Running ‘testthat.R’ [67s/86s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
  > library(testthat)
  > library(psycho)
  > 
  > test_check("psycho")
  Failed with error:  'there is no package called 'GPArotation''
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
  Rejecting initial value:
    Log probability evaluates to log(0), i.e. negative infinity.
    Stan can't start sampling from this initial value.
  Rejecting initial value:
    Log probability evaluates to log(0), i.e. negative infinity.
    Stan can't start sampling from this initial value.
  
  Gradient evaluation took 6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.6 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.417056 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.8 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 2.05779 seconds (Warm-up)
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                 3.8285 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 5.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.55 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.059063 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.46 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.062529 seconds (Warm-up)
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                 0.121499 seconds (Total)
  
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 6.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.66 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
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  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -4e+02             1.000            1.000
     200     -2e+02             1.011            1.021
     300     -2e+02             0.682            1.000
     400     -2e+02             0.511            1.000
     500     -2e+02             0.409            0.024
     600     -2e+02             0.341            0.024
     700     -2e+02             0.293            0.002   MEDIAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  ------------------------------------------------------------
  EXPERIMENTAL ALGORITHM:
    This procedure has not been thoroughly tested and may be unstable
    or buggy. The interface is subject to change.
  ------------------------------------------------------------
  
  
  
  Gradient evaluation took 7.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.74 seconds.
  Adjust your expectations accordingly!
  
  
  Begin eta adaptation.
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  Success! Found best value [eta = 1] earlier than expected.
  
  Begin stochastic gradient ascent.
    iter       ELBO   delta_ELBO_mean   delta_ELBO_med   notes 
     100     -9e+04             1.000            1.000
     200     -6e+02            79.074          157.147
     300     -5e+02            52.759            1.000
     400     -2e+02            39.982            1.651
     500     -2e+02            31.989            1.000
     600     -2e+02            26.660            1.000
     700     -2e+02            22.854            0.130
     800     -2e+02            19.999            0.130
     900     -2e+02            17.778            0.018
    1000     -2e+02            16.000            0.018
    1100     -2e+02            15.902            0.016   MAY BE DIVERGING... INSPECT ELBO
    1200     -2e+02             0.188            0.014
    1300     -2e+02             0.176            0.014
    1400     -2e+02             0.011            0.014
    1500     -2e+02             0.010            0.013   MEAN ELBO CONVERGED
  
  Drawing a sample of size 1000 from the approximate posterior... 
  COMPLETED.
  Initial log joint probability = -100816
  Optimization terminated normally: 
    Convergence detected: relative gradient magnitude is below tolerance
  The Crawford-Howell (1998) t-test suggests no dissociation between test X and test Y (t(5) = 2.10, p = 0.09<b0>). The patient's score on test X is not significantly altered compared to its score on test Y.
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 0.000175 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.75 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.705933 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 9.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.91 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.733044 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 9.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.93 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.697834 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 0.000107 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.07 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.673636 seconds (Warm-up)
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  ── 1. Error: If it works. (@test-get_contrasts.stanreg.R#10)  ──────────────────
  number of items to replace is not a multiple of replacement length
  1: psycho::get_contrasts(fit, "Salary") at testthat/test-get_contrasts.stanreg.R:10
  2: get_contrasts.stanreg(fit, "Salary")
  3: fit %>% emmeans::emmeans(formula) %>% coda::as.mcmc() %>% as.matrix() %>% coda::as.mcmc() %>% 
         as.data.frame()
  4: withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
  5: eval(quote(`_fseq`(`_lhs`)), env, env)
  6: eval(quote(`_fseq`(`_lhs`)), env, env)
  7: `_fseq`(`_lhs`)
  8: freduce(value, `_function_list`)
  9: function_list[[i]](value)
  10: as.matrix(.)
  11: as.matrix.mcmc(.)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 1).
  
  Gradient evaluation took 5.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.53 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.139537 seconds (Warm-up)
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                 0.28711 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 2).
  
  Gradient evaluation took 2.7e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.142272 seconds (Warm-up)
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                 0.310426 seconds (Total)
  
  
  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 3).
  
  Gradient evaluation took 3.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.134255 seconds (Warm-up)
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  SAMPLING FOR MODEL 'bernoulli' NOW (CHAIN 4).
  
  Gradient evaluation took 3.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.32 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.121949 seconds (Warm-up)
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                 0.248244 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 4.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.106747 seconds (Warm-up)
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                 0.208646 seconds (Total)
  
  
  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 2.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.104633 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 2.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.095764 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 2.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
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   Elapsed Time: 0.123033 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 6.3e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.63 seconds.
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   Elapsed Time: 0.292114 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 3.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.39 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.296969 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 3.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
  Adjust your expectations accordingly!
  
  
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   Elapsed Time: 0.289468 seconds (Warm-up)
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 6.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.62 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 0.000131 seconds
  1000 transitions using 10 leapfrog steps per transition would take 1.31 seconds.
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 0.000183 seconds
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 6.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.69 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 7.2e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.72 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 4.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 3.8e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.38 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 8.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.85 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.5 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 4.9e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.49 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
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             term_buffer = 5
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 4.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.44 seconds.
  Adjust your expectations accordingly!
  
  
  WARNING: There aren't enough warmup iterations to fit the
           three stages of adaptation as currently configured.
           Reducing each adaptation stage to 15%/75%/10% of
           the given number of warmup iterations:
             init_buffer = 7
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             term_buffer = 5
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
  
  Gradient evaluation took 6.1e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
  
  Gradient evaluation took 3.4e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
  
  Gradient evaluation took 3.5e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
  Adjust your expectations accordingly!
  
  
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  SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
  
  Gradient evaluation took 3.6e-05 seconds
  1000 transitions using 10 leapfrog steps per transition would take 0.36 seconds.
  Adjust your expectations accordingly!
  
  
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  .0%
  .33%
  .67%
  .100%
  1
  ══ testthat results  ═══════════════════════════════════════════════════════════
  OK: 137 SKIPPED: 0 FAILED: 1
  1. Error: If it works. (@test-get_contrasts.stanreg.R#10) 
  
  Error: testthat unit tests failed
  Execution halted