Last updated on 2025-08-27 23:49:11 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.4 | 85.03 | 408.70 | 493.73 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.5 | 50.82 | 285.84 | 336.66 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.5 | 830.59 | OK | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.5 | 854.20 | OK | |||
| r-devel-windows-x86_64 | 1.0.5 | 89.00 | 309.00 | 398.00 | ERROR | |
| r-patched-linux-x86_64 | 1.0.5 | 87.96 | 395.67 | 483.63 | OK | |
| r-release-linux-x86_64 | 1.0.4 | 81.38 | 384.96 | 466.34 | OK | |
| r-release-macos-arm64 | 1.0.5 | 206.00 | OK | |||
| r-release-macos-x86_64 | 1.0.5 | 408.00 | WARN | |||
| r-release-windows-x86_64 | 1.0.4 | 96.00 | 398.00 | 494.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.5 | 196.00 | NOTE | |||
| r-oldrel-macos-x86_64 | 1.0.5 | 359.00 | NOTE | |||
| r-oldrel-windows-x86_64 | 1.0.5 | 85.00 | 347.00 | 432.00 | ERROR |
Version: 1.0.5
Check: tests
Result: ERROR
Running 'testthat.R' [170s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> # CRAN OMP THREAD LIMIT
> Sys.setenv("OMP_THREAD_LIMIT" = 1)
>
> library(testthat)
> library(shapr)
Attaching package: 'shapr'
The following object is masked from 'package:testthat':
setup
>
> test_check("shapr")
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 5
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 5
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 128`, and is therefore set to `2^n_features = 128`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 7
* Number of observations to explain: 2
-- Main computation started --
i Using 128 of 128 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 64`, and is therefore set to `2^n_features = 64`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 6
* Number of observations to explain: 2
-- Main computation started --
i Using 64 of 64 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 2
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence
* Procedure: Iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- 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 10 of 32 coalitions, 2 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 14 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 16 of 32 coalitions, 2 new.
-- Iteration 7 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 8 -----------------------------------------------------------------
i Using 20 of 32 coalitions, 2 new.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- 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 16 of 32 coalitions, 4 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 22 of 32 coalitions, 4 new.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 5
* Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month:
{"Month"}; Day: {"Day"}
* Number of observations to explain: 3
-- 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 16 of 32 coalitions, 4 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 22 of 32 coalitions, 4 new.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 10 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 3
* Feature groups: A: {"Solar.R", "Wind"}; B: {"Temp", "Month_factor"}; C:
{"Day"}
* Number of observations to explain: 3
-- Main computation started --
i Using 6 of 8 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` at 2025-08-26 02:10:22 --------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Computations (temporary) saved at:
'D:\temp\2025_08_26_01_50_00_7068\RtmpIXKhxo\shapr_obj_1bdc8db416ed.rds'
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, gaussian, and copula
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, gaussian, and copula
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, gaussian, and copula
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian, gaussian, gaussian, and gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, independence, and empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, independence, and empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: vaeac
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 10
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Flavor: r-devel-windows-x86_64
Version: 1.0.5
Check: Rd files
Result: WARN
additional_regression_setup.Rd: Sections \title, and \name must exist and be unique in Rd files
aicc_full_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
aicc_full_single_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
append_vS_list.Rd: Sections \title, and \name must exist and be unique in Rd files
categorical_to_one_hot_layer.Rd: Sections \title, and \name must exist and be unique in Rd files
check_categorical_valid_MCsamp.Rd: Sections \title, and \name must exist and be unique in Rd files
check_convergence.Rd: Sections \title, and \name must exist and be unique in Rd files
check_groups.Rd: Sections \title, and \name must exist and be unique in Rd files
check_verbose.Rd: Sections \title, and \name must exist and be unique in Rd files
cli_compute_vS.Rd: Sections \title, and \name must exist and be unique in Rd files
cli_iter.Rd: Sections \title, and \name must exist and be unique in Rd files
cli_startup.Rd: Sections \title, and \name must exist and be unique in Rd files
cli_topline.Rd: Sections \title, and \name must exist and be unique in Rd files
coalition_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
Error writing to connection: No space left on device
compute_estimates.Rd: Sections \title, and \name must exist and be unique in Rd files
compute_shapley.Rd: Sections \title, and \name must exist and be unique in Rd files
compute_time.Rd: Sections \title, and \name must exist and be unique in Rd files
compute_vS.Rd: Sections \title, and \name must exist and be unique in Rd files
convert_feature_name_to_idx.Rd: Sections \title, and \name must exist and be unique in Rd files
correction_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
Error writing to connection: No space left on device
create_ctree.Rd: Sections \title, and \name must exist and be unique in Rd files
create_marginal_data_cat.Rd: Sections \title, and \name must exist and be unique in Rd files
create_marginal_data_gaussian.Rd: Sections \title, and \name must exist and be unique in Rd files
create_marginal_data_training.Rd: Sections \title, and \name must exist and be unique in Rd files
default_doc_export.Rd: Sections \title, and \name must exist and be unique in Rd files
default_doc_internal.Rd: Sections \title, and \name must exist and be unique in Rd files
exact_coalition_table.Rd: Sections \title, and \name must exist and be unique in Rd files
Error writing to connection: No space left on device
Error writing to connection: No space left on device
finalize_explanation.Rd: Sections \title, and \name must exist and be unique in Rd files
format_convergence_info.Rd: Sections \title, and \name must exist and be unique in Rd files
format_info_basic.Rd: Sections \title, and \name must exist and be unique in Rd files
Warning in for (i in seq_along(specs)) { :
closing unused connection 6 ()
Warning in for (i in seq_along(specs)) { :
closing unused connection 5 ()
Warning in for (i in seq_along(specs)) { :
closing unused connection 4 ()
Warning in for (i in seq_along(specs)) { :
closing unused connection 3 ()
format_info_extra.Rd: Sections \title, and \name must exist and be unique in Rd files
format_round.Rd: Sections \title, and \name must exist and be unique in Rd files
format_shapley_info.Rd: Sections \title, and \name must exist and be unique in Rd files
gauss_cat_loss.Rd: Sections \title, and \name must exist and be unique in Rd files
gauss_cat_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files
gauss_cat_sampler_most_likely.Rd: Sections \title, and \name must exist and be unique in Rd files
gauss_cat_sampler_random.Rd: Sections \title, and \name must exist and be unique in Rd files
gaussian_transform.Rd: Sections \title, and \name must exist and be unique in Rd files
gaussian_transform_separate.Rd: Sections \title, and \name must exist and be unique in Rd files
get_S_causal_steps.Rd: Sections \title, and \name must exist and be unique in Rd files
get_cov_mat.Rd: Sections \title, and \name must exist and be unique in Rd files
get_data_forecast.Rd: Sections \title, and \name must exist and be unique in Rd files
get_data_specs.Rd: Sections \title, and \name must exist and be unique in Rd files
Error writing to connection: No space left on device
get_extra_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files
get_feature_specs.Rd: Sections \title, and \name must exist and be unique in Rd files
get_iterative_args_default.Rd: Sections \title, and \name must exist and be unique in Rd files
get_max_n_coalitions_causal.Rd: Sections \title, and \name must exist and be unique in Rd files
get_model_specs.Rd: Sections \title, and \name must exist and be unique in Rd files
get_mu_vec.Rd: Sections \title, and \name must exist and be unique in Rd files
get_nice_time.Rd: Sections \title, and \name must exist and be unique in Rd files
get_output_args_default.Rd: Sections \title, and \name must exist and be unique in Rd files
get_predict_model.Rd: Sections \title, and \name must exist and be unique in Rd files
Error writing to connection: No space left on device
get_supported_approaches.Rd: Sections \title, and \name must exist and be unique in Rd files
get_supported_models.Rd: Sections \title, and \name must exist and be unique in Rd files
get_valid_causal_coalitions.Rd: Sections \title, and \name must exist and be unique in Rd files
group_forecast_setup.Rd: Sections \title, and \name must exist and be unique in Rd files
hat_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
inv_gaussian_transform_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
lag_data.Rd: Sections \title, and \name must exist and be unique in Rd files
mahalanobis_distance_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
mcar_mask_generator.Rd: Sections \title, and \name must exist and be unique in Rd files
memory_layer.Rd: Sections \title, and \name must exist and be unique in Rd files
model_checker.Rd: Sections \title, and \name must exist and be unique in Rd files
num_str.Rd: Sections \title, and \name must exist and be unique in Rd files
observation_impute.Rd: Sections \title, and \name must exist and be unique in Rd files
observation_impute_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
paired_sampler.Rd: Sections \title, and \name must exist and be unique in Rd files
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prepare_data.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_data_causal.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_data_copula_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_data_copula_cpp_caus.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_data_gaussian_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_data_gaussian_cpp_caus.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_data_single_coalition.Rd: Sections \title, and \name must exist and be unique in Rd files
prepare_next_iteration.Rd: Sections \title, and \name must exist and be unique in Rd files
print.shapr.Rd: Sections \title, and \name must exist and be unique in Rd files
print_iter.Rd: Sections \title, and \name must exist and be unique in Rd files
process_factor_data.Rd: Sections \title, and \name must exist and be unique in Rd files
quantile_type7_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
reg_forecast_setup.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.check_namespaces.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.check_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.check_recipe_func.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.check_sur_n_comb.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.check_vfold_cv_para.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.cv_message.Rd: Sections \title, and \name must exist and be unique in Rd files
regression.get_string_to_R.Rd: Sections \title, and \name must exist and be unique in Rd files
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round_manual.Rd: Sections \title, and \name must exist and be unique in Rd files
rss_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
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sample_coalitions_cpp_str_paired.Rd: Sections \title, and \name must exist and be unique in Rd files
sample_combinations.Rd: Sections \title, and \name must exist and be unique in Rd files
sample_ctree.Rd: Sections \title, and \name must exist and be unique in Rd files
save_results.Rd: Sections \title, and \name must exist and be unique in Rd files
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shapley_setup.Rd: Sections \title, and \name must exist and be unique in Rd files
shapley_weights.Rd: Sections \title, and \name must exist and be unique in Rd files
shapr-package.Rd: Sections \title, and \name must exist and be unique in Rd files
skip_connection.Rd: Sections \title, and \name must exist and be unique in Rd files
specified_masks_mask_generator.Rd: Sections \title, and \name must exist and be unique in Rd files
specified_prob_mask_generator.Rd: Sections \title, and \name must exist and be unique in Rd files
summary.shapr.Rd: Sections \title, and \name must exist and be unique in Rd files
test_predict_model.Rd: Sections \title, and \name must exist and be unique in Rd files
testing_cleanup.Rd: Sections \title, and \name must exist and be unique in Rd files
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vaeac_categorical_parse_params.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_activation_func.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_cuda.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_epoch_values.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_extra_named_list.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_logicals.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_mask_gen.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_masking_ratio.Rd: Sections \title, and \name must exist and be unique in Rd files
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vaeac_check_positive_integers.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_positive_numerics.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_probabilities.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_save_names.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_save_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_which_vaeac_model.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_check_x_colnames.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_compute_normalization.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_dataset.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_extend_batch.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_current_save_state.Rd: Sections \title, and \name must exist and be unique in Rd files
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vaeac_get_evaluation_criteria.Rd: Sections \title, and \name must exist and be unique in Rd files
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vaeac_get_full_state_list.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_mask_generator_name.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_model_from_checkp.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_n_decimals.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_optimizer.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_save_file_names.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_val_iwae.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_get_x_explain_extended.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_impute_missing_entries.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_kl_normal_normal.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_normal_parse_params.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_normalize_data.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_postprocess_data.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_preprocess_data.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_print_train_summary.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_save_state.Rd: Sections \title, and \name must exist and be unique in Rd files
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vaeac_train_model_continue.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_update_para_locations.Rd: Sections \title, and \name must exist and be unique in Rd files
vaeac_update_pretrained_model.Rd: Sections \title, and \name must exist and be unique in Rd files
weight_matrix.Rd: Sections \title, and \name must exist and be unique in Rd files
weight_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files
problems found in ‘additional_regression_setup.Rd’, ‘aicc_full_cpp.Rd’, ‘aicc_full_single_cpp.Rd’, ‘append_vS_list.Rd’, ‘categorical_to_one_hot_layer.Rd’, ‘check_categorical_valid_MCsamp.Rd’, ‘check_convergence.Rd’, ‘check_groups.Rd’, ‘check_verbose.Rd’, ‘cli_compute_vS.Rd’, ‘cli_iter.Rd’, ‘cli_startup.Rd’, ‘cli_topline.Rd’, ‘coalition_matrix_cpp.Rd’, ‘compute_MSEv_eval_crit.Rd’, ‘compute_estimates.Rd’, ‘compute_shapley.Rd’, ‘compute_time.Rd’, ‘compute_vS.Rd’, ‘convert_feature_name_to_idx.Rd’, ‘correction_matrix_cpp.Rd’, ‘create_coalition_table.Rd’, ‘create_ctree.Rd’, ‘create_marginal_data_cat.Rd’, ‘create_marginal_data_gaussian.Rd’, ‘create_marginal_data_training.Rd’, ‘default_doc_export.Rd’, ‘default_doc_internal.Rd’, ‘exact_coalition_table.Rd’, ‘explain.Rd’, ‘explain_forecast.Rd’, ‘finalize_explanation.Rd’, ‘format_convergence_info.Rd’, ‘format_info_basic.Rd’, ‘format_info_extra.Rd’, ‘format_round.Rd’, ‘format_shapley_info.Rd’, ‘gauss_cat_loss.Rd’, ‘gauss_cat_parameters.Rd’, ‘gauss_cat_sampler_most_likely.Rd’, ‘gauss_cat_sampler_random.Rd’, ‘gaussian_transform.Rd’, ‘gaussian_transform_separate.Rd’, ‘get_S_causal_steps.Rd’, ‘get_cov_mat.Rd’, ‘get_data_forecast.Rd’, ‘get_data_specs.Rd’, ‘get_extra_comp_args_default.Rd’, ‘get_extra_parameters.Rd’, ‘get_feature_specs.Rd’, ‘get_iterative_args_default.Rd’, ‘get_max_n_coalitions_causal.Rd’, ‘get_model_specs.Rd’, ‘get_mu_vec.Rd’, ‘get_nice_time.Rd’, ‘get_output_args_default.Rd’, ‘get_predict_model.Rd’, ‘get_results.Rd’, ‘get_supported_approaches.Rd’, ‘get_supported_models.Rd’, ‘get_valid_causal_coalitions.Rd’, ‘group_forecast_setup.Rd’, ‘hat_matrix_cpp.Rd’, ‘inv_gaussian_transform_cpp.Rd’, ‘lag_data.Rd’, ‘mahalanobis_distance_cpp.Rd’, ‘mcar_mask_generator.Rd’, ‘memory_layer.Rd’, ‘model_checker.Rd’, ‘num_str.Rd’, ‘observation_impute.Rd’, ‘observation_impute_cpp.Rd’, ‘paired_sampler.Rd’, ‘plot.shapr.Rd’, ‘plot_MSEv_eval_crit.Rd’, ‘plot_SV_several_approaches.Rd’, ‘plot_vaeac_eval_crit.Rd’, ‘plot_vaeac_imputed_ggpairs.Rd’, ‘predict_model.Rd’, ‘prepare_data.Rd’, ‘prepare_data_causal.Rd’, ‘prepare_data_copula_cpp.Rd’, ‘prepare_data_copula_cpp_caus.Rd’, ‘prepare_data_gaussian_cpp.Rd’, ‘prepare_data_gaussian_cpp_caus.Rd’, ‘prepare_data_single_coalition.Rd’, ‘prepare_next_iteration.Rd’, ‘print.shapr.Rd’, ‘print_iter.Rd’, ‘process_factor_data.Rd’, ‘quantile_type7_cpp.Rd’, ‘reg_forecast_setup.Rd’, ‘regression.check_namespaces.Rd’, ‘regression.check_parameters.Rd’, ‘regression.check_recipe_func.Rd’, ‘regression.check_sur_n_comb.Rd’, ‘regression.check_vfold_cv_para.Rd’, ‘regression.cv_message.Rd’, ‘regression.get_string_to_R.Rd’, ‘round_manual.Rd’, ‘rss_cpp.Rd’, ‘sample_coalition_table.Rd’, ‘sample_coalitions_cpp_str_paired.Rd’, ‘sample_combinations.Rd’, ‘sample_ctree.Rd’, ‘save_results.Rd’, ‘setup.Rd’, ‘setup_approach.Rd’, ‘shapley_setup.Rd’, ‘shapley_weights.Rd’, ‘shapr-package.Rd’, ‘skip_connection.Rd’, ‘specified_masks_mask_generator.Rd’, ‘specified_prob_mask_generator.Rd’, ‘summary.shapr.Rd’, ‘test_predict_model.Rd’, ‘testing_cleanup.Rd’, ‘vaeac.Rd’, ‘vaeac_categorical_parse_params.Rd’, ‘vaeac_check_activation_func.Rd’, ‘vaeac_check_cuda.Rd’, ‘vaeac_check_epoch_values.Rd’, ‘vaeac_check_extra_named_list.Rd’, ‘vaeac_check_logicals.Rd’, ‘vaeac_check_mask_gen.Rd’, ‘vaeac_check_masking_ratio.Rd’, ‘vaeac_check_parameters.Rd’, ‘vaeac_check_positive_integers.Rd’, ‘vaeac_check_positive_numerics.Rd’, ‘vaeac_check_probabilities.Rd’, ‘vaeac_check_save_names.Rd’, ‘vaeac_check_save_parameters.Rd’, ‘vaeac_check_which_vaeac_model.Rd’, ‘vaeac_check_x_colnames.Rd’, ‘vaeac_compute_normalization.Rd’, ‘vaeac_dataset.Rd’, ‘vaeac_extend_batch.Rd’, ‘vaeac_get_current_save_state.Rd’, ‘vaeac_get_data_objects.Rd’, ‘vaeac_get_evaluation_criteria.Rd’, ‘vaeac_get_extra_para_default.Rd’, ‘vaeac_get_full_state_list.Rd’, ‘vaeac_get_mask_generator_name.Rd’, ‘vaeac_get_model_from_checkp.Rd’, ‘vaeac_get_n_decimals.Rd’, ‘vaeac_get_optimizer.Rd’, ‘vaeac_get_save_file_names.Rd’, ‘vaeac_get_val_iwae.Rd’, ‘vaeac_get_x_explain_extended.Rd’, ‘vaeac_impute_missing_entries.Rd’, ‘vaeac_kl_normal_normal.Rd’, ‘vaeac_normal_parse_params.Rd’, ‘vaeac_normalize_data.Rd’, ‘vaeac_postprocess_data.Rd’, ‘vaeac_preprocess_data.Rd’, ‘vaeac_print_train_summary.Rd’, ‘vaeac_save_state.Rd’, ‘vaeac_train_model.Rd’, ‘vaeac_train_model_auxiliary.Rd’, ‘vaeac_train_model_continue.Rd’, ‘vaeac_update_para_locations.Rd’, ‘vaeac_update_pretrained_model.Rd’, ‘weight_matrix.Rd’, ‘weight_matrix_cpp.Rd’
Flavor: r-release-macos-x86_64
Version: 1.0.5
Check: installed package size
Result: NOTE
installed size is 8.6Mb
sub-directories of 1Mb or more:
doc 3.3Mb
libs 4.1Mb
Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64
Version: 1.0.5
Check: tests
Result: ERROR
Running 'testthat.R' [178s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> # CRAN OMP THREAD LIMIT
> Sys.setenv("OMP_THREAD_LIMIT" = 1)
>
> library(testthat)
> library(shapr)
Attaching package: 'shapr'
The following object is masked from 'package:testthat':
setup
>
> test_check("shapr")
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 5
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 5
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 128`, and is therefore set to `2^n_features = 128`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 7
* Number of observations to explain: 2
-- Main computation started --
i Using 128 of 128 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 64`, and is therefore set to `2^n_features = 64`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 6
* Number of observations to explain: 2
-- Main computation started --
i Using 64 of 64 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 2
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
-- Starting `shapr::explain_forecast()` ----------------------------------------
i Feature names extracted from the model contain `NA`.
Consistency checks between model and data are therefore disabled.
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`.
-- Explanation overview --
* Model class: <Arima>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 2
* Number of observations to explain: 2
-- Main computation started --
i Using 4 of 4 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence
* Procedure: Iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- 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 10 of 32 coalitions, 2 new.
-- Iteration 4 -----------------------------------------------------------------
i Using 12 of 32 coalitions, 2 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 14 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 16 of 32 coalitions, 2 new.
-- Iteration 7 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 8 -----------------------------------------------------------------
i Using 20 of 32 coalitions, 2 new.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- 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 16 of 32 coalitions, 4 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 22 of 32 coalitions, 4 new.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 5
* Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month:
{"Month"}; Day: {"Day"}
* Number of observations to explain: 3
-- 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 16 of 32 coalitions, 4 new.
-- Iteration 5 -----------------------------------------------------------------
i Using 18 of 32 coalitions, 2 new.
-- Iteration 6 -----------------------------------------------------------------
i Using 22 of 32 coalitions, 4 new.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 10 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of group-wise Shapley values: 3
* Feature groups: A: {"Solar.R", "Wind"}; B: {"Temp", "Month_factor"}; C:
{"Day"}
* Number of observations to explain: 3
-- Main computation started --
i Using 6 of 8 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: ctree
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` at 2025-08-27 02:10:23 --------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
* Computations (temporary) saved at:
'D:\temp\2025_08_27_01_50_00_27070\RtmpIzVbf9\shapr_obj_2c8942a9149ed.rds'
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, gaussian, and copula
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, gaussian, and copula
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, gaussian, and copula
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: gaussian, gaussian, gaussian, and gaussian
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, independence, and empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: independence, empirical, independence, and empirical
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 1000
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
-- Starting `shapr::explain()` -------------------------------------------------
i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`.
-- Explanation overview --
* Model class: <lm>
* v(S) estimation class: Monte Carlo integration
* Approach: vaeac
* Procedure: Non-iterative
* Number of Monte Carlo integration samples: 10
* Number of feature-wise Shapley values: 5
* Number of observations to explain: 3
-- Main computation started --
i Using 32 of 32 coalitions.
Flavor: r-oldrel-windows-x86_64
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