Nothing
tests/testthat/_snaps/print.power_report.md
In graphicalMCP: Graphical Multiple Comparison Procedures
printing Bonferroni power - sequential
Code
graph_calculate_power(g, sim_n = 5, verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0.0 0.5 0.5 0.0
H2 0.0 0.0 0.0 1.0
H3 0.0 0.5 0.0 0.5
H4 0.0 1.0 0.0 0.0
Alpha = 0.025
Test types
bonferroni: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 5 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0 0 0 0
Expected no. of rejections: 0
Power to reject 1 or more: 0
Power to reject all: 0
Simulation details ($details) --------------------------------------------------
p_sim_H1 p_sim_H2 p_sim_H3 p_sim_H4 rej_H1 rej_H2 rej_H3 rej_H4
0.27164 0.57377 0.75558 0.02575 FALSE FALSE FALSE FALSE
0.24822 0.08515 0.60782 0.74755 FALSE FALSE FALSE FALSE
0.69245 0.01727 0.88671 0.92456 FALSE FALSE FALSE FALSE
0.73153 0.50846 0.48313 0.65990 FALSE FALSE FALSE FALSE
0.66310 0.16738 0.35002 0.65456 FALSE FALSE FALSE FALSE
Code
print(graph_calculate_power(g, sim_n = 100), indent = 6, precision = 3)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0.0 0.5 0.5 0.0
H2 0.0 0.0 0.0 1.0
H3 0.0 0.5 0.0 0.5
H4 0.0 1.0 0.0 0.0
Alpha = 0.025
Test types
bonferroni: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.02 0.00 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
printing Simes power
Code
graph_calculate_power(g, test_types = "s", sim_n = 100)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
Test types
simes: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.01 0.01 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
Code
print(graph_calculate_power(g, test_types = "s", sim_n = 100), indent = 6,
precision = 3)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
Test types
simes: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.01 0.01 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
printing parametric power
Code
graph_calculate_power(g, test_types = "p", sim_n = 100, test_corr = list(diag(4)))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0 1 0 0
H2 0 0 1 0
H3 0 0 0 1
H4 0 0 0 0
Alpha = 0.025
Parametric testing correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Test types
parametric: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.02 0.00 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
Code
print(graph_calculate_power(g, test_types = "p", sim_n = 100, test_corr = list(
diag(4))), indent = 6, precision = 3)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0 1 0 0
H2 0 0 1 0
H3 0 0 0 1
H4 0 0 0 0
Alpha = 0.025
Parametric testing correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Test types
parametric: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.02 0.00 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
printing blended power
Code
print(graph_calculate_power(graph = g, alpha = 0.0254871, power_marginal = pi /
seq(0.3, 2.8, by = 0.5) / 11, test_groups = list(4:3, c(6, 1), c(2, 5)),
test_types = c("b", "s", "p"), test_corr = list(NA, NA, t_corr[c(2, 5), c(2, 5)]),
sim_n = 1328, sim_corr = s_corr, sim_success = list(function(.) .[1] || .[5] ||
.[6], function(.) .[2] && (.[5] || .[6])), verbose = TRUE), indent = 0,
precision = 10)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.1666666667
H2: 0.1666666667
H3: 0.1666666667
H4: 0.1666666667
H5: 0.1666666667
H6: 0.1666666667
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0 0.2 0.2 0.2 0.2 0.2
H2 0.2 0.0 0.2 0.2 0.2 0.2
H3 0.2 0.2 0.0 0.2 0.2 0.2
H4 0.2 0.2 0.2 0.0 0.2 0.2
H5 0.2 0.2 0.2 0.2 0.0 0.2
H6 0.2 0.2 0.2 0.2 0.2 0.0
Alpha = 0.0254871
Parametric testing correlation: H2 H5
H2 1.0000000000 0.7853981634
H5 0.7853981634 1.0000000000
Test types
bonferroni: (H4, H3)
simes: (H6, H1)
parametric: (H2, H5)
Simulation parameters ($inputs) ------------------------------------------------
Testing 1,328 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.9519977738 0.3569991652 0.2196917940 0.1586662956
H5 H6
Marginal power: 0.1241736227 0.1019997615
Correlation: H1 H2 H3 H4
H1 1.0000000000 0.7853981634 0.7853981634 0.7853981634
H2 0.7853981634 1.0000000000 0.7853981634 0.7853981634
H3 0.7853981634 0.7853981634 1.0000000000 0.7853981634
H4 0.7853981634 0.7853981634 0.7853981634 1.0000000000
H5 0.7853981634 0.7853981634 0.7853981634 0.7853981634
H6 0.7853981634 0.7853981634 0.7853981634 0.7853981634
H5 H6
0.7853981634 0.7853981634
0.7853981634 0.7853981634
0.7853981634 0.7853981634
0.7853981634 0.7853981634
1.0000000000 0.7853981634
0.7853981634 1.0000000000
Power calculation ($power) -----------------------------------------------------
H1 H2 H3
Local power: 0.84036144578 0.20256024096 0.08960843373
H4 H5 H6
Local power: 0.07304216867 0.06325301205 0.05346385542
Expected no. of rejections: 1.322289157
Power to reject 1 or more: 0.8403614458
Power to reject all: 0.0406626506
Success measure Power
.[1] || .[5] || .[6] 0.84036144578
.[2] && (.[5] || .[6]) 0.07003012048
Simulation details ($details) --------------------------------------------------
p_sim_H1 p_sim_H2 p_sim_H3 p_sim_H4
3.030776739e-07 4.792257190e-03 2.563333001e-02 1.613563087e-03
1.799993896e-03 2.425644224e-01 3.566996361e-01 8.808114728e-02
7.012987714e-04 7.503335761e-02 1.441254616e-01 2.660807415e-01
1.256356378e-06 1.035116345e-02 1.600108848e-02 7.478702995e-03
6.917640824e-11 1.003429108e-06 1.046010311e-03 1.065818840e-06
2.362041390e-03 1.307269410e-01 2.604735863e-01 3.769023080e-01
1.080855471e-05 2.206046776e-02 5.881555020e-03 7.515552106e-02
1.699215401e-05 2.547859799e-03 5.850381205e-03 6.483741341e-03
1.056104200e-04 1.082619625e-01 2.076903618e-01 2.763834889e-01
4.608025515e-04 1.373739092e-01 2.457022772e-01 1.873977665e-01
p_sim_H5 p_sim_H6 rej_H1 rej_H2 rej_H3 rej_H4 rej_H5 rej_H6
1.376906715e-02 8.017449511e-03 TRUE TRUE FALSE TRUE FALSE TRUE
6.334791645e-01 7.130173989e-01 TRUE FALSE FALSE FALSE FALSE FALSE
3.205256009e-01 1.605471054e-01 TRUE FALSE FALSE FALSE FALSE FALSE
1.289967157e-02 2.864565452e-02 TRUE FALSE FALSE FALSE FALSE FALSE
1.435089782e-06 5.607954111e-03 TRUE TRUE TRUE TRUE TRUE TRUE
4.211837866e-01 6.863684924e-01 TRUE FALSE FALSE FALSE FALSE FALSE
5.133590888e-02 3.726628011e-02 TRUE FALSE FALSE FALSE FALSE FALSE
1.791560792e-03 1.110089048e-02 TRUE TRUE TRUE TRUE TRUE TRUE
3.199693067e-01 6.455150468e-01 TRUE FALSE FALSE FALSE FALSE FALSE
1.767959161e-01 1.487829955e-01 TRUE FALSE FALSE FALSE FALSE FALSE
... (Use `print(x, rows = <nn>)` for more)
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printing Bonferroni power - sequential
Code
graph_calculate_power(g, sim_n = 5, verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0.0 0.5 0.5 0.0
H2 0.0 0.0 0.0 1.0
H3 0.0 0.5 0.0 0.5
H4 0.0 1.0 0.0 0.0
Alpha = 0.025
Test types
bonferroni: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 5 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0 0 0 0
Expected no. of rejections: 0
Power to reject 1 or more: 0
Power to reject all: 0
Simulation details ($details) --------------------------------------------------
p_sim_H1 p_sim_H2 p_sim_H3 p_sim_H4 rej_H1 rej_H2 rej_H3 rej_H4
0.27164 0.57377 0.75558 0.02575 FALSE FALSE FALSE FALSE
0.24822 0.08515 0.60782 0.74755 FALSE FALSE FALSE FALSE
0.69245 0.01727 0.88671 0.92456 FALSE FALSE FALSE FALSE
0.73153 0.50846 0.48313 0.65990 FALSE FALSE FALSE FALSE
0.66310 0.16738 0.35002 0.65456 FALSE FALSE FALSE FALSE
Code
print(graph_calculate_power(g, sim_n = 100), indent = 6, precision = 3)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0.0 0.5 0.5 0.0
H2 0.0 0.0 0.0 1.0
H3 0.0 0.5 0.0 0.5
H4 0.0 1.0 0.0 0.0
Alpha = 0.025
Test types
bonferroni: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.02 0.00 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
printing Simes power
Code
graph_calculate_power(g, test_types = "s", sim_n = 100)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
Test types
simes: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.01 0.01 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
Code
print(graph_calculate_power(g, test_types = "s", sim_n = 100), indent = 6,
precision = 3)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
Test types
simes: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.01 0.01 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
printing parametric power
Code
graph_calculate_power(g, test_types = "p", sim_n = 100, test_corr = list(diag(4)))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0 1 0 0
H2 0 0 1 0
H3 0 0 0 1
H4 0 0 0 0
Alpha = 0.025
Parametric testing correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Test types
parametric: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.02 0.00 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
Code
print(graph_calculate_power(g, test_types = "p", sim_n = 100, test_corr = list(
diag(4))), indent = 6, precision = 3)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: 0
--- Transition weights ---
H1 H2 H3 H4
H1 0 1 0 0
H2 0 0 1 0
H3 0 0 0 1
H4 0 0 0 0
Alpha = 0.025
Parametric testing correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Test types
parametric: (H1, H2, H3, H4)
Simulation parameters ($inputs) ------------------------------------------------
Testing 100 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.025 0.025 0.025 0.025
Correlation: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Power calculation ($power) -----------------------------------------------------
H1 H2 H3 H4
Local power: 0.02 0.00 0.00 0.00
Expected no. of rejections: 0.02
Power to reject 1 or more: 0.02
Power to reject all: 0
printing blended power
Code
print(graph_calculate_power(graph = g, alpha = 0.0254871, power_marginal = pi /
seq(0.3, 2.8, by = 0.5) / 11, test_groups = list(4:3, c(6, 1), c(2, 5)),
test_types = c("b", "s", "p"), test_corr = list(NA, NA, t_corr[c(2, 5), c(2, 5)]),
sim_n = 1328, sim_corr = s_corr, sim_success = list(function(.) .[1] || .[5] ||
.[6], function(.) .[2] && (.[5] || .[6])), verbose = TRUE), indent = 0,
precision = 10)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.1666666667
H2: 0.1666666667
H3: 0.1666666667
H4: 0.1666666667
H5: 0.1666666667
H6: 0.1666666667
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0 0.2 0.2 0.2 0.2 0.2
H2 0.2 0.0 0.2 0.2 0.2 0.2
H3 0.2 0.2 0.0 0.2 0.2 0.2
H4 0.2 0.2 0.2 0.0 0.2 0.2
H5 0.2 0.2 0.2 0.2 0.0 0.2
H6 0.2 0.2 0.2 0.2 0.2 0.0
Alpha = 0.0254871
Parametric testing correlation: H2 H5
H2 1.0000000000 0.7853981634
H5 0.7853981634 1.0000000000
Test types
bonferroni: (H4, H3)
simes: (H6, H1)
parametric: (H2, H5)
Simulation parameters ($inputs) ------------------------------------------------
Testing 1,328 simulations with multivariate normal params:
H1 H2 H3 H4
Marginal power: 0.9519977738 0.3569991652 0.2196917940 0.1586662956
H5 H6
Marginal power: 0.1241736227 0.1019997615
Correlation: H1 H2 H3 H4
H1 1.0000000000 0.7853981634 0.7853981634 0.7853981634
H2 0.7853981634 1.0000000000 0.7853981634 0.7853981634
H3 0.7853981634 0.7853981634 1.0000000000 0.7853981634
H4 0.7853981634 0.7853981634 0.7853981634 1.0000000000
H5 0.7853981634 0.7853981634 0.7853981634 0.7853981634
H6 0.7853981634 0.7853981634 0.7853981634 0.7853981634
H5 H6
0.7853981634 0.7853981634
0.7853981634 0.7853981634
0.7853981634 0.7853981634
0.7853981634 0.7853981634
1.0000000000 0.7853981634
0.7853981634 1.0000000000
Power calculation ($power) -----------------------------------------------------
H1 H2 H3
Local power: 0.84036144578 0.20256024096 0.08960843373
H4 H5 H6
Local power: 0.07304216867 0.06325301205 0.05346385542
Expected no. of rejections: 1.322289157
Power to reject 1 or more: 0.8403614458
Power to reject all: 0.0406626506
Success measure Power
.[1] || .[5] || .[6] 0.84036144578
.[2] && (.[5] || .[6]) 0.07003012048
Simulation details ($details) --------------------------------------------------
p_sim_H1 p_sim_H2 p_sim_H3 p_sim_H4
3.030776739e-07 4.792257190e-03 2.563333001e-02 1.613563087e-03
1.799993896e-03 2.425644224e-01 3.566996361e-01 8.808114728e-02
7.012987714e-04 7.503335761e-02 1.441254616e-01 2.660807415e-01
1.256356378e-06 1.035116345e-02 1.600108848e-02 7.478702995e-03
6.917640824e-11 1.003429108e-06 1.046010311e-03 1.065818840e-06
2.362041390e-03 1.307269410e-01 2.604735863e-01 3.769023080e-01
1.080855471e-05 2.206046776e-02 5.881555020e-03 7.515552106e-02
1.699215401e-05 2.547859799e-03 5.850381205e-03 6.483741341e-03
1.056104200e-04 1.082619625e-01 2.076903618e-01 2.763834889e-01
4.608025515e-04 1.373739092e-01 2.457022772e-01 1.873977665e-01
p_sim_H5 p_sim_H6 rej_H1 rej_H2 rej_H3 rej_H4 rej_H5 rej_H6
1.376906715e-02 8.017449511e-03 TRUE TRUE FALSE TRUE FALSE TRUE
6.334791645e-01 7.130173989e-01 TRUE FALSE FALSE FALSE FALSE FALSE
3.205256009e-01 1.605471054e-01 TRUE FALSE FALSE FALSE FALSE FALSE
1.289967157e-02 2.864565452e-02 TRUE FALSE FALSE FALSE FALSE FALSE
1.435089782e-06 5.607954111e-03 TRUE TRUE TRUE TRUE TRUE TRUE
4.211837866e-01 6.863684924e-01 TRUE FALSE FALSE FALSE FALSE FALSE
5.133590888e-02 3.726628011e-02 TRUE FALSE FALSE FALSE FALSE FALSE
1.791560792e-03 1.110089048e-02 TRUE TRUE TRUE TRUE TRUE TRUE
3.199693067e-01 6.455150468e-01 TRUE FALSE FALSE FALSE FALSE FALSE
1.767959161e-01 1.487829955e-01 TRUE FALSE FALSE FALSE FALSE FALSE
... (Use `print(x, rows = <nn>)` for more)
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