tune_covlmc.md
In mixvlmc: Variable Length Markov Chains with Covariates

print works as expected

Code
  print(bt_covlmc)
Output
  VLMC with covariate context tree on A, B, C 
   cutoff in quantile scale: 7.738e-07
   Number of contexts: 3 
   Maximum context length: 1 
   Selected by BIC (1044.993) with likelihood function "truncated" (-466.5832)

Code
  print(at_covlmc)
Output
  VLMC with covariate context tree on A, B, C 
   cutoff in quantile scale: 0.5
   Number of contexts: 3 
   Maximum context length: 1 
   Selected by AIC (947.2463) with likelihood function "truncated" (-449.6231)

summary works as expected

Code
  print(summary(bt_covlmc))
Output
  VLMC with covariate tune results

  Best VLMC with covariate selected by BIC (1044.993) with likelihood function "truncated" (-466.5832)
  VLMC with covariate context tree on A, B, C 
   cutoff in quantile scale: 7.738e-07
   Number of contexts: 3 
   Maximum context length: 1

  Pruning results
          alpha depth nb_contexts loglikelihood cov_depth       AIC      BIC
   5.000000e-01     1           3     -449.6231         1  947.2463 1048.349
   7.738076e-07     1           3     -466.5832         1  969.1665 1044.993
   1.454609e-13     1           3     -499.9600         1 1019.9199 1062.046
   1.950257e-17     0           1     -546.7455         0 1097.4909 1105.916

Code
  print(summary(at_covlmc))
Output
  VLMC with covariate tune results

  Best VLMC with covariate selected by AIC (947.2463) with likelihood function "truncated" (-449.6231)
  VLMC with covariate context tree on A, B, C 
   cutoff in quantile scale: 0.5
   Number of contexts: 3 
   Maximum context length: 1

  Pruning results
          alpha depth nb_contexts loglikelihood cov_depth       AIC      BIC
   5.000000e-01     1           3     -449.6231         1  947.2463 1048.349
   7.738076e-07     1           3     -466.5832         1  969.1665 1044.993
   1.454609e-13     1           3     -499.9600         1 1019.9199 1062.046
   1.950257e-17     0           1     -546.7455         0 1097.4909 1105.916

tune_vlmc verbosity is adequate

Fitting a covlmc with max_depth= 100 and alpha= 0.5 
Initial criterion= Inf 
Improving criterion= 1072.785 
Pruning covlmc with alpha= 1.323035e-09 
VLMC with covariate context tree on A, B, C 
 cutoff in quantile scale: 0.5
 Number of contexts: 3 
 Maximum context length: 1 
 Selected by BIC (1072.785) with likelihood function "truncated" (-461.8413)