Running 'testthat.R' [175s]
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 2025-08-31 19:12:10 --------------------------
  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_31_01_50_00_8774\Rtmp4iswgs\shapr_obj_8de046653a83.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.