print_plot.md
In loo: Efficient Leave-One-Out Cross-Validation and WAIC for Bayesian Models

print.waic output is ok

Code
  print(waic1)
Output

  Computed from 1000 by 32 log-likelihood matrix.

            Estimate  SE
  elpd_waic    -83.5 4.3
  p_waic         3.3 1.1
  waic         167.1 8.5

  3 (9.4%) p_waic estimates greater than 0.4. We recommend trying loo instead.

print.psis_loo and print.psis output ok

Code
  print(psis1)
Output
  Computed from 1000 by 32 log-weights matrix.
  MCSE and ESS estimates assume independent draws (r_eff=1).

  All Pareto k estimates are good (k < 0.67).
  See help('pareto-k-diagnostic') for details.

Code
  print(loo1)
Output

  Computed from 1000 by 32 log-likelihood matrix.

           Estimate  SE
  elpd_loo    -83.6 4.3
  p_loo         3.3 1.2
  looic       167.2 8.6
  ------
  MCSE of elpd_loo is 0.1.
  MCSE and ESS estimates assume independent draws (r_eff=1).

  All Pareto k estimates are good (k < 0.67).
  See help('pareto-k-diagnostic') for details.

Code
  print(loo1_r_eff)
Output

  Computed from 1000 by 32 log-likelihood matrix.

           Estimate  SE
  elpd_loo    -83.6 4.3
  p_loo         3.3 1.2
  looic       167.2 8.6
  ------
  MCSE of elpd_loo is 0.1.
  MCSE and ESS estimates assume MCMC draws (r_eff in [0.6, 1.0]).

  All Pareto k estimates are good (k < 0.67).
  See help('pareto-k-diagnostic') for details.

mcse_loo extractor gives correct value

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print.loo supports kfold with pareto-k diagnostics - calibrated

Code
  print(kfold1)
Output

  Based on 10-fold cross-validation.

             Estimate   SE
  elpd_kfold   -285.0  9.2
  p_kfold         2.5  0.6
  kfoldic       570.0 18.4
  ------

  All Pareto k estimates are good (k < 0.7).
  See help('pareto-k-diagnostic') for details.

print.loo supports kfold with pareto-k diagnostics - miscalibrated

Code
  print(kfold1)
Output

  Based on 10-fold cross-validation.

             Estimate     SE
  elpd_kfold  -5556.6  701.0
  p_kfold       358.2  108.5
  kfoldic     11113.1 1401.9
  ------

  Pareto k diagnostic values:
                           Count Pct.    Min. ESS
  (-Inf, 0.7]   (good)     245   93.5%   24      
     (0.7, 1]   (bad)        8    3.1%   <NA>    
     (1, Inf)   (very bad)   9    3.4%   <NA>    
  See help('pareto-k-diagnostic') for details.

print.loo supports kfold without pareto-k diagnostics

Code
  print(kfold1)
Output

  Based on 10-fold cross-validation.

             Estimate     SE
  elpd_kfold  -5556.6  701.0
  p_kfold       358.2  108.5
  kfoldic     11113.1 1401.9