                 1.71 seconds (Total)
  
  
  exmpl_> example_model
  stan_glmer(formula = cbind(incidence, size - incidence) ~ size + 
      period + (1 | herd), data = lme4::cbpp, family = binomial, 
      chains = 2, cores = 1, seed = 12345, iter = 500)
  
  Estimates:
              Median MAD_SD
  (Intercept) -1.5    0.7  
  size         0.0    0.0  
  period2     -1.0    0.3  
  period3     -1.1    0.4  
  period4     -1.5    0.5  
  
  Error terms:
   Groups Name        Std.Dev.
   herd   (Intercept) 0.77    
  Num. levels: herd 15 
  
  Sample avg. posterior predictive 
  distribution of y (X = xbar):
           Median MAD_SD
  mean_PPD 1.8    0.2   
  > if (!grepl("^sparc",  R.version$platform)) test_check("rstanarm")
  
   4-fold cross-validation
  
             Estimate  SE
  elpd_kfold    -77.7 3.5
  
   2-fold cross-validation
  
             Estimate   SE
  elpd_kfold   -106.2 11.9
  Loading required package: Matrix
  Setting 'QR' to TRUE can often be helpful when using the 'meanfield' algorithm.
  See the documentation for the 'QR' argument.