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

print works as expected

VLMC context tree on 0, 1, 2 
 cutoff: 3.379 (quantile: 0.03409)
 Number of contexts: 3 
 Maximum context length: 1 
 Selected by BIC (968.9371) with likelihood function "truncated" (-471.5896)

VLMC context tree on 0, 1, 2 
 cutoff: 3.379 (quantile: 0.03409)
 Number of contexts: 3 
 Maximum context length: 1 
 Selected by AIC (938.2772) with likelihood function "truncated" (-471.5896)

summary works as expected

VLMC tune results

Best VLMC selected by BIC (968.9371) with likelihood function "truncated" (-471.5896)
VLMC context tree on 0, 1, 2 
 cutoff: 3.379 (quantile: 0.03409)
 Number of contexts: 3 
 Maximum context length: 1

Pruning results
    cutoff        alpha depth nb_contexts loglikelihood       AIC       BIC
  1.553652 2.114743e-01     8          56     -406.2783 1036.5565 1506.7862
  1.566520 2.087705e-01     8          52     -409.1532 1026.3064 1462.9481
  1.595110 2.028862e-01     8          50     -411.2862 1022.5724 1442.4203
  1.617948 1.983051e-01     8          49     -412.8997 1021.7994 1433.2504
  1.629846 1.959597e-01     8          49     -414.5221 1025.0442 1436.4951
  1.638429 1.942851e-01     8          48     -416.1594 1024.3189 1427.3729
  1.659070 1.903160e-01     8          46     -418.4485 1020.8971 1407.1571
  1.685882 1.852809e-01     8          44     -420.6821 1017.3642 1386.8303
  1.727358 1.777533e-01     8          42     -423.6394 1015.2789 1367.9511
  1.771785 1.700292e-01     8          39     -426.0461 1008.0922 1335.5735
  1.806683 1.641979e-01     8          38     -427.8272 1007.6545 1326.7389
  1.833370 1.598738e-01     8          36     -429.7605 1003.5210 1305.8115
  1.841439 1.585891e-01     8          35     -431.5947 1003.1893 1297.0828
  1.906938 1.485344e-01     7          27     -436.9799  981.9598 1208.6776
  1.968433 1.396755e-01     5          23     -439.3010  970.6021 1163.7321
  2.079931 1.249389e-01     5          21     -441.9384  967.8768 1144.2129
  2.212651 1.094102e-01     5          19     -444.5310  965.0620 1124.6041
  2.256204 1.047474e-01     5          17     -446.8942  961.7884 1104.5366
  2.303889 9.986969e-02     5          14     -450.8427  957.6853 1075.2427
  2.552296 7.790260e-02     4          10     -454.5862  949.1724 1033.1420
  2.834259 5.876203e-02     4           8     -457.7708  947.5416 1014.7173
  2.880310 5.611737e-02     3           5     -462.5273  945.0546  987.0394
  3.378896 3.408506e-02     1           3     -465.8731  943.7462  968.9371
  8.715379 1.640434e-04     1           3     -470.0193  952.0386  977.2294
 21.194476 6.242464e-10     1           2     -488.8284  985.6569 1002.4508
 25.464063 8.731682e-12     0           1     -511.0119 1026.0239 1034.4208

VLMC tune results

Best VLMC selected by AIC (957.1793) with likelihood function "specific" (-471.5896)
VLMC context tree on 0, 1, 2 
 cutoff: 3.379 (quantile: 0.03409)
 Number of contexts: 3 
 Maximum context length: 1

Pruning results
    cutoff        alpha depth nb_contexts loglikelihood       AIC       BIC
  1.000000 3.678794e-01    10         112     -352.0230 1172.0461 2158.2644
  1.015516 3.622156e-01    10         111     -353.0325 1170.0649 2147.8540
  1.027283 3.579784e-01    10         107     -356.9642 1161.9283 2106.0005
  1.035378 3.550922e-01    10         104     -358.8625 1153.7251 2072.5096
  1.039644 3.535805e-01    10         103     -359.9003 1151.8007 2062.1560
  1.072543 3.421375e-01    10         102     -360.9418 1149.8836 2051.8098
  1.115718 3.276801e-01    10         102     -362.0464 1152.0927 2054.0188
  1.133206 3.219993e-01    10         100     -363.4503 1146.9007 2031.9684
  1.175289 3.087298e-01    10          96     -367.3507 1138.7014 1990.0522
  1.214332 2.969084e-01    10          95     -368.5630 1137.1259 1980.0475
  1.227189 2.931154e-01    10          89     -374.3681 1124.7362 1917.0826
  1.247246 2.872949e-01    10          87     -376.2179 1120.4357 1895.9236
  1.319793 2.671906e-01    10          86     -377.4743 1118.9487 1886.0074
  1.392476 2.484592e-01     8          66     -392.2825 1064.5651 1654.6102
  1.402794 2.459090e-01     8          66     -393.6812 1067.3624 1657.4076
  1.421783 2.412833e-01     8          65     -395.6243 1067.2486 1648.8646
  1.437610 2.374947e-01     8          64     -397.0611 1066.1223 1639.3090
  1.445170 2.357060e-01     8          60     -398.9878 1053.9756 1593.4454
  1.477267 2.282607e-01     8          59     -400.5155 1053.0310 1584.0717
  1.517674 2.192213e-01     8          58     -402.0185 1052.0371 1574.6485
  1.538883 2.146207e-01     8          57     -404.7330 1053.4659 1567.6481
  1.550745 2.120899e-01     8          56     -406.2783 1052.5565 1558.3095
  1.566520 2.087705e-01     8          52     -409.1532 1042.3064 1514.3425
  1.595110 2.028862e-01     8          50     -411.2862 1038.5724 1493.7501
  1.617948 1.983051e-01     8          49     -412.8997 1037.7994 1484.5479
  1.629846 1.959597e-01     8          49     -414.5221 1041.0442 1487.7927
  1.638429 1.942851e-01     8          48     -416.1594 1040.3189 1478.6381
  1.659070 1.903160e-01     8          46     -418.4485 1036.8971 1458.3579
  1.685882 1.852809e-01     8          44     -420.6821 1033.3642 1437.9665
  1.727358 1.777533e-01     8          42     -423.6394 1031.2789 1419.0228
  1.771785 1.700292e-01     8          39     -426.0461 1024.0922 1386.5485
  1.806683 1.641979e-01     8          38     -427.8272 1023.6545 1377.6815
  1.833370 1.598738e-01     8          36     -429.7605 1019.5210 1356.6896
  1.841439 1.585891e-01     8          35     -431.5947 1019.1893 1347.9287
  1.906938 1.485344e-01     7          27     -438.0408  998.0815 1255.1726
  1.968433 1.396755e-01     5          23     -442.2061  986.4122 1201.3573
  2.079931 1.249389e-01     5          21     -444.8435  983.6870 1181.7736
  2.212651 1.094102e-01     5          19     -447.7499  981.4997 1162.7279
  2.256204 1.047474e-01     5          17     -450.1131  978.2261 1142.5958
  2.303889 9.986969e-02     5          14     -454.0615  974.1231 1113.2051
  2.552296 7.790260e-02     4          10     -458.8848  965.7695 1066.9201
  2.834259 5.876203e-02     4           8     -461.9353  963.8706 1048.1627
  2.880310 5.611737e-02     3           5     -467.2549  960.5098 1015.2997
  3.378896 3.408506e-02     1           3     -471.5896  957.1793  986.6815
  8.715379 1.640434e-04     1           3     -475.5345  965.0689  994.5712
 21.194476 6.242464e-10     1           2     -494.7895  999.5790 1020.6520
 25.464063 8.731682e-12     0           1     -519.1294 1042.2587 1050.6880

tune_vlmc verbosity is adequate

Fitting a vlmc with max_depth= 2 and cutoff= 1.553652 
Max depth reached, increasing it to 4 
Max depth reached, increasing it to 8 
Max depth reached, increasing it to 16 
Initial criterion = Inf 
Improving criterion = 1506.786 likelihood = -406.2783 df = 112 nobs =  492 
Pruning vlmc with cutoff = 1.56652 
Improving criterion = 1462.948 likelihood = -409.1532 df = 104 nobs =  492 
Pruning vlmc with cutoff = 1.59511 
Improving criterion = 1442.42 likelihood = -411.2862 df = 100 nobs =  492 
Pruning vlmc with cutoff = 1.617948 
Improving criterion = 1433.25 likelihood = -412.8997 df = 98 nobs =  492 
Pruning vlmc with cutoff = 1.629846 
Pruning vlmc with cutoff = 1.638429 
Improving criterion = 1427.373 likelihood = -416.1594 df = 96 nobs =  492 
Pruning vlmc with cutoff = 1.65907 
Improving criterion = 1407.157 likelihood = -418.4485 df = 92 nobs =  492 
Pruning vlmc with cutoff = 1.685882 
Improving criterion = 1386.83 likelihood = -420.6821 df = 88 nobs =  492 
Pruning vlmc with cutoff = 1.727358 
Improving criterion = 1367.951 likelihood = -423.6394 df = 84 nobs =  492 
Pruning vlmc with cutoff = 1.771785 
Improving criterion = 1335.574 likelihood = -426.0461 df = 78 nobs =  492 
Pruning vlmc with cutoff = 1.806683 
Improving criterion = 1326.739 likelihood = -427.8272 df = 76 nobs =  492 
Pruning vlmc with cutoff = 1.83337 
Improving criterion = 1305.811 likelihood = -429.7605 df = 72 nobs =  492 
Pruning vlmc with cutoff = 1.841439 
Improving criterion = 1297.083 likelihood = -431.5947 df = 70 nobs =  492 
Pruning vlmc with cutoff = 1.906938 
Improving criterion = 1208.678 likelihood = -436.9799 df = 54 nobs =  492 
Pruning vlmc with cutoff = 1.968433 
Improving criterion = 1163.732 likelihood = -439.301 df = 46 nobs =  492 
Pruning vlmc with cutoff = 2.079931 
Improving criterion = 1144.213 likelihood = -441.9384 df = 42 nobs =  492 
Pruning vlmc with cutoff = 2.212651 
Improving criterion = 1124.604 likelihood = -444.531 df = 38 nobs =  492 
Pruning vlmc with cutoff = 2.256204 
Improving criterion = 1104.537 likelihood = -446.8942 df = 34 nobs =  492 
Pruning vlmc with cutoff = 2.303889 
Improving criterion = 1075.243 likelihood = -450.8427 df = 28 nobs =  492 
Pruning vlmc with cutoff = 2.552296 
Improving criterion = 1033.142 likelihood = -454.5862 df = 20 nobs =  492 
Pruning vlmc with cutoff = 2.834259 
Improving criterion = 1014.717 likelihood = -457.7708 df = 16 nobs =  492 
Pruning vlmc with cutoff = 2.88031 
Improving criterion = 987.0394 likelihood = -462.5273 df = 10 nobs =  492 
Pruning vlmc with cutoff = 3.378896 
Improving criterion = 968.9371 likelihood = -465.8731 df = 6 nobs =  492 
Pruning vlmc with cutoff = 8.715379 
Pruning vlmc with cutoff = 21.19448 
Pruning vlmc with cutoff = 25.46406 
VLMC context tree on 0, 1, 2 
 cutoff: 3.379 (quantile: 0.03409)
 Number of contexts: 3 
 Maximum context length: 1 
 Selected by BIC (968.9371) with likelihood function "truncated" (-471.5896)