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tests/testthat/_snaps/print.graph_report.md
In graphicalMCP: Graphical Multiple Comparison Procedures
printing Bonferroni/Simes closure test
Code
graph_test_closure(par_gate, rep(0.01, 4), test_types = "s")
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
simes: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.01 TRUE
H2 0.01 TRUE
H3 0.01 TRUE
H4 0.01 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Code
graph_test_closure(par_gate, rep(0.01, 4), verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_inter reject_intersection
1111 0.5 0.5 0.0 0.0 0.02 0.02 TRUE
1110 0.5 0.5 0.0 NA 0.02 0.02 TRUE
1101 0.5 0.5 NA 0.0 0.02 0.02 TRUE
1100 0.5 0.5 NA NA 0.02 0.02 TRUE
1011 0.5 NA 0.0 0.5 0.02 0.02 TRUE
1010 1.0 NA 0.0 NA 0.01 0.01 TRUE
1001 0.5 NA NA 0.5 0.02 0.02 TRUE
1000 1.0 NA NA NA 0.01 0.01 TRUE
0111 NA 0.5 0.5 0.0 0.02 0.02 TRUE
0110 NA 0.5 0.5 NA 0.02 0.02 TRUE
... (Use `print(x, rows = <nn>)` for more)
Code
graph_test_closure(par_gate, rep(0.01, 4), test_values = TRUE)
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= Weight * Alpha Inequality_holds
1111 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1111 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1110 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1101 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
... (Use `print(x, rows = <nn>)` for more)
printing parametric closure test
Code
graph_test_closure(par_gate, rep(0.01, 4), test_types = "p", test_corr = list(
diag(4)))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Correlation matrix: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Test types
parametric: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.0199 TRUE
H2 0.0199 TRUE
H3 0.0199 TRUE
H4 0.0199 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Code
graph_test_closure(par_gate, rep(0.01, 4), test_groups = list(1:2, 3:4),
test_types = c("p", "s"), test_corr = list(diag(2), NA), test_values = TRUE,
verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Correlation matrix: H1 H2
H1 1 0
H2 0 1
Test types
parametric: (H1, H2)
simes: (H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_grp2 adj_p_inter
1111 0.5 0.5 0.0 0.0 0.0199 1.00 0.0199
1110 0.5 0.5 0.0 NA 0.0199 1.00 0.0199
1101 0.5 0.5 NA 0.0 0.0199 1.00 0.0199
1100 0.5 0.5 NA NA 0.0199 1.00 0.0199
1011 0.5 NA 0.0 0.5 0.0200 0.02 0.0200
1010 1.0 NA 0.0 NA 0.0100 1.00 0.0100
1001 0.5 NA NA 0.5 0.0200 0.02 0.0200
1000 1.0 NA NA NA 0.0100 1.00 0.0100
0111 NA 0.5 0.5 0.0 0.0200 0.02 0.0200
0110 NA 0.5 0.5 NA 0.0200 0.02 0.0200
reject_intersection
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
... (Use `print(x, rows = <nn>)` for more)
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= c_value * Weight * Alpha
1111 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H3 simes 0.01 <= * 0.0 * 0.025
1111 H4 simes 0.01 <= * 0.0 * 0.025
1110 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H3 simes 0.01 <= * 0.0 * 0.025
1101 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H4 simes 0.01 <= * 0.0 * 0.025
Inequality_holds
TRUE
TRUE
FALSE
FALSE
TRUE
TRUE
FALSE
TRUE
TRUE
FALSE
... (Use `print(x, rows = <nn>)` for more)
Code
graph_test_closure(par_gate, rep(0.01, 4), test_groups = list(1:2, 3:4),
test_types = c("p", "p"), test_corr = list(diag(2), diag(2)), test_values = TRUE,
verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Correlation matrix: H1 H2 H3 H4
H1 1 0 NA NA
H2 0 1 NA NA
H3 NA NA 1 0
H4 NA NA 0 1
Test types
parametric: (H1, H2)
parametric: (H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_grp2 adj_p_inter
1111 0.5 0.5 0.0 0.0 0.0199 1.0000 0.0199
1110 0.5 0.5 0.0 NA 0.0199 1.0000 0.0199
1101 0.5 0.5 NA 0.0 0.0199 1.0000 0.0199
1100 0.5 0.5 NA NA 0.0199 1.0000 0.0199
1011 0.5 NA 0.0 0.5 0.0200 0.0200 0.0200
1010 1.0 NA 0.0 NA 0.0100 1.0000 0.0100
1001 0.5 NA NA 0.5 0.0200 0.0200 0.0200
1000 1.0 NA NA NA 0.0100 1.0000 0.0100
0111 NA 0.5 0.5 0.0 0.0200 0.0200 0.0200
0110 NA 0.5 0.5 NA 0.0200 0.0200 0.0200
reject_intersection
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
... (Use `print(x, rows = <nn>)` for more)
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= c_value * Weight * Alpha
1111 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H3 parametric 0.01 <= 1.000 * 0.0 * 0.025
1111 H4 parametric 0.01 <= 1.000 * 0.0 * 0.025
1110 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H3 parametric 0.01 <= 1.000 * 0.0 * 0.025
1101 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H4 parametric 0.01 <= 1.000 * 0.0 * 0.025
Inequality_holds
TRUE
TRUE
FALSE
FALSE
TRUE
TRUE
FALSE
TRUE
TRUE
FALSE
... (Use `print(x, rows = <nn>)` for more)
printing Bonferroni sequential results
Code
graph_test_shortcut(simple_successive_1(), rep(0.01, 4))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Code
graph_test_shortcut(simple_successive_1(), rep(0.01, 4), verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Step 1: Updated graph after removing hypothesis H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA 0 0 1
H3 NA 1 0 0
H4 NA 0 1 0
Step 2: Updated graph after removing hypotheses H1, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: 0.5
H4: 0.5
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA 0 1
H4 NA NA 1 0
Step 3: Updated graph after removing hypotheses H1, H2, H3
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: 1
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA 0
Step 4: Updated graph after removing hypotheses H1, H2, H3, H4
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
add alternate orderings
Code
test_res_alt
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
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
Step 1: Updated graph after removing hypothesis H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA 0 0 1
H3 NA 1 0 0
H4 NA 0 1 0
Step 2: Updated graph after removing hypotheses H1, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: 0.5
H4: 0.5
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA 0 1
H4 NA NA 1 0
Step 3: Updated graph after removing hypotheses H1, H2, H3
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: 1
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA 0
Step 4: Updated graph after removing hypotheses H1, H2, H3, H4
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Alternate rejection orderings ($valid_rejection_orderings) ---------------------
H1 H2 H3 H4
1 2 3 4
H1 H2 H4 H3
1 2 4 3
H1 H3 H2 H4
1 3 2 4
H2 H1 H3 H4
2 1 3 4
H2 H1 H4 H3
2 1 4 3
H2 H4 H1 H3
2 4 1 3
additional printing options for graph report
Code
print(graph_test_closure(par_gate, rep(0.01, 4), verbose = TRUE, test_values = TRUE),
precison = 4, indent = 4)
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_inter reject_intersection
1111 0.5 0.5 0.0 0.0 0.02 0.02 TRUE
1110 0.5 0.5 0.0 NA 0.02 0.02 TRUE
1101 0.5 0.5 NA 0.0 0.02 0.02 TRUE
1100 0.5 0.5 NA NA 0.02 0.02 TRUE
1011 0.5 NA 0.0 0.5 0.02 0.02 TRUE
1010 1.0 NA 0.0 NA 0.01 0.01 TRUE
1001 0.5 NA NA 0.5 0.02 0.02 TRUE
1000 1.0 NA NA NA 0.01 0.01 TRUE
0111 NA 0.5 0.5 0.0 0.02 0.02 TRUE
0110 NA 0.5 0.5 NA 0.02 0.02 TRUE
... (Use `print(x, rows = <nn>)` for more)
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= Weight * Alpha Inequality_holds
1111 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1111 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1110 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1101 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
... (Use `print(x, rows = <nn>)` for more)
Code
print(graph_test_shortcut(simple_successive_1(), rep(0.01, 4), verbose = TRUE,
test_values = TRUE), precision = 7, indent = 9)
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
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
Step 1: Updated graph after removing hypothesis H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA 0 0 1
H3 NA 1 0 0
H4 NA 0 1 0
Step 2: Updated graph after removing hypotheses H1, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: 0.5
H4: 0.5
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA 0 1
H4 NA NA 1 0
Step 3: Updated graph after removing hypotheses H1, H2, H3
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: 1
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA 0
Step 4: Updated graph after removing hypotheses H1, H2, H3, H4
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Step Hypothesis p <= Weight * Alpha Inequality_holds
1 H1 0.01 <= 0.5 * 0.025 TRUE
2 H2 0.01 <= 0.5 * 0.025 TRUE
3 H3 0.01 <= 0.5 * 0.025 TRUE
4 H4 0.01 <= 1.0 * 0.025 TRUE
Code
print(graph_test_shortcut(two_doses_two_primary_two_secondary(), 5:0 / 200,
verbose = TRUE, test_values = TRUE))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Alpha = 0.025
H1 H2 H3 H4 H5 H6
Unadjusted p-values: 0.025 0.020 0.015 0.010 0.005 0.000
Test types
bonferroni: (H1, H2, H3, H4, H5, H6)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.025 TRUE
H2 0.030 FALSE
H3 0.030 FALSE
H4 0.020 TRUE
H5 0.020 TRUE
H6 0.020 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Step 1: Updated graph after removing hypothesis H4
--- Hypothesis weights ---
H1: 0.50
H2: 0.00
H3: 0.00
H4: NA
H5: 0.25
H6: 0.25
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.00000 0.50000 0.50000 NA 0.00000 0.00000
H2 0.00000 0.00000 1.00000 NA 0.00000 0.00000
H3 0.00000 0.99990 0.00000 NA 0.00005 0.00005
H4 NA NA NA NA NA NA
H5 0.00000 0.00000 0.00000 NA 0.00000 1.00000
H6 0.00010 0.00000 0.00000 NA 0.99990 0.00000
Step 2: Updated graph after removing hypotheses H4, H6
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: NA
H5: 0.5
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5
H1 0.000000000 0.500000000 0.500000000 NA 0.000000000
H2 0.000000000 0.000000000 1.000000000 NA 0.000000000
H3 0.000000005 0.999900000 0.000000000 NA 0.000099995
H4 NA NA NA NA NA
H5 1.000000000 0.000000000 0.000000000 NA 0.000000000
H6 NA NA NA NA NA
H6
NA
NA
NA
NA
NA
NA
Step 3: Updated graph after removing hypotheses H4, H6, H5
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 NA NA NA
H2 0.0000 0.0000 1.0000 NA NA NA
H3 0.0001 0.9999 0.0000 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 4: Updated graph after removing hypotheses H4, H6, H5, H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Step Hypothesis p <= Weight * Alpha Inequality_holds
1 H4 0.010 <= 0.50 * 0.025 TRUE
2 H6 0.000 <= 0.25 * 0.025 TRUE
3 H5 0.005 <= 0.50 * 0.025 TRUE
4 H1 0.025 <= 1.00 * 0.025 TRUE
5 H3 0.015 <= 0.50 * 0.025 FALSE
5 H2 0.020 <= 0.50 * 0.025 FALSE
Code
print(graph_rejection_orderings(graph_test_shortcut(
two_doses_two_primary_two_secondary(), 6:1 / 400, verbose = TRUE,
test_values = TRUE)))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Alpha = 0.025
H1 H2 H3 H4 H5 H6
Unadjusted p-values: 0.0150 0.0125 0.0100 0.0075 0.0050 0.0025
Test types
bonferroni: (H1, H2, H3, H4, H5, H6)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.015 TRUE
H2 0.020 TRUE
H3 0.020 TRUE
H4 0.015 TRUE
H5 0.015 TRUE
H6 0.015 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA NA NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Step 1: Updated graph after removing hypothesis H4
--- Hypothesis weights ---
H1: 0.50
H2: 0.00
H3: 0.00
H4: NA
H5: 0.25
H6: 0.25
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.00000 0.50000 0.50000 NA 0.00000 0.00000
H2 0.00000 0.00000 1.00000 NA 0.00000 0.00000
H3 0.00000 0.99990 0.00000 NA 0.00005 0.00005
H4 NA NA NA NA NA NA
H5 0.00000 0.00000 0.00000 NA 0.00000 1.00000
H6 0.00010 0.00000 0.00000 NA 0.99990 0.00000
Step 2: Updated graph after removing hypotheses H4, H6
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: NA
H5: 0.5
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5
H1 0.000000000 0.500000000 0.500000000 NA 0.000000000
H2 0.000000000 0.000000000 1.000000000 NA 0.000000000
H3 0.000000005 0.999900000 0.000000000 NA 0.000099995
H4 NA NA NA NA NA
H5 1.000000000 0.000000000 0.000000000 NA 0.000000000
H6 NA NA NA NA NA
H6
NA
NA
NA
NA
NA
NA
Step 3: Updated graph after removing hypotheses H4, H6, H5
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 NA NA NA
H2 0.0000 0.0000 1.0000 NA NA NA
H3 0.0001 0.9999 0.0000 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 4: Updated graph after removing hypotheses H4, H6, H5, H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 5: Updated graph after removing hypotheses H4, H6, H5, H1, H3
--- Hypothesis weights ---
H1: NA
H2: 1
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 6: Updated graph after removing hypotheses H4, H6, H5, H1, H3, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA NA NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA NA NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Step Hypothesis p <= Weight * Alpha Inequality_holds
1 H4 0.0075 <= 0.50 * 0.025 TRUE
2 H6 0.0025 <= 0.25 * 0.025 TRUE
3 H5 0.0050 <= 0.50 * 0.025 TRUE
4 H1 0.0150 <= 1.00 * 0.025 TRUE
5 H3 0.0100 <= 0.50 * 0.025 TRUE
6 H2 0.0125 <= 1.00 * 0.025 TRUE
Alternate rejection orderings ($valid_rejection_orderings) ---------------------
H4 H5 H6 H1 H2 H3
4 5 6 1 2 3
H4 H5 H6 H1 H3 H2
4 5 6 1 3 2
H4 H6 H5 H1 H2 H3
4 6 5 1 2 3
H4 H6 H5 H1 H3 H2
4 6 5 1 3 2
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printing Bonferroni/Simes closure test
Code
graph_test_closure(par_gate, rep(0.01, 4), test_types = "s")
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
simes: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.01 TRUE
H2 0.01 TRUE
H3 0.01 TRUE
H4 0.01 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Code
graph_test_closure(par_gate, rep(0.01, 4), verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_inter reject_intersection
1111 0.5 0.5 0.0 0.0 0.02 0.02 TRUE
1110 0.5 0.5 0.0 NA 0.02 0.02 TRUE
1101 0.5 0.5 NA 0.0 0.02 0.02 TRUE
1100 0.5 0.5 NA NA 0.02 0.02 TRUE
1011 0.5 NA 0.0 0.5 0.02 0.02 TRUE
1010 1.0 NA 0.0 NA 0.01 0.01 TRUE
1001 0.5 NA NA 0.5 0.02 0.02 TRUE
1000 1.0 NA NA NA 0.01 0.01 TRUE
0111 NA 0.5 0.5 0.0 0.02 0.02 TRUE
0110 NA 0.5 0.5 NA 0.02 0.02 TRUE
... (Use `print(x, rows = <nn>)` for more)
Code
graph_test_closure(par_gate, rep(0.01, 4), test_values = TRUE)
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= Weight * Alpha Inequality_holds
1111 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1111 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1110 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1101 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
... (Use `print(x, rows = <nn>)` for more)
printing parametric closure test
Code
graph_test_closure(par_gate, rep(0.01, 4), test_types = "p", test_corr = list(
diag(4)))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Correlation matrix: H1 H2 H3 H4
H1 1 0 0 0
H2 0 1 0 0
H3 0 0 1 0
H4 0 0 0 1
Test types
parametric: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.0199 TRUE
H2 0.0199 TRUE
H3 0.0199 TRUE
H4 0.0199 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Code
graph_test_closure(par_gate, rep(0.01, 4), test_groups = list(1:2, 3:4),
test_types = c("p", "s"), test_corr = list(diag(2), NA), test_values = TRUE,
verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Correlation matrix: H1 H2
H1 1 0
H2 0 1
Test types
parametric: (H1, H2)
simes: (H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_grp2 adj_p_inter
1111 0.5 0.5 0.0 0.0 0.0199 1.00 0.0199
1110 0.5 0.5 0.0 NA 0.0199 1.00 0.0199
1101 0.5 0.5 NA 0.0 0.0199 1.00 0.0199
1100 0.5 0.5 NA NA 0.0199 1.00 0.0199
1011 0.5 NA 0.0 0.5 0.0200 0.02 0.0200
1010 1.0 NA 0.0 NA 0.0100 1.00 0.0100
1001 0.5 NA NA 0.5 0.0200 0.02 0.0200
1000 1.0 NA NA NA 0.0100 1.00 0.0100
0111 NA 0.5 0.5 0.0 0.0200 0.02 0.0200
0110 NA 0.5 0.5 NA 0.0200 0.02 0.0200
reject_intersection
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
... (Use `print(x, rows = <nn>)` for more)
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= c_value * Weight * Alpha
1111 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H3 simes 0.01 <= * 0.0 * 0.025
1111 H4 simes 0.01 <= * 0.0 * 0.025
1110 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H3 simes 0.01 <= * 0.0 * 0.025
1101 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H4 simes 0.01 <= * 0.0 * 0.025
Inequality_holds
TRUE
TRUE
FALSE
FALSE
TRUE
TRUE
FALSE
TRUE
TRUE
FALSE
... (Use `print(x, rows = <nn>)` for more)
Code
graph_test_closure(par_gate, rep(0.01, 4), test_groups = list(1:2, 3:4),
test_types = c("p", "p"), test_corr = list(diag(2), diag(2)), test_values = TRUE,
verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Correlation matrix: H1 H2 H3 H4
H1 1 0 NA NA
H2 0 1 NA NA
H3 NA NA 1 0
H4 NA NA 0 1
Test types
parametric: (H1, H2)
parametric: (H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_grp2 adj_p_inter
1111 0.5 0.5 0.0 0.0 0.0199 1.0000 0.0199
1110 0.5 0.5 0.0 NA 0.0199 1.0000 0.0199
1101 0.5 0.5 NA 0.0 0.0199 1.0000 0.0199
1100 0.5 0.5 NA NA 0.0199 1.0000 0.0199
1011 0.5 NA 0.0 0.5 0.0200 0.0200 0.0200
1010 1.0 NA 0.0 NA 0.0100 1.0000 0.0100
1001 0.5 NA NA 0.5 0.0200 0.0200 0.0200
1000 1.0 NA NA NA 0.0100 1.0000 0.0100
0111 NA 0.5 0.5 0.0 0.0200 0.0200 0.0200
0110 NA 0.5 0.5 NA 0.0200 0.0200 0.0200
reject_intersection
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
TRUE
... (Use `print(x, rows = <nn>)` for more)
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= c_value * Weight * Alpha
1111 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1111 H3 parametric 0.01 <= 1.000 * 0.0 * 0.025
1111 H4 parametric 0.01 <= 1.000 * 0.0 * 0.025
1110 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1110 H3 parametric 0.01 <= 1.000 * 0.0 * 0.025
1101 H1 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H2 parametric 0.01 <= 1.006 * 0.5 * 0.025
1101 H4 parametric 0.01 <= 1.000 * 0.0 * 0.025
Inequality_holds
TRUE
TRUE
FALSE
FALSE
TRUE
TRUE
FALSE
TRUE
TRUE
FALSE
... (Use `print(x, rows = <nn>)` for more)
printing Bonferroni sequential results
Code
graph_test_shortcut(simple_successive_1(), rep(0.01, 4))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Code
graph_test_shortcut(simple_successive_1(), rep(0.01, 4), verbose = TRUE)
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Alpha = 0.025
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.5
H3: 0.0
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 0 0 1 0
H2 0 0 0 1
H3 0 1 0 0
H4 1 0 0 0
Step 1: Updated graph after removing hypothesis H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA 0 0 1
H3 NA 1 0 0
H4 NA 0 1 0
Step 2: Updated graph after removing hypotheses H1, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: 0.5
H4: 0.5
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA 0 1
H4 NA NA 1 0
Step 3: Updated graph after removing hypotheses H1, H2, H3
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: 1
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA 0
Step 4: Updated graph after removing hypotheses H1, H2, H3, H4
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
add alternate orderings
Code
test_res_alt
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
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
Step 1: Updated graph after removing hypothesis H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA 0 0 1
H3 NA 1 0 0
H4 NA 0 1 0
Step 2: Updated graph after removing hypotheses H1, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: 0.5
H4: 0.5
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA 0 1
H4 NA NA 1 0
Step 3: Updated graph after removing hypotheses H1, H2, H3
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: 1
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA 0
Step 4: Updated graph after removing hypotheses H1, H2, H3, H4
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Alternate rejection orderings ($valid_rejection_orderings) ---------------------
H1 H2 H3 H4
1 2 3 4
H1 H2 H4 H3
1 2 4 3
H1 H3 H2 H4
1 3 2 4
H2 H1 H3 H4
2 1 3 4
H2 H1 H4 H3
2 1 4 3
H2 H4 H1 H3
2 4 1 3
additional printing options for graph report
Code
print(graph_test_closure(par_gate, rep(0.01, 4), verbose = TRUE, test_values = TRUE),
precison = 4, indent = 4)
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Adjusted p details ($details) --------------------------------------------------
Intersection H1 H2 H3 H4 adj_p_grp1 adj_p_inter reject_intersection
1111 0.5 0.5 0.0 0.0 0.02 0.02 TRUE
1110 0.5 0.5 0.0 NA 0.02 0.02 TRUE
1101 0.5 0.5 NA 0.0 0.02 0.02 TRUE
1100 0.5 0.5 NA NA 0.02 0.02 TRUE
1011 0.5 NA 0.0 0.5 0.02 0.02 TRUE
1010 1.0 NA 0.0 NA 0.01 0.01 TRUE
1001 0.5 NA NA 0.5 0.02 0.02 TRUE
1000 1.0 NA NA NA 0.01 0.01 TRUE
0111 NA 0.5 0.5 0.0 0.02 0.02 TRUE
0110 NA 0.5 0.5 NA 0.02 0.02 TRUE
... (Use `print(x, rows = <nn>)` for more)
Detailed test values ($test_values) --------------------------------------------
Intersection Hypothesis Test p <= Weight * Alpha Inequality_holds
1111 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1111 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1111 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1110 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1110 H3 bonferroni 0.01 <= 0.0 * 0.025 FALSE
1101 H1 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H2 bonferroni 0.01 <= 0.5 * 0.025 TRUE
1101 H4 bonferroni 0.01 <= 0.0 * 0.025 FALSE
... (Use `print(x, rows = <nn>)` for more)
Code
print(graph_test_shortcut(simple_successive_1(), rep(0.01, 4), verbose = TRUE,
test_values = TRUE), precision = 7, indent = 9)
Output
Test parameters ($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
H1 H2 H3 H4
Unadjusted p-values: 0.01 0.01 0.01 0.01
Test types
bonferroni: (H1, H2, H3, H4)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.02 TRUE
H2 0.02 TRUE
H3 0.02 TRUE
H4 0.02 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
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
Step 1: Updated graph after removing hypothesis H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: 0.0
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA 0 0 1
H3 NA 1 0 0
H4 NA 0 1 0
Step 2: Updated graph after removing hypotheses H1, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: 0.5
H4: 0.5
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA 0 1
H4 NA NA 1 0
Step 3: Updated graph after removing hypotheses H1, H2, H3
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: 1
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA 0
Step 4: Updated graph after removing hypotheses H1, H2, H3, H4
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
--- Transition weights ---
H1 H2 H3 H4
H1 NA NA NA NA
H2 NA NA NA NA
H3 NA NA NA NA
H4 NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Step Hypothesis p <= Weight * Alpha Inequality_holds
1 H1 0.01 <= 0.5 * 0.025 TRUE
2 H2 0.01 <= 0.5 * 0.025 TRUE
3 H3 0.01 <= 0.5 * 0.025 TRUE
4 H4 0.01 <= 1.0 * 0.025 TRUE
Code
print(graph_test_shortcut(two_doses_two_primary_two_secondary(), 5:0 / 200,
verbose = TRUE, test_values = TRUE))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Alpha = 0.025
H1 H2 H3 H4 H5 H6
Unadjusted p-values: 0.025 0.020 0.015 0.010 0.005 0.000
Test types
bonferroni: (H1, H2, H3, H4, H5, H6)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.025 TRUE
H2 0.030 FALSE
H3 0.030 FALSE
H4 0.020 TRUE
H5 0.020 TRUE
H6 0.020 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Step 1: Updated graph after removing hypothesis H4
--- Hypothesis weights ---
H1: 0.50
H2: 0.00
H3: 0.00
H4: NA
H5: 0.25
H6: 0.25
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.00000 0.50000 0.50000 NA 0.00000 0.00000
H2 0.00000 0.00000 1.00000 NA 0.00000 0.00000
H3 0.00000 0.99990 0.00000 NA 0.00005 0.00005
H4 NA NA NA NA NA NA
H5 0.00000 0.00000 0.00000 NA 0.00000 1.00000
H6 0.00010 0.00000 0.00000 NA 0.99990 0.00000
Step 2: Updated graph after removing hypotheses H4, H6
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: NA
H5: 0.5
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5
H1 0.000000000 0.500000000 0.500000000 NA 0.000000000
H2 0.000000000 0.000000000 1.000000000 NA 0.000000000
H3 0.000000005 0.999900000 0.000000000 NA 0.000099995
H4 NA NA NA NA NA
H5 1.000000000 0.000000000 0.000000000 NA 0.000000000
H6 NA NA NA NA NA
H6
NA
NA
NA
NA
NA
NA
Step 3: Updated graph after removing hypotheses H4, H6, H5
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 NA NA NA
H2 0.0000 0.0000 1.0000 NA NA NA
H3 0.0001 0.9999 0.0000 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 4: Updated graph after removing hypotheses H4, H6, H5, H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Step Hypothesis p <= Weight * Alpha Inequality_holds
1 H4 0.010 <= 0.50 * 0.025 TRUE
2 H6 0.000 <= 0.25 * 0.025 TRUE
3 H5 0.005 <= 0.50 * 0.025 TRUE
4 H1 0.025 <= 1.00 * 0.025 TRUE
5 H3 0.015 <= 0.50 * 0.025 FALSE
5 H2 0.020 <= 0.50 * 0.025 FALSE
Code
print(graph_rejection_orderings(graph_test_shortcut(
two_doses_two_primary_two_secondary(), 6:1 / 400, verbose = TRUE,
test_values = TRUE)))
Output
Test parameters ($inputs) ------------------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Alpha = 0.025
H1 H2 H3 H4 H5 H6
Unadjusted p-values: 0.0150 0.0125 0.0100 0.0075 0.0050 0.0025
Test types
bonferroni: (H1, H2, H3, H4, H5, H6)
Test summary ($outputs) --------------------------------------------------------
Hypothesis Adj. P-value Reject
H1 0.015 TRUE
H2 0.020 TRUE
H3 0.020 TRUE
H4 0.015 TRUE
H5 0.015 TRUE
H6 0.015 TRUE
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA NA NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Rejection sequence details ($details) ------------------------------------------
Initial graph
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: 0.5
H5: 0.0
H6: 0.0
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 0.0000 0.0000 0.0000
H2 0.0000 0.0000 1.0000 0.0000 0.0000 0.0000
H3 0.0000 0.9999 0.0000 0.0001 0.0000 0.0000
H4 0.0000 0.0000 0.0000 0.0000 0.5000 0.5000
H5 0.0000 0.0000 0.0000 0.0000 0.0000 1.0000
H6 0.0001 0.0000 0.0000 0.0000 0.9999 0.0000
Step 1: Updated graph after removing hypothesis H4
--- Hypothesis weights ---
H1: 0.50
H2: 0.00
H3: 0.00
H4: NA
H5: 0.25
H6: 0.25
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.00000 0.50000 0.50000 NA 0.00000 0.00000
H2 0.00000 0.00000 1.00000 NA 0.00000 0.00000
H3 0.00000 0.99990 0.00000 NA 0.00005 0.00005
H4 NA NA NA NA NA NA
H5 0.00000 0.00000 0.00000 NA 0.00000 1.00000
H6 0.00010 0.00000 0.00000 NA 0.99990 0.00000
Step 2: Updated graph after removing hypotheses H4, H6
--- Hypothesis weights ---
H1: 0.5
H2: 0.0
H3: 0.0
H4: NA
H5: 0.5
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5
H1 0.000000000 0.500000000 0.500000000 NA 0.000000000
H2 0.000000000 0.000000000 1.000000000 NA 0.000000000
H3 0.000000005 0.999900000 0.000000000 NA 0.000099995
H4 NA NA NA NA NA
H5 1.000000000 0.000000000 0.000000000 NA 0.000000000
H6 NA NA NA NA NA
H6
NA
NA
NA
NA
NA
NA
Step 3: Updated graph after removing hypotheses H4, H6, H5
--- Hypothesis weights ---
H1: 1
H2: 0
H3: 0
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 0.0000 0.5000 0.5000 NA NA NA
H2 0.0000 0.0000 1.0000 NA NA NA
H3 0.0001 0.9999 0.0000 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 4: Updated graph after removing hypotheses H4, H6, H5, H1
--- Hypothesis weights ---
H1: NA
H2: 0.5
H3: 0.5
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 1 NA NA NA
H3 NA 1 0 NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 5: Updated graph after removing hypotheses H4, H6, H5, H1, H3
--- Hypothesis weights ---
H1: NA
H2: 1
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA 0 NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Step 6: Updated graph after removing hypotheses H4, H6, H5, H1, H3, H2
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA NA NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Final updated graph after removing rejected hypotheses
--- Hypothesis weights ---
H1: NA
H2: NA
H3: NA
H4: NA
H5: NA
H6: NA
--- Transition weights ---
H1 H2 H3 H4 H5 H6
H1 NA NA NA NA NA NA
H2 NA NA NA NA NA NA
H3 NA NA NA NA NA NA
H4 NA NA NA NA NA NA
H5 NA NA NA NA NA NA
H6 NA NA NA NA NA NA
Detailed test values ($test_values) --------------------------------------------
Step Hypothesis p <= Weight * Alpha Inequality_holds
1 H4 0.0075 <= 0.50 * 0.025 TRUE
2 H6 0.0025 <= 0.25 * 0.025 TRUE
3 H5 0.0050 <= 0.50 * 0.025 TRUE
4 H1 0.0150 <= 1.00 * 0.025 TRUE
5 H3 0.0100 <= 0.50 * 0.025 TRUE
6 H2 0.0125 <= 1.00 * 0.025 TRUE
Alternate rejection orderings ($valid_rejection_orderings) ---------------------
H4 H5 H6 H1 H2 H3
4 5 6 1 2 3
H4 H5 H6 H1 H3 H2
4 5 6 1 3 2
H4 H6 H5 H1 H2 H3
4 6 5 1 2 3
H4 H6 H5 H1 H3 H2
4 6 5 1 3 2
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