tests/testthat/_snaps/data_management.md

In loelschlaeger/RprobitB: Bayesian Probit Choice Modeling

overview effects can be generated

Code
  out
Output
        effect as_value as_coef random
  1 income_bus    FALSE    TRUE  FALSE
  2 income_car    FALSE    TRUE  FALSE
  3   time_bus     TRUE    TRUE  FALSE
  4   time_car     TRUE    TRUE  FALSE
  5 time_train     TRUE    TRUE  FALSE
  6       cost     TRUE   FALSE   TRUE
  7    ASC_bus    FALSE    TRUE   TRUE
  8    ASC_car    FALSE    TRUE   TRUE

data preparation works

Code
  print(data)
Output
  Empirical data of 2929 choices.

Code
  summary(data)
Output
                   count
  deciders           235
  choice occasions  5-19
  total choices     2929
  alternatives         2
  - 'A'             1474
  - 'B'             1455

data preparation with ordered choices works

Code
  print(data)
Output
  Empirical data of 55 (ordered) choices.

Code
  summary(data)
Output
                   count
  deciders            10
  choice occasions  1-10
  total choices       55
  alternatives         5
  - 'very bad'        34
  - 'bad'             17
  - 'indifferent'      4
  - 'good'             0
  - 'very good'        0

data preparation with ranked choices works

Code
  print(data)
Output
  Empirical data of 55 (ranked) choices.

Code
  summary(data)
Output
                   count
  deciders            10
  choice occasions  1-10
  total choices       55
  alternatives         6
  - 'A,B,C'            3
  - 'A,C,B'            9
  - 'B,A,C'            4
  - 'B,C,A'           17
  - 'C,A,B'            7
  - 'C,B,A'           15

data preparation with non-standard base alternative works

Code
  print(data)
Output
  Empirical data of 2929 choices.

Code
  summary(data)
Output
                   count
  deciders           235
  choice occasions  5-19
  total choices     2929
  alternatives         2
  - 'A'             1474
  - 'B'             1455

simulating choice data works

Code
  print(data)
Output
  Simulated data of 55 choices.

Code
  summary(data)
Output
                   count
  deciders            10
  choice occasions  1-10
  total choices       55
  alternatives         3
  - 'bus'              7
  - 'car'             22
  - 'train'           26

simulating ordered choices works

Code
  print(data)
Output
  Simulated data of 55 (ordered) choices.

Code
  summary(data)
Output
                   count
  deciders            10
  choice occasions  1-10
  total choices       55
  alternatives         5
  - 'very bad'        34
  - 'bad'             17
  - 'indifferent'      4
  - 'good'             0
  - 'very good'        0

simulating ranked choices works

Code
  print(data)
Output
  Simulated data of 55 (ranked) choices.

Code
  summary(data)
Output
                   count
  deciders            10
  choice occasions  1-10
  total choices       55
  alternatives         6
  - 'A,B,C'            3
  - 'A,C,B'            9
  - 'B,A,C'            4
  - 'B,C,A'           17
  - 'C,A,B'            7
  - 'C,B,A'           15

splitting data set by N works

Code
  train_test(x, test_proportion = 0.3, by = "N")
Output
  $train
  Simulated data of 28 choices.

  $test
  Simulated data of 27 choices.

Code
  train_test(x, test_proportion = 0, by = "N")
Output
  $train
  Simulated data of 55 choices.

  $test
  Simulated data of 0 choices.

Code
  train_test(x, test_proportion = 1, by = "N")
Output
  $train
  Simulated data of 0 choices.

  $test
  Simulated data of 55 choices.

Code
  train_test(x, test_proportion = 0.5, by = "N", random = TRUE, seed = 1)
Output
  $train
  Simulated data of 32 choices.

  $test
  Simulated data of 23 choices.

Code
  train_test(x, test_number = 1, by = "N")
Output
  $train
  Simulated data of 45 choices.

  $test
  Simulated data of 10 choices.

Code
  train_test(x, test_number = 2, by = "N")
Output
  $train
  Simulated data of 36 choices.

  $test
  Simulated data of 19 choices.

Code
  train_test(x, test_number = 1, by = "N", random = TRUE, seed = 1)
Output
  $train
  Simulated data of 46 choices.

  $test
  Simulated data of 9 choices.

splitting data set by T works

Code
  train_test(x, test_proportion = 0.3, by = "T")
Output
  $train
  Simulated data of 70 choices.

  $test
  Simulated data of 30 choices.

Code
  train_test(x, test_proportion = 0.5, by = "T", random = TRUE, seed = 1)
Output
  $train
  Simulated data of 50 choices.

  $test
  Simulated data of 50 choices.

Code
  train_test(x, test_number = 1, by = "T")
Output
  $train
  Simulated data of 90 choices.

  $test
  Simulated data of 10 choices.

Code
  train_test(x, test_number = 2, by = "T")
Output
  $train
  Simulated data of 80 choices.

  $test
  Simulated data of 20 choices.

Code
  train_test(x, test_number = 1, by = "T", random = TRUE, seed = 1)
Output
  $train
  Simulated data of 90 choices.

  $test
  Simulated data of 10 choices.

loelschlaeger/RprobitB documentation built on Oct. 15, 2024, 11:08 a.m.