tests/testthat/_snaps/regular-output.md

In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values

output_lm_numeric_independence

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp  Month    Day
          <int> <num>   <num>   <num>  <num>  <num>  <num>
  1:          1 42.44  -4.537   8.269 17.517 -5.581 -3.066
  2:          2 42.44   2.250  -3.345 -5.232 -5.581 -1.971
  3:          3 42.44   3.708 -18.610 -1.440 -2.541  1.316

output_lm_numeric_independence_MSEv_Shapley_weights

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp  Month    Day
          <int> <num>   <num>   <num>  <num>  <num>  <num>
  1:          1 42.44  -4.537   8.269 17.517 -5.581 -3.066
  2:          2 42.44   2.250  -3.345 -5.232 -5.581 -1.971
  3:          3 42.44   3.708 -18.610 -1.440 -2.541  1.316

output_lm_numeric_empirical

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Month    Day
          <int> <num>   <num>   <num>  <num>    <num>  <num>
  1:          1 42.44 -13.252  15.541 12.826 -5.77179  3.259
  2:          2 42.44   2.758  -3.325 -7.992 -7.12800  1.808
  3:          3 42.44   6.805 -22.126  3.730 -0.09234 -5.885

output_lm_numeric_empirical_n_coalitions

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 20 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month    Day
          <int> <num>   <num>   <num>  <num>   <num>  <num>
  1:          1 42.44 -13.143  16.584 13.624 -6.3475  1.884
  2:          2 42.44   3.044  -4.511 -8.918 -6.1276  2.632
  3:          3 42.44   5.599 -23.352  4.228 -0.8872 -3.156

output_lm_numeric_empirical_n_coal_unpaired_on_N

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 20 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp  Month    Day
          <int> <num>   <num>   <num>  <num>  <num>  <num>
  1:          1 42.44 -14.780  14.745 13.447 -3.975  3.164
  2:          2 42.44   4.254  -3.284 -7.536 -9.798  2.485
  3:          3 42.44   4.144 -20.306  2.532  3.106 -7.043

output_lm_numeric_empirical_independence

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
Condition
  Warning:
  ! Using empirical.type = 'independence' for approach = 'empirical' is deprecated.
    Please use approach = 'independence' instead.
Message

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
  i `empirical.eta` force set to 1 for `empirical.type` = 'independence'.
Output
     explain_id  none Solar.R    Wind   Temp  Month    Day
          <int> <num>   <num>   <num>  <num>  <num>  <num>
  1:          1 42.44  -4.537   8.269 17.517 -5.581 -3.066
  2:          2 42.44   2.250  -3.345 -5.232 -5.581 -1.971
  3:          3 42.44   3.708 -18.610 -1.440 -2.541  1.316

output_lm_numeric_empirical_AICc_each

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 8 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month      Day
          <int> <num>   <num>   <num>  <num>   <num>    <num>
  1:          1 42.44  -9.778   1.983 16.038   1.983  2.37679
  2:          2 42.44   6.833  -5.547 -9.636  -5.547  0.01837
  3:          3 42.44   6.895 -11.847  3.643 -11.847 -4.41122

output_lm_numeric_empirical_AICc_full

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 8 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month      Day
          <int> <num>   <num>   <num>  <num>   <num>    <num>
  1:          1 42.44  -9.778   1.983 16.038   1.983  2.37679
  2:          2 42.44   6.833  -5.547 -9.636  -5.547  0.01837
  3:          3 42.44   6.895 -11.847  3.643 -11.847 -4.41122

output_lm_numeric_gaussian

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: gaussian
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind     Temp  Month     Day
          <int> <num>   <num>   <num>    <num>  <num>   <num>
  1:          1 42.44  -8.645   7.842  14.4120  0.535 -1.5427
  2:          2 42.44   4.751  -4.814 -11.6985 -1.132 -0.9848
  3:          3 42.44   7.339 -25.590   0.2717 -0.562  0.9729

output_lm_numeric_copula

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: copula
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind    Temp   Month    Day
          <int> <num>   <num>   <num>   <num>   <num>  <num>
  1:          1 42.44  -6.512   7.341  14.357 -0.5201 -2.064
  2:          2 42.44   3.983  -4.656 -10.001 -1.8813 -1.324
  3:          3 42.44   6.076 -25.219   1.754 -1.3488  1.169

output_lm_numeric_ctree

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month    Day
          <int> <num>   <num>   <num>  <num>   <num>  <num>
  1:          1 42.44  -9.198   9.679 16.925 -1.3310 -3.473
  2:          2 42.44   5.283  -6.046 -8.095 -2.7998 -2.222
  3:          3 42.44   6.984 -20.837 -4.762 -0.1545  1.201

output_lm_numeric_vaeac

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: vaeac
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R   Wind  Temp  Month    Day
          <int> <num>   <num>  <num> <num>  <num>  <num>
  1:          1  42.4   -4.94   7.50 17.47 -4.355 -3.069
  2:          2  42.4    1.82  -5.19 -8.94  0.071 -1.638
  3:          3  42.4    4.53 -20.29  3.17 -4.285 -0.698

output_lm_categorical_ctree

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 16`, and is therefore set to `2^n_features = 16`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 4
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 16 of 16 coalitions. 
Output
     explain_id  none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
          <int> <num>        <num>              <num>          <num>       <num>
  1:          1 42.44       -5.719              15.22         -6.220      -3.791
  2:          2 42.44       -5.687             -17.48         22.095     -13.755
  3:          3 42.44        6.839             -21.90          1.997      -5.301

output_lm_categorical_vaeac

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 16`, and is therefore set to `2^n_features = 16`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: vaeac
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 4
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 16 of 16 coalitions. 
Output
     explain_id  none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
          <int> <num>        <num>              <num>          <num>       <num>
  1:          1  42.4        -1.97               12.6          -4.72       -6.38
  2:          2  42.4        -2.41              -14.4          14.43      -12.47
  3:          3  42.4         2.76              -14.2           3.22      -10.10

output_lm_categorical_categorical

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 16`, and is therefore set to `2^n_features = 16`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: categorical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 4
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 16 of 16 coalitions. 
Output
     explain_id  none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
          <int> <num>        <num>              <num>          <num>       <num>
  1:          1 42.44       -5.448              11.31        -11.445       5.078
  2:          2 42.44       -7.493             -12.27         19.672     -14.744
  3:          3 42.44       13.656             -19.73          4.369     -16.659

output_lm_categorical_independence

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 16`, and is therefore set to `2^n_features = 16`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 4
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 16 of 16 coalitions. 
Output
     explain_id  none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
          <int> <num>        <num>              <num>          <num>       <num>
  1:          1 42.44       -5.252              13.95         -7.041      -2.167
  2:          2 42.44       -5.252             -15.61         20.086     -14.050
  3:          3 42.44        4.833             -15.61          0.596      -8.178

output_lm_ts_timeseries

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_groups = 16`, and is therefore set to `2^n_groups = 16`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: timeseries
  * Iterative estimation: FALSE
  * Number of group-wise Shapley values: 4
  * Number of observations to explain: 2

  -- Main computation started --

  i Using 16 of 16 coalitions. 
Output
     explain_id  none      S1     S2       S3      S4
          <int> <num>   <num>  <num>    <num>   <num>
  1:          1 4.895 -0.5261 0.7831 -0.21023 -0.3885
  2:          2 4.895 -0.6310 1.6288 -0.04498 -2.9297

output_lm_numeric_comb1

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: gaussian, empirical, ctree, and independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month     Day
          <int> <num>   <num>   <num>  <num>   <num>   <num>
  1:          1 42.44  -8.987   9.070 15.511 -2.5647 -0.4281
  2:          2 42.44   2.916  -4.516 -7.845 -4.1649 -0.2686
  3:          3 42.44   6.968 -22.988 -1.717  0.6776 -0.5085

output_lm_numeric_comb2

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: ctree, copula, independence, and copula
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind    Temp  Month     Day
          <int> <num>   <num>   <num>   <num>  <num>   <num>
  1:          1 42.44  -9.394   9.435 17.0084 -1.700 -2.7465
  2:          2 42.44   5.227  -5.209 -8.5226 -2.968 -2.4068
  3:          3 42.44   6.186 -22.904 -0.3273 -1.132  0.6081

output_lm_numeric_comb3

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence, empirical, gaussian, and empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp  Month      Day
          <int> <num>   <num>   <num>  <num>  <num>    <num>
  1:          1 42.44  -6.887  10.715 12.199 -3.670  0.24393
  2:          2 42.44   2.603  -2.648 -8.464 -5.405  0.03415
  3:          3 42.44   5.868 -22.184  3.401 -2.955 -1.69888

output_lm_mixed_independence

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Day Month_factor
          <int> <num>   <num>   <num>  <num>  <num>        <num>
  1:          1 42.44  -4.730   7.750 17.753 -2.601       -7.588
  2:          2 42.44   2.338  -3.147 -5.310 -1.676       -7.588
  3:          3 42.44   3.857 -17.469 -1.466  1.099        3.379

output_lm_mixed_ctree

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp     Day Month_factor
          <int> <num>   <num>   <num>  <num>   <num>        <num>
  1:          1 42.44  -9.150  12.057 13.162 -0.8269       -4.658
  2:          2 42.44   4.425  -6.006 -6.260 -0.3910       -7.151
  3:          3 42.44   6.941 -21.427 -7.518  1.3987       10.006

output_lm_mixed_vaeac

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: vaeac
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R   Wind    Temp    Day Month_factor
          <int> <num>   <num>  <num>   <num>  <num>        <num>
  1:          1  42.4   -5.05   6.86 15.7301 -0.208        -6.75
  2:          2  42.4    2.60  -4.64 -2.2641 -3.129        -7.95
  3:          3  42.4    5.14 -17.88 -0.0137  0.585         1.57

output_lm_mixed_comb

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: ctree, independence, ctree, and independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Day Month_factor
          <int> <num>   <num>   <num>  <num>  <num>        <num>
  1:          1 42.44  -7.677  10.757 16.247 -1.446       -7.297
  2:          2 42.44   5.049  -5.028 -6.965 -1.265       -7.174
  3:          3 42.44   5.895 -20.744 -4.468  0.775        7.943

output_custom_lm_numeric_independence_1

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp  Month    Day
          <int> <num>   <num>   <num>  <num>  <num>  <num>
  1:          1 42.44  -4.537   8.269 17.517 -5.581 -3.066
  2:          2 42.44   2.250  -3.345 -5.232 -5.581 -1.971
  3:          3 42.44   3.708 -18.610 -1.440 -2.541  1.316

output_custom_xgboost_mixed_dummy_ctree

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i You passed a model to `shapr::explain()` which is not natively supported, and did not supply a `get_model_specs` function to `shapr::explain()`.
    Consistency checks between model and data is therefore disabled.
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <xgb.Booster>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R   Wind   Temp     Day Month_factor
          <int> <num>   <num>  <num>  <num>   <num>        <num>
  1:          1 42.44  -5.639  13.31  20.93 -0.4716       -0.425
  2:          2 42.44   5.709 -13.30 -16.52  1.4006       -2.738
  3:          3 42.44   6.319 -14.07 -19.77  1.0831        5.870

output_lm_numeric_interaction

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 4`, and is therefore set to `2^n_features = 4`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: independence
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 2
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 4 of 4 coalitions. 
Output
     explain_id  none Solar.R    Wind
          <int> <num>   <num>   <num>
  1:          1 42.44 -13.818  10.579
  2:          2 42.44   4.642  -6.287
  3:          3 42.44   4.452 -34.602

output_lm_numeric_ctree_parallelized

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: ctree
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp   Month    Day
          <int> <num>   <num>   <num>  <num>   <num>  <num>
  1:          1 42.44  -9.198   9.679 16.925 -1.3310 -3.473
  2:          2 42.44   5.283  -6.046 -8.095 -2.7998 -2.222
  3:          3 42.44   6.984 -20.837 -4.762 -0.1545  1.201

output_lm_numeric_empirical_progress

Code
  print({
    out <- code
  }, digits = digits)
Message

  -- Starting `shapr::explain()` -------------------------------------------------
  i `max_n_coalitions` is `NULL` or larger than or `2^n_features = 32`, and is therefore set to `2^n_features = 32`.

  -- Explanation overview --

  * Model class: <lm>
  * Approach: empirical
  * Iterative estimation: FALSE
  * Number of feature-wise Shapley values: 5
  * Number of observations to explain: 3

  -- Main computation started --

  i Using 32 of 32 coalitions. 
Output
     explain_id  none Solar.R    Wind   Temp    Month    Day
          <int> <num>   <num>   <num>  <num>    <num>  <num>
  1:          1 42.44 -13.252  15.541 12.826 -5.77179  3.259
  2:          2 42.44   2.758  -3.325 -7.992 -7.12800  1.808
  3:          3 42.44   6.805 -22.126  3.730 -0.09234 -5.885

NorskRegnesentral/shapr documentation built on June 15, 2025, 6:18 a.m.