tests/testthat/_snaps/asymmetric-causal-output.md
In NorskRegnesentral/shapr: Prediction Explanation with Dependence-Aware Shapley Values
output_asymmetric_conditional
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -24.516 29.347 11.557 -0.626 -3.161
2: 2 42.44 -7.632 8.053 -7.467 -4.634 -2.200
3: 3 42.44 -3.458 -18.240 4.321 -1.347 1.156
output_asym_cond_reg
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: regression_separate
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -11.337 15.032 14.506 -2.656 -2.943
2: 2 42.44 5.546 -6.262 -4.518 -6.664 -1.982
3: 3 42.44 9.720 -32.555 7.270 -3.377 1.374
output_asym_cond_reg_iterative
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: regression_separate
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 5 of 8 coalitions, 5 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 8 coalitions, 3 new.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -11.354 15.015 14.544 -2.658 -2.945
2: 2 42.44 5.553 -6.255 -4.527 -6.666 -1.984
3: 3 42.44 9.720 -32.556 7.270 -3.377 1.374
output_symmetric_conditional
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 -11.395 7.610 15.278 1.3845 -0.2755
2: 2 42.44 2.001 -5.047 -10.833 -0.2829 0.2824
3: 3 42.44 4.589 -25.823 1.138 0.2876 2.2401
output_symmetric_marginal_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
* Causal ordering: {Solar.R, Wind, Temp, Month, Day}
* Components with confounding: {Solar.R, Wind, Temp, Month, Day}
-- 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 -2.644 6.870 16.5974 -0.5859 -7.636
2: 2 42.44 -1.315 -3.251 -6.6438 -5.9780 3.308
3: 3 42.44 -1.114 -10.549 -0.8839 -7.0244 2.004
output_symmetric_marginal_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
* Causal ordering: {Solar.R, Wind, Temp, Month, Day}
* Components with confounding: {Solar.R, Wind, Temp, Month, Day}
-- 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 -8.1241 6.631 15.251 -2.3173 1.161
2: 2 42.44 0.8798 -2.652 -6.971 -1.2012 -3.935
3: 3 42.44 3.3391 -14.550 -3.145 -0.4127 -2.800
output_asym_caus_conf_TRUE
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Month, Day}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -12.804 11.755 17.3723 -0.499 -3.222
2: 2 42.44 1.471 -2.609 -5.9820 -4.592 -2.168
3: 3 42.44 14.736 -31.711 -0.3884 -1.430 1.225
output_asym_caus_conf_FALSE
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: No component with confounding
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -15.4362 17.9420 13.883 -0.626 -3.161
2: 2 42.44 -0.8741 -0.4898 -5.682 -4.634 -2.200
3: 3 42.44 7.2517 -30.3922 5.763 -1.347 1.156
output_asym_caus_conf_mix
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -12.804 11.755 17.4378 -0.626 -3.161
2: 2 42.44 1.471 -2.609 -5.9087 -4.634 -2.200
3: 3 42.44 14.736 -31.711 -0.4028 -1.347 1.156
output_asym_caus_conf_mix_n_coal
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 6 of 6 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 1.488 8.454 6.0255 -1.004 -2.362
2: 2 42.44 1.115 4.578 -13.1691 -4.993 -1.411
3: 3 42.44 19.410 -37.477 0.3474 -1.911 2.062
output_asym_caus_conf_mix_empirical
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -9.609 9.859 17.410 -4.136 -0.9212
2: 2 42.44 14.220 -17.195 -7.333 -1.904 -1.6682
3: 3 42.44 0.661 -20.737 7.258 -5.048 0.2978
output_asym_caus_conf_mix_ctree
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -17.734 20.45 19.217 -5.820 -3.5086
2: 2 42.44 19.188 -15.28 -9.429 -8.159 -0.1952
3: 3 42.44 5.409 -29.78 8.986 -1.464 -0.7140
output_sym_caus_conf_TRUE
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Month, Day}
-- 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 -10.586 9.603 14.085 -2.429 1.9293
2: 2 42.44 1.626 -3.712 -2.724 -7.310 -1.7595
3: 3 42.44 9.581 -25.344 1.892 -4.089 0.3918
output_sym_caus_conf_FALSE
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: No component with confounding
-- 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 -8.606 10.387 13.963 -4.010 0.8685
2: 2 42.44 3.506 -6.071 -4.521 -6.183 -0.6104
3: 3 42.44 2.371 -26.300 2.790 1.600 1.9707
output_sym_caus_conf_mix
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- 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 -10.60 9.600 14.068 -2.464 1.9983
2: 2 42.44 1.62 -3.719 -2.722 -7.284 -1.7747
3: 3 42.44 9.58 -25.345 1.893 -4.005 0.3084
output_sym_caus_conf_TRUE_group
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_groups = 8`, and is therefore set to `2^n_groups = 8`.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 3
* Number of observations to explain: 3
* Causal ordering: {A, B}, {C}
* Components with confounding: {A, B}, {C}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none A B C
<int> <num> <num> <num> <num>
1: 1 42.44 11.547 16.725 -15.67
2: 2 42.44 7.269 -10.685 -10.46
3: 3 42.44 -5.058 1.578 -14.09
output_sym_caus_conf_mix_group
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_groups = 8`, and is therefore set to `2^n_groups = 8`.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 3
* Number of observations to explain: 3
* Causal ordering: {A}, {B}, {C}
* Components with confounding: {A}, {B}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none A B C
<int> <num> <num> <num> <num>
1: 1 42.44 -13.728 31.822 -5.493
2: 2 42.44 3.126 -6.343 -10.662
3: 3 42.44 5.310 -17.036 -5.842
output_sym_caus_conf_mix_group_iterative
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
-- Iteration 1 -----------------------------------------------------------------
-- Convergence info
i Not converged after 6 coalitions:
Current convergence measure: 0.23 [needs 0.02]
Estimated remaining coalitions: 2
(Conservatively) adding about 10% of that (2 coalitions) in the next iteration.
-- Iteration 2 -----------------------------------------------------------------
-- Convergence info
v Converged after 8 coalitions:
All (8) coalitions used.
Maximum number of coalitions reached!
Output
explain_id none A B C
<int> <num> <num> <num> <num>
1: 1 42.44 -6.7937 35.93 -16.534
2: 2 42.44 0.8771 -11.25 -3.507
3: 3 42.44 9.0029 -18.97 -7.599
output_mixed_sym_caus_conf_TRUE
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
-- 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 -1.065 18.16 8.030 -0.1478 -14.394
2: 2 42.44 4.729 -11.40 -7.837 1.6971 -2.570
3: 3 42.44 3.010 -23.62 3.218 4.8728 1.922
output_mixed_sym_caus_conf_TRUE_iterative
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: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 32 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 4 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 6 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 24 of 32 coalitions, 6 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 28 of 32 coalitions, 4 new.
-- Iteration 7 -----------------------------------------------------------------
i Using 30 of 32 coalitions, 2 new.
-- Iteration 8 -----------------------------------------------------------------
i Using 32 of 32 coalitions, 2 new.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -1.185 9.662 9.097 -2.617 -4.372
2: 2 42.44 4.587 -10.237 -5.900 -2.779 -1.054
3: 3 42.44 5.672 -18.778 -2.893 -2.084 7.483
output_mixed_asym_caus_conf_mixed
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -2.8521 17.231 5.46662 -6.018 -3.243
2: 2 42.44 0.6492 -4.826 -0.02641 -5.053 -6.127
3: 3 42.44 -10.7232 -14.690 8.32742 1.080 5.406
output_mixed_asym_caus_conf_mixed_2
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Temp}, {Day, Month_factor}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 1.656 17.903 0.2668 -3.7786 -5.463
2: 2 42.44 -2.941 -6.389 4.8876 -4.4941 -6.446
3: 3 42.44 4.715 -34.627 13.1031 0.4327 5.776
output_mixed_asym_cond_reg
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: regression_separate
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 5 of 8 coalitions, 5 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 8 coalitions, 3 new.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -11.302 15.083 14.284 -2.2810 -5.200
2: 2 42.44 5.496 -6.311 -4.642 -1.6403 -8.286
3: 3 42.44 9.635 -32.764 7.453 0.8939 4.183
output_categorical_asym_causal_mixed_cat
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: 2
* Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor},
{Month_factor}
* Components with confounding: {Solar.R_factor, Wind_factor}
-- 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 -10.128 15.35 -10.26 4.526
2: 2 42.44 -4.316 -10.80 21.06 -20.769
output_cat_asym_causal_mixed_cat_ad
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: TRUE
* Number of feature-wise Shapley values: 4
* Number of observations to explain: 3
* Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor},
{Month_factor}
* Components with confounding: {Solar.R_factor, Wind_factor}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 16 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 16 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 10 of 16 coalitions, 2 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 12 of 16 coalitions, 2 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 14 of 16 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 16 of 16 coalitions, 2 new.
Output
explain_id none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
<int> <num> <num> <num> <num> <num>
1: 1 42.44 -9.089 15.45 -9.112 2.237
2: 2 42.44 -3.126 -10.25 15.463 -16.914
3: 3 42.44 19.458 -21.09 3.881 -20.614
output_categorical_asym_causal_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 = 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
* Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor},
{Month_factor}
* Components with confounding: {Solar.R_factor, Wind_factor}
-- 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 -7.113 11.37 -6.100 1.336
2: 2 42.44 -2.421 -21.49 23.445 -14.366
3: 3 42.44 11.296 -16.94 2.581 -15.297
NorskRegnesentral/shapr documentation built on June 15, 2025, 6:18 a.m.
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output_asymmetric_conditional
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -24.516 29.347 11.557 -0.626 -3.161
2: 2 42.44 -7.632 8.053 -7.467 -4.634 -2.200
3: 3 42.44 -3.458 -18.240 4.321 -1.347 1.156
output_asym_cond_reg
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: regression_separate
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -11.337 15.032 14.506 -2.656 -2.943
2: 2 42.44 5.546 -6.262 -4.518 -6.664 -1.982
3: 3 42.44 9.720 -32.555 7.270 -3.377 1.374
output_asym_cond_reg_iterative
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: regression_separate
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 5 of 8 coalitions, 5 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 8 coalitions, 3 new.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -11.354 15.015 14.544 -2.658 -2.945
2: 2 42.44 5.553 -6.255 -4.527 -6.666 -1.984
3: 3 42.44 9.720 -32.556 7.270 -3.377 1.374
output_symmetric_conditional
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 -11.395 7.610 15.278 1.3845 -0.2755
2: 2 42.44 2.001 -5.047 -10.833 -0.2829 0.2824
3: 3 42.44 4.589 -25.823 1.138 0.2876 2.2401
output_symmetric_marginal_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
* Causal ordering: {Solar.R, Wind, Temp, Month, Day}
* Components with confounding: {Solar.R, Wind, Temp, Month, Day}
-- 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 -2.644 6.870 16.5974 -0.5859 -7.636
2: 2 42.44 -1.315 -3.251 -6.6438 -5.9780 3.308
3: 3 42.44 -1.114 -10.549 -0.8839 -7.0244 2.004
output_symmetric_marginal_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
* Causal ordering: {Solar.R, Wind, Temp, Month, Day}
* Components with confounding: {Solar.R, Wind, Temp, Month, Day}
-- 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 -8.1241 6.631 15.251 -2.3173 1.161
2: 2 42.44 0.8798 -2.652 -6.971 -1.2012 -3.935
3: 3 42.44 3.3391 -14.550 -3.145 -0.4127 -2.800
output_asym_caus_conf_TRUE
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Month, Day}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -12.804 11.755 17.3723 -0.499 -3.222
2: 2 42.44 1.471 -2.609 -5.9820 -4.592 -2.168
3: 3 42.44 14.736 -31.711 -0.3884 -1.430 1.225
output_asym_caus_conf_FALSE
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: No component with confounding
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -15.4362 17.9420 13.883 -0.626 -3.161
2: 2 42.44 -0.8741 -0.4898 -5.682 -4.634 -2.200
3: 3 42.44 7.2517 -30.3922 5.763 -1.347 1.156
output_asym_caus_conf_mix
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -12.804 11.755 17.4378 -0.626 -3.161
2: 2 42.44 1.471 -2.609 -5.9087 -4.634 -2.200
3: 3 42.44 14.736 -31.711 -0.4028 -1.347 1.156
output_asym_caus_conf_mix_n_coal
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 6 of 6 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 1.488 8.454 6.0255 -1.004 -2.362
2: 2 42.44 1.115 4.578 -13.1691 -4.993 -1.411
3: 3 42.44 19.410 -37.477 0.3474 -1.911 2.062
output_asym_caus_conf_mix_empirical
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: empirical
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -9.609 9.859 17.410 -4.136 -0.9212
2: 2 42.44 14.220 -17.195 -7.333 -1.904 -1.6682
3: 3 42.44 0.661 -20.737 7.258 -5.048 0.2978
output_asym_caus_conf_mix_ctree
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Month Day
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -17.734 20.45 19.217 -5.820 -3.5086
2: 2 42.44 19.188 -15.28 -9.429 -8.159 -0.1952
3: 3 42.44 5.409 -29.78 8.986 -1.464 -0.7140
output_sym_caus_conf_TRUE
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Month, Day}
-- 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 -10.586 9.603 14.085 -2.429 1.9293
2: 2 42.44 1.626 -3.712 -2.724 -7.310 -1.7595
3: 3 42.44 9.581 -25.344 1.892 -4.089 0.3918
output_sym_caus_conf_FALSE
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: No component with confounding
-- 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 -8.606 10.387 13.963 -4.010 0.8685
2: 2 42.44 3.506 -6.071 -4.521 -6.183 -0.6104
3: 3 42.44 2.371 -26.300 2.790 1.600 1.9707
output_sym_caus_conf_mix
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Month, Day}
* Components with confounding: {Solar.R, Wind}
-- 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 -10.60 9.600 14.068 -2.464 1.9983
2: 2 42.44 1.62 -3.719 -2.722 -7.284 -1.7747
3: 3 42.44 9.58 -25.345 1.893 -4.005 0.3084
output_sym_caus_conf_TRUE_group
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_groups = 8`, and is therefore set to `2^n_groups = 8`.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 3
* Number of observations to explain: 3
* Causal ordering: {A, B}, {C}
* Components with confounding: {A, B}, {C}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none A B C
<int> <num> <num> <num> <num>
1: 1 42.44 11.547 16.725 -15.67
2: 2 42.44 7.269 -10.685 -10.46
3: 3 42.44 -5.058 1.578 -14.09
output_sym_caus_conf_mix_group
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or `2^n_groups = 8`, and is therefore set to `2^n_groups = 8`.
-- Explanation overview --
* Model class: <lm>
* Approach: gaussian
* Iterative estimation: FALSE
* Number of group-wise Shapley values: 3
* Number of observations to explain: 3
* Causal ordering: {A}, {B}, {C}
* Components with confounding: {A}, {B}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none A B C
<int> <num> <num> <num> <num>
1: 1 42.44 -13.728 31.822 -5.493
2: 2 42.44 3.126 -6.343 -10.662
3: 3 42.44 5.310 -17.036 -5.842
output_sym_caus_conf_mix_group_iterative
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
-- Iteration 1 -----------------------------------------------------------------
-- Convergence info
i Not converged after 6 coalitions:
Current convergence measure: 0.23 [needs 0.02]
Estimated remaining coalitions: 2
(Conservatively) adding about 10% of that (2 coalitions) in the next iteration.
-- Iteration 2 -----------------------------------------------------------------
-- Convergence info
v Converged after 8 coalitions:
All (8) coalitions used.
Maximum number of coalitions reached!
Output
explain_id none A B C
<int> <num> <num> <num> <num>
1: 1 42.44 -6.7937 35.93 -16.534
2: 2 42.44 0.8771 -11.25 -3.507
3: 3 42.44 9.0029 -18.97 -7.599
output_mixed_sym_caus_conf_TRUE
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
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
-- 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 -1.065 18.16 8.030 -0.1478 -14.394
2: 2 42.44 4.729 -11.40 -7.837 1.6971 -2.570
3: 3 42.44 3.010 -23.62 3.218 4.8728 1.922
output_mixed_sym_caus_conf_TRUE_iterative
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: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 32 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 32 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 4 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 6 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 24 of 32 coalitions, 6 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 28 of 32 coalitions, 4 new.
-- Iteration 7 -----------------------------------------------------------------
i Using 30 of 32 coalitions, 2 new.
-- Iteration 8 -----------------------------------------------------------------
i Using 32 of 32 coalitions, 2 new.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -1.185 9.662 9.097 -2.617 -4.372
2: 2 42.44 4.587 -10.237 -5.900 -2.779 -1.054
3: 3 42.44 5.672 -18.778 -2.893 -2.084 7.483
output_mixed_asym_caus_conf_mixed
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Solar.R, Wind}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -2.8521 17.231 5.46662 -6.018 -3.243
2: 2 42.44 0.6492 -4.826 -0.02641 -5.053 -6.127
3: 3 42.44 -10.7232 -14.690 8.32742 1.080 5.406
output_mixed_asym_caus_conf_mixed_2
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: ctree
* Iterative estimation: FALSE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
* Components with confounding: {Temp}, {Day, Month_factor}
-- Main computation started --
i Using 8 of 8 coalitions.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 1.656 17.903 0.2668 -3.7786 -5.463
2: 2 42.44 -2.941 -6.389 4.8876 -4.4941 -6.446
3: 3 42.44 4.715 -34.627 13.1031 0.4327 5.776
output_mixed_asym_cond_reg
Code
print({
out <- code
}, digits = digits)
Message
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than or number of coalitions respecting the causal ordering 8, and is therefore set to 8.
-- Explanation overview --
* Model class: <lm>
* Approach: regression_separate
* Iterative estimation: TRUE
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Number of asymmetric coalitions: 8
* Causal ordering: {Solar.R, Wind}, {Temp}, {Day, Month_factor}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 5 of 8 coalitions, 5 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 8 coalitions, 3 new.
Output
explain_id none Solar.R Wind Temp Day Month_factor
<int> <num> <num> <num> <num> <num> <num>
1: 1 42.44 -11.302 15.083 14.284 -2.2810 -5.200
2: 2 42.44 5.496 -6.311 -4.642 -1.6403 -8.286
3: 3 42.44 9.635 -32.764 7.453 0.8939 4.183
output_categorical_asym_causal_mixed_cat
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: 2
* Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor},
{Month_factor}
* Components with confounding: {Solar.R_factor, Wind_factor}
-- 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 -10.128 15.35 -10.26 4.526
2: 2 42.44 -4.316 -10.80 21.06 -20.769
output_cat_asym_causal_mixed_cat_ad
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: TRUE
* Number of feature-wise Shapley values: 4
* Number of observations to explain: 3
* Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor},
{Month_factor}
* Components with confounding: {Solar.R_factor, Wind_factor}
-- iterative computation started --
-- Iteration 1 -----------------------------------------------------------------
i Using 6 of 16 coalitions, 6 new.
-- Iteration 2 -----------------------------------------------------------------
i Using 8 of 16 coalitions, 2 new.
-- Iteration 3 -----------------------------------------------------------------
i Using 10 of 16 coalitions, 2 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 12 of 16 coalitions, 2 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 14 of 16 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 16 of 16 coalitions, 2 new.
Output
explain_id none Month_factor Ozone_sub30_factor Solar.R_factor Wind_factor
<int> <num> <num> <num> <num> <num>
1: 1 42.44 -9.089 15.45 -9.112 2.237
2: 2 42.44 -3.126 -10.25 15.463 -16.914
3: 3 42.44 19.458 -21.09 3.881 -20.614
output_categorical_asym_causal_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 = 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
* Causal ordering: {Solar.R_factor, Wind_factor}, {Ozone_sub30_factor},
{Month_factor}
* Components with confounding: {Solar.R_factor, Wind_factor}
-- 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 -7.113 11.37 -6.100 1.336
2: 2 42.44 -2.421 -21.49 23.445 -14.366
3: 3 42.44 11.296 -16.94 2.581 -15.297
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