Last updated on 2026-06-07 20:51:41 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.7 | 23.64 | 280.00 | 303.64 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.8 | 17.71 | 246.19 | 263.90 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.8 | 43.00 | 584.79 | 627.79 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 1.0.8 | 43.00 | 616.21 | 659.21 | OK | |
| r-devel-windows-x86_64 | 1.0.7 | 26.00 | 340.00 | 366.00 | OK | |
| r-patched-linux-x86_64 | 1.0.7 | 22.77 | 265.15 | 287.92 | OK | |
| r-release-linux-x86_64 | 1.0.7 | 23.39 | 263.11 | 286.50 | OK | |
| r-release-macos-arm64 | 1.0.8 | 8.00 | 106.00 | 114.00 | ERROR | |
| r-release-macos-x86_64 | 1.0.8 | 20.00 | 414.00 | 434.00 | OK | |
| r-release-windows-x86_64 | 1.0.7 | 27.00 | 324.00 | 351.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.8 | 8.00 | 116.00 | 124.00 | ERROR | |
| r-oldrel-macos-x86_64 | 1.0.8 | 19.00 | 398.00 | 417.00 | OK | |
| r-oldrel-windows-x86_64 | 1.0.7 | 35.00 | 437.00 | 472.00 | OK |
Version: 1.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [24s/24s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(colocboost)
>
> test_check("colocboost")
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
No trait-specific (uncolocalized) effects in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Extracting colocalization results with cos_npc_cutoff = 0.2 and npc_outcome_cutoff = 0.1.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.1.
Validating input data.
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 59 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 2 iterations!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
No ambiguous colocalization events!
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
Extracting colocalization results with cos_npc_cutoff = 0.8 and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.8. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
Extracting colocalization results with pvalue_cutoff = 0.05, cos_npc_cutoff = 0.5, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 3 converged after 92 iterations!
Gradient boosting for outcome 1 converged after 95 iterations!
Gradient boosting for outcome 2 converged after 99 iterations!
Performing inference on colocalization events.
All possible colocalization events are reported regardless of their relative evidence compared to uncolocalized events (cos_npc_cutoff = 0 and npc_outcome_cutoff = 0).
All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 3 converged after 92 iterations!
Gradient boosting for outcome 1 converged after 95 iterations!
Gradient boosting for outcome 2 converged after 99 iterations!
Performing inference on colocalization events.
All possible colocalization events are reported regardless of their relative evidence compared to uncolocalized events (cos_npc_cutoff = 0 and npc_outcome_cutoff = 0).
All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
No ambiguous colocalization events!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 109 iterations!
Gradient boosting for outcome 3 converged after 111 iterations!
Gradient boosting for outcome 2 converged after 117 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 109 iterations!
Gradient boosting for outcome 3 converged after 111 iterations!
Gradient boosting for outcome 2 converged after 117 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
No ambiguous colocalization events!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.1.
Keep only uCoS with npc_outcome_cutoff >= 0.1.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.8.
Keep only uCoS with npc_outcome_cutoff >= 0.8.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 1.
Keep only uCoS with npc_outcome_cutoff >= 1.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 1.
Keep only uCoS with npc_outcome_cutoff >= 1.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Saving _problems/test_inference-1335.R
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 71 iterations!
Gradient boosting for outcome 3 converged after 98 iterations!
Gradient boosting for outcome 2 converged after 112 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.1.
Keep only uCoS with npc_outcome_cutoff >= 0.1.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.3.
Keep only uCoS with npc_outcome_cutoff >= 0.3.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.5.
Keep only uCoS with npc_outcome_cutoff >= 0.5.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.7.
Keep only uCoS with npc_outcome_cutoff >= 0.7.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.9.
Keep only uCoS with npc_outcome_cutoff >= 0.9.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001.
For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 152 iterations!
Gradient boosting for outcome 3 converged after 161 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 152 iterations!
Gradient boosting for outcome 3 converged after 161 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 68 iterations!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 152 iterations!
Gradient boosting for outcome 3 converged after 161 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Show all CoSs to uncolocalized outcomes.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 152 iterations!
Gradient boosting for outcome 3 converged after 161 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Show all CoSs to uncolocalized outcomes.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 152 iterations!
Gradient boosting for outcome 3 converged after 161 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 152 iterations!
Gradient boosting for outcome 3 converged after 161 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
Show all CoSs to uncolocalized outcomes.
Extracting colocalization results with cos_npc_cutoff = 0.7 and npc_outcome_cutoff = 0.3.
Keep only CoS with cos_npc >= 0.7. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.3.
Show all CoSs to uncolocalized outcomes.
Extracting colocalization results with cos_npc_cutoff = 0.9 and npc_outcome_cutoff = 0.5.
Keep only CoS with cos_npc >= 0.9. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.5.
Show all CoSs to uncolocalized outcomes.
Extracting colocalization results with cos_npc_cutoff = 1 and npc_outcome_cutoff = 0.5.
Keep only CoS with cos_npc >= 1. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.5.
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 1.
Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 1.
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Extracting colocalization results with cos_npc_cutoff = 1 and npc_outcome_cutoff = 1.
Keep only CoS with cos_npc >= 1. For each CoS, keep the outcomes configurations that the npc_outcome >= 1.
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 2 converged after 62 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001.
For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001.
For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Using multiple testing correction method: lfdr. Outcome 2 for all variants are greater than 1. Will not update it!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 3 converged after 9 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
[ FAIL 1 | WARN 2 | SKIP 2 | PASS 718 ]
══ Skipped tests (2) ═══════════════════════════════════════════════════════════
• No ucos detected (1): 'test_Xref.R:593:3'
• No ucos detected in test data (1): 'test_inference.R:1038:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test_inference.R:1335:7'): get_robust_ucos and get_ucos_evidence work together ──
Expected `all(evidence$npc_outcome >= 0.2 | evidence$npc_outcome == 0)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
[ FAIL 1 | WARN 2 | SKIP 2 | PASS 718 ]
Error:
! Test failures.
Execution halted
Flavor: r-release-macos-arm64
Version: 1.0.8
Check: tests
Result: ERROR
Running ‘testthat.R’ [25s/23s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> # This file is part of the standard setup for testthat.
> # It is recommended that you do not modify it.
> #
> # Where should you do additional test configuration?
> # Learn more about the roles of various files in:
> # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview
> # * https://testthat.r-lib.org/articles/special-files.html
>
> library(testthat)
> library(colocboost)
>
> test_check("colocboost")
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
There is no colocalization in this region!. Showing margianl for all outcomes!
No trait-specific (uncolocalized) effects in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Extracting colocalization results with cos_npc_cutoff = 0.2 and npc_outcome_cutoff = 0.1.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.1.
Validating input data.
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 2 converged after 50 iterations!
Gradient boosting for outcome 1 converged after 65 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 2 iterations!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
No ambiguous colocalization events!
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
No colocalization results in this region!
No colocalization results in this region!
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 3 converged after 94 iterations!
Gradient boosting for outcome 1 converged after 97 iterations!
Gradient boosting for outcome 2 converged after 101 iterations!
Performing inference on colocalization events.
All possible colocalization events are reported regardless of their relative evidence compared to uncolocalized events (cos_npc_cutoff = 0 and npc_outcome_cutoff = 0).
All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 3 converged after 94 iterations!
Gradient boosting for outcome 1 converged after 97 iterations!
Gradient boosting for outcome 2 converged after 101 iterations!
Performing inference on colocalization events.
All possible colocalization events are reported regardless of their relative evidence compared to uncolocalized events (cos_npc_cutoff = 0 and npc_outcome_cutoff = 0).
All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
No ambiguous colocalization events!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
There exists the ambiguous colocalization events from trait-specific effects. Extracting!
There are 1 ambiguous trait-specific effects.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 113 iterations!
Gradient boosting for outcome 3 converged after 116 iterations!
Gradient boosting for outcome 2 converged after 117 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 113 iterations!
Gradient boosting for outcome 3 converged after 116 iterations!
Gradient boosting for outcome 2 converged after 117 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
No ambiguous colocalization events!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.1.
Keep only uCoS with npc_outcome_cutoff >= 0.1.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.8.
Keep only uCoS with npc_outcome_cutoff >= 0.8.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 1.
Keep only uCoS with npc_outcome_cutoff >= 1.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
All possible uncolocalized events with positive relative evidence are reported (npc_outcome_cutoff = 0).
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 1.
Keep only uCoS with npc_outcome_cutoff >= 1.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.2.
Keep only uCoS with npc_outcome_cutoff >= 0.2.
Saving _problems/test_inference-1335.R
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 82 iterations!
Gradient boosting for outcome 3 converged after 89 iterations!
Gradient boosting for outcome 2 converged after 109 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.1.
Keep only uCoS with npc_outcome_cutoff >= 0.1.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.3.
Keep only uCoS with npc_outcome_cutoff >= 0.3.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.5.
Keep only uCoS with npc_outcome_cutoff >= 0.5.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.7.
Keep only uCoS with npc_outcome_cutoff >= 0.7.
Extracting outcome-specific (uncolocalized) results with npc_outcome_cutoff = 0.9.
Keep only uCoS with npc_outcome_cutoff >= 0.9.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001.
For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
There are 1 uCoS generated after filtering the robust colocalization.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 153 iterations!
Gradient boosting for outcome 3 converged after 160 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 153 iterations!
Gradient boosting for outcome 3 converged after 160 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 68 iterations!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 153 iterations!
Gradient boosting for outcome 3 converged after 160 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Show all CoSs to uncolocalized outcomes.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 153 iterations!
Gradient boosting for outcome 3 converged after 160 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Show all CoSs to uncolocalized outcomes.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 153 iterations!
Gradient boosting for outcome 3 converged after 160 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for focal outcome 4 converged after 68 iterations!
Gradient boosting for outcome 2 converged after 153 iterations!
Gradient boosting for outcome 3 converged after 160 iterations!
Gradient boosting for outcome 1 converged after 162 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05, and npc_outcome_cutoff = 0.2.
For each uCoS, keep the outcome-specific (uncolocalized) events that pvalue of variants for the outcome < 1e-05 and npc_outcome >0.2.
Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.2.
Show all CoSs to uncolocalized outcomes.
Extracting colocalization results with cos_npc_cutoff = 0.7 and npc_outcome_cutoff = 0.3.
Keep only CoS with cos_npc >= 0.7. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.3.
Show all CoSs to uncolocalized outcomes.
Extracting colocalization results with cos_npc_cutoff = 0.9 and npc_outcome_cutoff = 0.5.
Keep only CoS with cos_npc >= 0.9. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.5.
Show all CoSs to uncolocalized outcomes.
Extracting colocalization results with cos_npc_cutoff = 1 and npc_outcome_cutoff = 0.5.
Keep only CoS with cos_npc >= 1. For each CoS, keep the outcomes configurations that the npc_outcome >= 0.5.
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Extracting colocalization results with cos_npc_cutoff = 0.5 and npc_outcome_cutoff = 1.
Keep only CoS with cos_npc >= 0.5. For each CoS, keep the outcomes configurations that the npc_outcome >= 1.
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Extracting colocalization results with cos_npc_cutoff = 1 and npc_outcome_cutoff = 1.
Keep only CoS with cos_npc >= 1. For each CoS, keep the outcomes configurations that the npc_outcome >= 1.
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
No colocalization results in this region!
Extracting outcome-specific (uncolocalized) results with pvalue_cutoff = 1e-05.
Keep only uCoS with pvalue of variants for the outcome < 1e-05.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
No uncolocalized results in this region!
Validating input data.
Starting gradient boosting algorithm.
Gradient boosting for outcome 1 converged after 83 iterations!
Gradient boosting for outcome 2 converged after 385 iterations!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
Validating input data.
Validating input data.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001.
For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
Validating input data.
Starting gradient boosting algorithm.
Running ColocBoost with assumption of one causal per outcome per region!
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001.
For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001.
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Using multiple testing correction method: lfdr. Outcome 2 for all variants are greater than 1. Will not update it!
Performing inference on colocalization events.
No colocalization results in this region!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
No colocalization results in this region!
There is no colocalization in this region!. Showing margianl for all outcomes!
There is no colocalization in this region!. Showing margianl for all outcomes!
Validating input data.
Starting gradient boosting algorithm.
Performing inference on colocalization events.
Extracting colocalization results with pvalue_cutoff = 0.001, cos_npc_cutoff = 0.2, and npc_outcome_cutoff = 0.2.
Keep only CoS with cos_npc >= 0.2. For each CoS, keep the outcomes configurations that pvalue of variants for the outcome < 0.001 and npc_outcome >0.2.
No uncolocalized results in this region!
[ FAIL 1 | WARN 0 | SKIP 1 | PASS 720 ]
══ Skipped tests (1) ═══════════════════════════════════════════════════════════
• No ucos detected in test data (1): 'test_inference.R:1038:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure ('test_inference.R:1335:7'): get_robust_ucos and get_ucos_evidence work together ──
Expected `all(evidence$npc_outcome >= 0.2 | evidence$npc_outcome == 0)` to be TRUE.
Differences:
`actual`: FALSE
`expected`: TRUE
[ FAIL 1 | WARN 0 | SKIP 1 | PASS 720 ]
Error:
! Test failures.
Execution halted
Flavor: r-oldrel-macos-arm64