Last updated on 2026-10-24 14:49:47 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 6.7.0 | 18.52 | 470.83 | 489.35 | OK | |
| r-devel-linux-x86_64-debian-gcc | 6.7.0 | 13.54 | 332.41 | 345.95 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 6.7.0 | 15.00 | 366.80 | 381.80 | OK | |
| r-devel-linux-x86_64-fedora-gcc | 6.7.0 | 15.00 | 390.85 | 405.85 | OK | |
| r-devel-windows-x86_64 | 6.7.0 | 24.00 | 453.00 | 477.00 | OK | |
| r-patched-linux-x86_64 | 6.7.0 | 20.04 | 459.23 | 479.27 | OK | |
| r-release-linux-x86_64 | 6.7.0 | 17.10 | 459.01 | 476.11 | OK | |
| r-release-macos-arm64 | 6.7.0 | 5.00 | 125.00 | 130.00 | OK | |
| r-release-macos-x86_64 | 6.7.0 | 14.00 | 488.00 | 502.00 | OK | |
| r-release-windows-x86_64 | 6.7.0 | 22.00 | 449.00 | 471.00 | OK | |
| r-oldrel-macos-arm64 | 6.7.0 | 4.00 | 124.00 | 128.00 | OK | |
| r-oldrel-macos-x86_64 | 6.7.0 | 14.00 | 444.00 | 458.00 | OK | |
| r-oldrel-windows-x86_64 | 6.7.0 | 31.00 | 630.00 | 661.00 | OK |
Version: 6.7.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [184s/224s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(PatientLevelPrediction)
> test_check("PatientLevelPrediction")
Internet: TRUE
attempting to download GiBleed
trying URL 'https://raw.githubusercontent.com/OHDSI/EunomiaDatasets/main/datasets/GiBleed/GiBleed_5.5.zip'
Content type 'application/zip' length 7414767 bytes (7.1 MB)
==================================================
downloaded 7.1 MB
attempting to extract and load: /home/hornik/tmp/scratch/Rtmp2T7cT4/GiBleed_5.5.zip to: /home/hornik/tmp/scratch/Rtmp2T7cT4/GiBleed_5.5.sqlite
Cohorts created in table main.cohort
No cdm database id entered so using cdmDatabaseSchema - if cdmDatabaseSchema is the same for multiple different databases, please use cdmDatabaseId to specify a unique identifier for the database and version
Connecting using SQLite driver
Constructing the at risk cohort
Executing SQL took 0.0372 secs
Fetching cohorts from server
Loading cohorts took 0.056 secs
Constructing features on server
Executing SQL took 0.12 secs
Fetching data from server
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Fetching data took 0.966 secs
Fetching outcomes from server
Loading outcomes took 0.0659 secs
Use timeStamp: TRUE
Creating save directory at: /home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/Test
Currently in a tryCatch or withCallingHandlers block, so unable to add global calling handlers. ParallelLogger will not capture R messages, errors, and warnings, only explicit calls to ParallelLogger. (This message will not be shown again this R session)
Patient-Level Prediction Package version 6.7.0
Study started at: 2026-10-23 14:49:49.056885
AnalysisID: Test
AnalysisName: Testing analysis
TargetID: 1
OutcomeID: 3
Cohort size: 1800
Covariates: 75
Creating population
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.128 secs
seed: 12
Creating a 25% test and 75% train (into 3 folds) random stratified split by class
Data split into 449 test cases and 1351 train cases (451, 450, 450)
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Data split in 3.24 secs
Train Set:
Fold 1 451 patients with 89 outcomes - Fold 2 450 patients with 89 outcomes - Fold 3 450 patients with 89 outcomes
75 covariates in train data
Test Set:
449 patients with 88 outcomes
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Removing 2 redundant covariates
Removing 0 infrequent covariates
Normalizing covariates
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Tidying covariates took 3.2 secs
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Train Set:
Fold 1 451 patients with 89 outcomes - Fold 2 450 patients with 89 outcomes - Fold 3 450 patients with 89 outcomes
73 covariates in train data
Test Set:
449 patients with 88 outcomes
Running Cyclops
Done.
GLM fit status: OK
Creating variable importance data frame
Prediction took 0.343 secs
Time to fit model: 2.22 secs
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Removing infrequent and redundant covariates and normalizing
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Removing infrequent and redundant covariates covariates and normalizing took 0.819 secs
Prediction took 0.29 secs
Prediction done in: 2.21 secs
Calculating Performance for Test
=============
AUC 71.48
95% lower AUC: 65.41
95% upper AUC: 77.54
AUPRC: 35.74
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.1897 : observed risk 0.196
Calibration in large- Intercept 0.3216
Weak calibration intercept: 0.3216 - gradient:1.2182
Hosmer-Lemeshow calibration gradient: 1.23 intercept: -0.06
Average Precision: 0.37
Calculating Performance for Train
=============
AUC 72.43
95% lower AUC: 68.99
95% upper AUC: 75.86
AUPRC: 39.39
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.1976 : observed risk 0.1976
Calibration in large- Intercept 0.174
Weak calibration intercept: 0.174 - gradient:1.1421
Hosmer-Lemeshow calibration gradient: 1.21 intercept: -0.04
Average Precision: 0.39
Calculating Performance for CV
=============
AUC 67.83
95% lower AUC: 64.15
95% upper AUC: 71.50
AUPRC: 31.83
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.1979 : observed risk 0.1976
Calibration in large- Intercept 0.1826
Weak calibration intercept: 0.1826 - gradient:1.149
Hosmer-Lemeshow calibration gradient: 1.17 intercept: -0.03
Average Precision: 0.32
Time to calculate evaluation metrics: 0.499 secs
Calculating covariate summary @ 2026-10-23 14:50:01.288755
This can take a while...
Creating binary labels
Joining with strata
calculating subset of strata 1
calculating subset of strata 2
calculating subset of strata 3
calculating subset of strata 4
Restricting to subgroup
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
Calculating summary for subgroup TestWithNoOutcome
Restricting to subgroup
duckdb keeps downloaded extensions and secrets in a temporary directory:
ℹ /home/hornik/tmp/scratch/Rtmp2T7cT4/duckdb
This is removed when the R session ends.
• Extensions are re-downloaded each session.
• Secrets are lost.
ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
ℹ See ?duckdb_storage for details and alternatives.
ℹ This message has been shown 60 times and will not be shown again this session.
Calculating summary for subgroup TrainWithNoOutcome
Restricting to subgroup
Calculating summary for subgroup TrainWithOutcome
Restricting to subgroup
Calculating summary for subgroup TestWithOutcome
Aggregating with labels and strata
Finished covariate summary @ 2026-10-23 14:50:05.327849
Time to calculate covariate summary: 4.04 secs
Run finished successfully.
Saving PlpResult
Creating directory to save model
plpResult saved to ..\/home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/Test\plpResult
runPlp time taken: 16.3 secs
Use timeStamp: TRUE
Diagnosing impact of minTimeAtRisk in populationSettings
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0615 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0737 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0627 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.069 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0519 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0571 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0739 secs
Saving diagnosePlp to /home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/Test/diagnosePlp.rds
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.0568 secs
seed: 12
Creating a 25% test and 75% train (into 3 folds) random stratified split by class
Data split into 449 test cases and 1351 train cases (451, 450, 450)
Data split in 3.39 secs
seed: 12
Creating a 25% test and 75% train (into 3 folds) random stratified split by class
Data split into 449 test cases and 1351 train cases (451, 450, 450)
Data split in 2.35 secs
Use timeStamp: TRUE
Creating save directory at: /home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/tinyResults/tinyFit
Patient-Level Prediction Package version 6.7.0
Study started at: 2026-10-23 14:50:16.517989
AnalysisID: tinyFit
AnalysisName: Study details
TargetID: 1
OutcomeID: 3
Cohort size: 865
Covariates: 2
Creating population
Outcome is 0 or 1
Population created with: 865 observations, 865 unique subjects and 262 outcomes
Population created in 0.219 secs
seed: 123
Creating a 25% test and 75% train (into 3 folds) random stratified split by class
Data split into 215 test cases and 650 train cases (217, 217, 216)
Data split in 2.6 secs
Train Set:
Fold 1 217 patients with 66 outcomes - Fold 2 217 patients with 66 outcomes - Fold 3 216 patients with 65 outcomes
2 covariates in train data
Test Set:
215 patients with 65 outcomes
Removing 0 redundant covariates
Removing 0 infrequent covariates
Normalizing covariates
Tidying covariates took 2.33 secs
Train Set:
Fold 1 217 patients with 66 outcomes - Fold 2 217 patients with 66 outcomes - Fold 3 216 patients with 65 outcomes
2 covariates in train data
Test Set:
215 patients with 65 outcomes
Running Cyclops
Done.
GLM fit status: OK
Creating variable importance data frame
Prediction took 0.284 secs
Time to fit model: 1.74 secs
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 0.885 secs
Prediction took 0.337 secs
Prediction done in: 2.04 secs
Calculating Performance for Test
=============
AUC 63.68
95% lower AUC: 57.26
95% upper AUC: 70.10
AUPRC: 40.36
Brier: 0.20
Eavg: 0.01
Calibration in large- Mean predicted risk 0.3018 : observed risk 0.3023
Calibration in large- Intercept -0.118
Weak calibration intercept: -0.118 - gradient:0.8335
Hosmer-Lemeshow calibration gradient: 0.90 intercept: 0.03
Average Precision: 0.41
Calculating Performance for Train
=============
AUC 64.99
95% lower AUC: 61.50
95% upper AUC: 68.48
AUPRC: 41.44
Brier: 0.19
Eavg: 0.00
Calibration in large- Mean predicted risk 0.3031 : observed risk 0.3031
Calibration in large- Intercept 0.0121
Weak calibration intercept: 0.0121 - gradient:1.0174
Hosmer-Lemeshow calibration gradient: 1.02 intercept: -0.00
Average Precision: 0.41
Calculating Performance for CV
=============
AUC 63.91
95% lower AUC: 59.57
95% upper AUC: 68.24
AUPRC: 39.42
Brier: 0.20
Eavg: 0.04
Calibration in large- Mean predicted risk 0.3031 : observed risk 0.3031
Calibration in large- Intercept -0.0778
Weak calibration intercept: -0.0778 - gradient:0.8913
Hosmer-Lemeshow calibration gradient: 1.02 intercept: 0.01
Average Precision: 0.39
Time to calculate evaluation metrics: 0.394 secs
Run finished successfully.
Saving PlpResult
Creating directory to save model
plpResult saved to ..\/home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/tinyResults/tinyFit\plpResult
runPlp time taken: 9.93 secs
Warning: PredictionDistribution not available for survival models
No databaseRefId specified so using schema as unique database identifier
Warning: Unable to coerce hyperParamSearch to data.frame; storing empty table for compatibility.
Connecting using SQLite driver
All or some PLP result tables do not exist, tables being recreated
Deleting existing tables
Creating PLP results tables
Executing SQL took 0.0255 secs
PLP result migration being applied
Migrating data set
Migrator using SQL files in PatientLevelPrediction
Connecting using SQLite driver
Creating migrations table
Executing SQL took 0.00372 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.00433 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.00429 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00558 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00339 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Executing migration: Migration_3-add_model_name.sql
Executing SQL took 0.00441 secs
Saving migration: Migration_3-add_model_name.sql
Executing SQL took 0.00357 secs
Migration complete Migration_3-add_model_name.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.00421 secs
Connecting using SQLite driver
Adding new model settings
Inserting data took 0.0147 secs
Connecting using SQLite driver
All or some PLP result tables do not exist, tables being recreated
Deleting existing tables
Creating PLP results tables
Executing SQL took 0.025 secs
PLP result migration being applied
Migrating data set
Migrator using SQL files in PatientLevelPrediction
Connecting using SQLite driver
Creating migrations table
Executing SQL took 0.00388 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.00478 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.00432 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00882 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00375 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Executing migration: Migration_3-add_model_name.sql
Executing SQL took 0.00557 secs
Saving migration: Migration_3-add_model_name.sql
Executing SQL took 0.00421 secs
Migration complete Migration_3-add_model_name.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.00512 secs
Connecting using SQLite driver
Adding new model settings
Inserting data took 0.0107 secs
Adding new model settings
Inserting data took 0.00997 secs
Adding new model settings
Inserting data took 0.0104 secs
Adding new model settings
Inserting data took 0.0151 secs
Adding new model settings
Inserting data took 0.0108 secs
Adding new model settings
Inserting data took 0.0105 secs
Adding new model settings
Inserting data took 0.00873 secs
Connecting using SQLite driver
Executing SQL took 0.00551 secs
Executing SQL took 0.00355 secs
Executing SQL took 0.00362 secs
Executing SQL took 0.00347 secs
Executing SQL took 0.00495 secs
Connecting using SQLite driver
All or some PLP result tables do not exist, tables being recreated
Deleting existing tables
Creating PLP results tables
Executing SQL took 0.0334 secs
PLP result migration being applied
Migrating data set
Migrator using SQL files in PatientLevelPrediction
Connecting using SQLite driver
Creating migrations table
Executing SQL took 0.00385 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.00419 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.00398 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00625 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.0034 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Executing migration: Migration_3-add_model_name.sql
Executing SQL took 0.00461 secs
Saving migration: Migration_3-add_model_name.sql
Executing SQL took 0.00388 secs
Migration complete Migration_3-add_model_name.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.00463 secs
Connecting using SQLite driver
Adding new hyperparameter settings
Inserting data took 0.00863 secs
Hyperparameter setting exists
Connecting using SQLite driver
All or some PLP result tables do not exist, tables being recreated
Deleting existing tables
Creating PLP results tables
Executing SQL took 0.0426 secs
PLP result migration being applied
Migrating data set
Migrator using SQL files in PatientLevelPrediction
Connecting using SQLite driver
Creating migrations table
Executing SQL took 0.00414 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.00477 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.00381 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00679 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.0042 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Executing migration: Migration_3-add_model_name.sql
Executing SQL took 0.00515 secs
Saving migration: Migration_3-add_model_name.sql
Executing SQL took 0.00366 secs
Migration complete Migration_3-add_model_name.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.00473 secs
Connecting using SQLite driver
Adding TAR
Executing SQL took 0.00399 secs
tarId: 1
Adding cohort target
Inserting data took 0.0108 secs
Adding cohort target
Inserting data took 0.0102 secs
tId: 1
Adding cohort outcome
Inserting data took 0.0104 secs
Adding cohort outcome
Inserting data took 0.00991 secs
oId: 2
Adding new population settings
Inserting data took 0.00845 secs
popSetId: 1
Adding new covariate settings
Inserting data took 0.0102 secs
covSetId: 1
Adding new model settings
Inserting data took 0.0235 secs
modSetId: 1
Adding new plp data settings
Inserting data took 0.0133 secs
plpDataSetId: 1
Adding new feature_engineering settings
Inserting data took 0.00963 secs
FESetId: 1
Adding new sample settings
Inserting data took 0.0113 secs
sampleSetId: 1
Adding new tidy covariates settings
Inserting data took 0.0102 secs
tidySetId: 1
Adding new split settings
Inserting data took 0.0101 secs
splitId: 1
Adding new hyperparameter settings
Inserting data took 0.013 secs
hyperparameterSetId: 2
Executing SQL took 0.00404 secs
modelDesignId: 1
TAR exists
tarId: 1
json in jsons:TRUE
Cohort target exists in cohort_definition with cohort id1
Cohort target exists in cohorts with cohort id1
tId: 1
json in jsons:TRUE
Cohort outcome exists in cohort_definition with cohort id2
Cohort outcome exists in cohorts with cohort id2
oId: 2
Population settings exists
popSetId: 1
Covariate setting exists
covSetId: 1
Model setting exists
modSetId: 1
Split setting exists
plpDataSetId: 1
feature engineering setting exists
FESetId: 1
sample setting exists
sampleSetId: 1
tidy covariates setting exists
tidySetId: 1
Adding new split settings
Inserting data took 0.00983 secs
splitId: 2
Adding new hyperparameter settings
Inserting data took 0.00943 secs
hyperparameterSetId: 3
Executing SQL took 0.00768 secs
modelDesignId: 2
TAR exists
tarId: 1
json in jsons:TRUE
Cohort target exists in cohort_definition with cohort id1
Cohort target exists in cohorts with cohort id1
tId: 1
json in jsons:TRUE
Cohort outcome exists in cohort_definition with cohort id2
Cohort outcome exists in cohorts with cohort id2
oId: 2
Population settings exists
popSetId: 1
Covariate setting exists
covSetId: 1
Model setting exists
modSetId: 1
Split setting exists
plpDataSetId: 1
feature engineering setting exists
FESetId: 1
sample setting exists
sampleSetId: 1
tidy covariates setting exists
tidySetId: 1
Split setting exists
splitId: 1
Hyperparameter setting exists
hyperparameterSetId: 2
modelDesignId: 1
Prediction done in: 0.000498 secs
Creating directory to save model
New best performance 0.5 with param: lambda=1
Fit best model on whole training set
Calculating covariate summary @ 2026-10-23 14:50:35.690097
This can take a while...
calculating subset of strata 1
Restricting to subgroup
Calculating summary for subgroup
Aggregating with no labels or strata
Finished covariate summary @ 2026-10-23 14:50:36.696324
Time to calculate covariate summary: 1.01 secs
Calculating covariate summary @ 2026-10-23 14:50:36.709664
This can take a while...
calculating subset of strata 1
Restricting to subgroup
Calculating summary for subgroup
Aggregating with no labels or strata
Finished covariate summary @ 2026-10-23 14:50:37.851704
Time to calculate covariate summary: 1.14 secs
Calculating covariate summary @ 2026-10-23 14:50:37.868597
This can take a while...
Creating binary labels
calculating subset of strata 1
calculating subset of strata 2
Restricting to subgroup
Calculating summary for subgroup WithOutcome
Restricting to subgroup
Calculating summary for subgroup WithNoOutcome
Aggregating with only labels or strata
Finished covariate summary @ 2026-10-23 14:50:39.622633
Time to calculate covariate summary: 1.75 secs
Restricting to subgroup
Calculating summary for subgroup
variance needs to be >= 0
variance should be of class:numericvariance should be of class:integer
seed should be of class:numericseed should be of class:NULLseed should be of class:integer
threads should be of class:numericthreads should be of class:integer
lowerLimit should be of class:numericlowerLimit should be of class:integer
upperLimit should be of class:numericupperLimit should be of class:integer
upperLimit needs to be >= 3
variance needs to be >= 0
variance should be of class:numericvariance should be of class:integer
seed should be of class:numericseed should be of class:NULLseed should be of class:integer
threads should be of class:numericthreads should be of class:integer
lowerLimit should be of class:numericlowerLimit should be of class:integer
upperLimit should be of class:numericupperLimit should be of class:integer
upperLimit needs to be >= 3
testFraction should be of class:numerictestFraction should be of class:integer
testFraction needs to be >= 0
-1 * testFraction needs to be > -1
trainFraction should be of class:numerictrainFraction should be of class:integer
-1 * trainFraction needs to be >= -1
trainFraction needs to be > 0
splitSeed should be of class:numericsplitSeed should be of class:integer
splitSeed should be of class:numericsplitSeed should be of class:integer
nfold should be of class:numericnfold should be of class:integer
nfold should be of class:numericnfold should be of class:integer
Invalid type setting.
Pick from: 'stratified','time','subject'
type should be of class:character
type should be of class:character
maxTrainingOutcomes needs to be > 0
maxTrainingOutcomes should be of class:numericmaxTrainingOutcomes should be of class:integer
maxTrainingOutcomes must be a single whole number
splitSeed should be of class:numericsplitSeed should be of class:integer
nfold needs to be > 1
nfold must be a single whole number
type should be stratified or subject
Data split into 240 test cases and 160 train cases with 40 training outcome rows (maxTrainingOutcomes = 40; folds: 40, 40, 40, 40)
Data split into 240 test cases and 160 train cases with 40 training outcome rows (maxTrainingOutcomes = 40; folds: 40, 40, 40, 40)
Data split into 180 test cases and 120 train cases with 20 training outcome rows (maxTrainingOutcomes = 20; folds: 30, 30, 30, 30)
Data split into 110 test cases and 220 train cases with 20 training outcome rows (maxTrainingOutcomes = 20; folds: 60, 60, 50, 50)
Warning: Outcome-limited split did not trigger because the population has 40 outcome rows, which is not greater than maxTrainingOutcomes = 100. Using the default 25% test and 75% train split.
seed: 1
Creating a 25% test and 75% train (into 4 folds) random stratified split by class
Data split into 50 test cases and 150 train cases (38, 38, 37, 37)
seed: 1
Creating a 25% test and 75% train (into 4 folds) random stratified split by class
Data split into 50 test cases and 150 train cases (38, 38, 37, 37)
seed: 40633
seed: 40633
Creating a 30% test and 70% train (into 4 folds) random stratified split by class
Data split into 59 test cases and 141 train cases (36, 36, 35, 34)
seed: 40633
Creating a 20% test and 80% train (into 4 folds) random stratified split by class
Data split into 99 test cases and 401 train cases (101, 100, 100, 100)
seed: 40633
Creating a 20% test and 40% train (into 4 folds) random stratified split by class
Data split into 99 test cases and 201 train cases (51, 51, 50, 49)
200 were not used for training or testing
seed: 40633
Creating 20% test and 80% train (into 4 folds) stratified split at 2011-02-05
Data split into 100 test cases and 400 train samples (101, 101, 101, 97)
seed: 40633
Creating 20% test and 40% train (into 4 folds) stratified split at 2011-02-05
Data split into 100 test cases and 204 train samples (51, 51, 51, 51)
196 were not used for training or testing
seed: 40633
seed: 40633
Creating a 20% test and 80% train (into 4 folds) stratified split by subject
Data split into 40 test cases and 160 train cases (41, 41, 39, 39)
seed: 40633
Creating a 25% test and 75% train (into 3 folds) stratified split by subject
Data split into 52 test cases and 148 train cases (52, 48, 48)
Calculating Performance for All
=============
AUC 51.00
95% lower AUC: 39.57
95% upper AUC: 62.43
AUPRC: 52.01
Brier: 0.33
Eavg: 0.24
Calibration in large- Mean predicted risk 0.5 : observed risk 0.5
Calibration in large- Intercept 0
Weak calibration intercept: 0 - gradient:0.0349
Hosmer-Lemeshow calibration gradient: 0.00 intercept: 0.50
Average Precision: 0.53
Time to calculate evaluation metrics: 0.167 secs
Evaluating survival model at time: 365 days
C-statistic: 0.38931 (0.269-0.51)
E-statistic: 0.0319220138645564
E-statistic 90%: 0.0649360660190881
Warning: PredictionDistribution not available for survival models
Time to calculate evaluation metrics: 0.462 secs
Setting levels: control = 0, case = 1
Warning: Cannot compute AUC: outcomeCount has only one class
Warning: Cannot compute AUC: outcomeCount has only one class
Warning: Cannot compute AUPRC: outcomeCount has only one class
Warning: Cannot compute Average Precision: outcomeCount has no positive class
Calculating Performance for Validation
=============
Warning: Cannot compute AUC: outcomeCount has only one class
AUC NA
95% lower AUC: NA
95% upper AUC: NA
Warning: Cannot compute AUPRC: outcomeCount has only one class
AUPRC: NA
Brier: 0.35
Eavg: 0.53
Calibration in large- Mean predicted risk 0.527 : observed risk 0
Calibration in large- Intercept -26.5661
Weak calibration intercept: -26.5661 - gradient:0
Hosmer-Lemeshow calibration gradient: 0.00 intercept: -0.00
Warning: Cannot compute Average Precision: outcomeCount has no positive class
Average Precision: NA
Warning: Number of positives is zero
Time to calculate evaluation metrics: 0.145 secs
Column names of coefficients are not correct
Column types of coefficients are not correct
Column types of coefficients are not correct
intercept should be of class:numeric
mapping should be of class:charactermapping should be of class:function
targetId should be of class:numerictargetId should be of class:NULL
outcomeId should be of class:numericoutcomeId should be of class:NULL
populationSettings should be of class:NULLpopulationSettings should be of class:populationSettings
restrictPlpDataSettings should be of class:NULLrestrictPlpDataSettings should be of class:restrictPlpDataSettings
covariateSettings should be of class:listcovariateSettings should be of class:NULLcovariateSettings should be of class:covariateSettings
predict risk probabilities using predictGlm
Prediction took 0.296 secs
Prediction done in: 0.315 secs
databaseDetails should be of class:databaseDetails
databaseDetails should be of class:databaseDetails
databaseDetails should be of class:databaseDetails
No cdm database name entered so using cdmDatabaseSchema
threshold needs to be >= 0
threshold should be of class:numeric
threshold needs to be < 1
starting to map the columns and rows
finished MapCovariates
starting to map the columns and rows
finished MapCovariates
plpData size estimated to use 0GBs of RAM
starting toSparseM
starting to map the columns and rows
finished MapCovariates
toSparseM non temporal used
plpData size estimated to use 0GBs of RAM
finishing toSparseM
toSparseM took 2.22397041320801 secs
starting to map the columns and rows
finished MapCovariates
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
missingThreshold needs to be > 0
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
missingThreshold should be of class:numeric
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
missingThreshold needs to be < 1
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
method should be mean or median
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
addMissingIndicator should be of class:logical
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
method should be pmm
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
missingThreshold needs to be > 0
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
missingThreshold should be of class:numeric
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
missingThreshold needs to be < 1
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
addMissingIndicator should be of class:logical
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
Imputing missing values with simpleImputer using: mean and missing threshold: 0.3
Calculating missingness in data
Found features with missing values
Imputation done in time: 5.39 secs
Applying imputation to test data with simpleImputer
using method: mean and missing threshold: 0.3
Imputation done in time: 4.32 secs
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
Imputing missing values with simpleImputer using: mean and missing threshold: 0.95
Calculating missingness in data
Found features with missing values
Imputation done in time: 3.98 secs
Applying imputation to test data with simpleImputer
using method: mean and missing threshold: 0.95
Imputation done in time: 3.17 secs
Warning: Imputation is experimental and may have bugs,
please report any issues on the GitHub repository.
Imputing missing values with simpleImputer using: mean and missing threshold: 0.3
Calculating missingness in data
Found features with missing values
Imputation done in time: 3.97 secs
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
Imputing missing values with iterativeImputer using: pmm, missing threshold: 0.3 and method settings: 111
Calculating missingness in data
Found features with missing values
Imputation iteration: 1
Imputing variable: fakeMissingVariable
Imputation done in time: 7.45 secs
Applying imputation to test data with iterativeImputer
using method: pmm and missing threshold: 0.3
Imputing variable: fakeMissingVariable
PMM predict for variable fakeMissingVariable (covariateId=666): missing=32, donors=64, k=1, time=0.361 secs
Imputation done in time: 6.96 secs
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
Applying imputation to test data with iterativeImputer
using method: pmm and missing threshold: 0.3
Imputation done in time: 3.32 secs
Warning: Imputation is experimental and may have bugs.
Please report any issues on the GitHub repository.
Applying imputation to test data with iterativeImputer
using method: pmm and missing threshold: 0.8
Imputing variable: bmi
PMM predict for variable bmi (covariateId=100): missing=2, donors=2, k=3, time=0.266 secs
Imputation done in time: 5.88 secs
Warning: Imputation model only has intercept. It does not fit the data well
Use timeStamp: TRUE
Creating save directory at: /home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/lcc/learningCurve_1
Patient-Level Prediction Package version 6.7.0
Study started at: 2026-10-23 14:52:00.661508
AnalysisID: learningCurve_1
AnalysisName: Study details
TargetID: 1
OutcomeID: 3
Cohort size: 1800
Covariates: 75
Creating population
Outcome is 0 or 1
Population created with: 1767 observations, 1767 unique subjects and 355 outcomes
Population created in 0.166 secs
seed: 70165
Creating a 20% test and 60% train (into 2 folds) random stratified split by class
Data split into 353 test cases and 1061 train cases (531, 530)
353 were not used for training or testing
Data split in 2.86 secs
Train Set:
Fold 1 531 patients with 107 outcomes - Fold 2 530 patients with 106 outcomes
75 covariates in train data
Test Set:
353 patients with 71 outcomes
Removing 2 redundant covariates
Removing 0 infrequent covariates
Normalizing covariates
Tidying covariates took 3.36 secs
Train Set:
Fold 1 531 patients with 107 outcomes - Fold 2 530 patients with 106 outcomes
73 covariates in train data
Test Set:
353 patients with 71 outcomes
Running Cyclops
Done.
GLM fit status: OK
Creating variable importance data frame
Prediction took 0.344 secs
Time to fit model: 1.63 secs
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 0.976 secs
Prediction took 0.361 secs
Prediction done in: 2.28 secs
Calculating Performance for Test
=============
AUC 70.65
95% lower AUC: 63.83
95% upper AUC: 77.47
AUPRC: 32.86
Brier: 0.14
Eavg: 0.01
Calibration in large- Mean predicted risk 0.1994 : observed risk 0.2011
Calibration in large- Intercept 0.1296
Weak calibration intercept: 0.1296 - gradient:1.0968
Hosmer-Lemeshow calibration gradient: 1.09 intercept: -0.02
Average Precision: 0.35
Calculating Performance for Train
=============
AUC 74.41
95% lower AUC: 70.82
95% upper AUC: 77.99
AUPRC: 40.35
Brier: 0.14
Eavg: 0.03
Calibration in large- Mean predicted risk 0.2008 : observed risk 0.2008
Calibration in large- Intercept 0.1786
Weak calibration intercept: 0.1786 - gradient:1.1495
Hosmer-Lemeshow calibration gradient: 1.25 intercept: -0.04
Average Precision: 0.41
Calculating Performance for CV
=============
AUC 69.66
95% lower AUC: 65.74
95% upper AUC: 73.58
AUPRC: 32.83
Brier: 0.14
Eavg: 0.03
Calibration in large- Mean predicted risk 0.2012 : observed risk 0.2008
Calibration in large- Intercept 0.2838
Weak calibration intercept: 0.2838 - gradient:1.2355
Hosmer-Lemeshow calibration gradient: 1.24 intercept: -0.05
Average Precision: 0.31
Time to calculate evaluation metrics: 0.481 secs
Run finished successfully.
Saving PlpResult
Creating directory to save model
plpResult saved to ..\/home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/lcc/learningCurve_1\plpResult
runPlp time taken: 11.5 secs
Creating save directory at: /home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/lcc/learningCurve_2
Patient-Level Prediction Package version 6.7.0
Study started at: 2026-10-23 14:52:12.204652
AnalysisID: learningCurve_2
AnalysisName: Study details
TargetID: 1
OutcomeID: 3
Cohort size: 1800
Covariates: 75
Creating population
Outcome is 0 or 1
Population created with: 1767 observations, 1767 unique subjects and 355 outcomes
Population created in 0.0947 secs
seed: 70165
Creating a 20% test and 70% train (into 2 folds) random stratified split by class
Data split into 353 test cases and 1237 train cases (619, 618)
177 were not used for training or testing
Data split in 3.15 secs
Train Set:
Fold 1 619 patients with 124 outcomes - Fold 2 618 patients with 124 outcomes
75 covariates in train data
Test Set:
353 patients with 71 outcomes
Removing 2 redundant covariates
Removing 0 infrequent covariates
Normalizing covariates
Tidying covariates took 2.75 secs
Train Set:
Fold 1 619 patients with 124 outcomes - Fold 2 618 patients with 124 outcomes
73 covariates in train data
Test Set:
353 patients with 71 outcomes
Running Cyclops
Done.
GLM fit status: OK
Creating variable importance data frame
Prediction took 0.391 secs
Time to fit model: 1.56 secs
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 1.09 secs
Prediction took 0.278 secs
Prediction done in: 2.25 secs
Calculating Performance for Test
=============
AUC 69.51
95% lower AUC: 62.68
95% upper AUC: 76.34
AUPRC: 30.91
Brier: 0.14
Eavg: 0.01
Calibration in large- Mean predicted risk 0.1987 : observed risk 0.2011
Calibration in large- Intercept 0.0621
Weak calibration intercept: 0.0621 - gradient:1.0373
Hosmer-Lemeshow calibration gradient: 0.90 intercept: 0.02
Average Precision: 0.33
Calculating Performance for Train
=============
AUC 74.47
95% lower AUC: 71.12
95% upper AUC: 77.83
AUPRC: 40.88
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.2005 : observed risk 0.2005
Calibration in large- Intercept 0.1642
Weak calibration intercept: 0.1642 - gradient:1.1391
Hosmer-Lemeshow calibration gradient: 1.26 intercept: -0.04
Average Precision: 0.41
Calculating Performance for CV
=============
AUC 70.17
95% lower AUC: 66.55
95% upper AUC: 73.79
AUPRC: 33.71
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.2004 : observed risk 0.2005
Calibration in large- Intercept 0.2634
Weak calibration intercept: 0.2634 - gradient:1.2179
Hosmer-Lemeshow calibration gradient: 1.24 intercept: -0.05
Average Precision: 0.33
Time to calculate evaluation metrics: 0.448 secs
Run finished successfully.
Saving PlpResult
Creating directory to save model
plpResult saved to ..\/home/hornik/tmp/scratch/Rtmp2T7cT4/saveLoc318012435e2fd5/lcc/learningCurve_2\plpResult
runPlp time taken: 10.9 secs
Finished in 22 secs.
type should be minmax or robust
type should be minmax or robust
type should be minmax or robust
type should be minmax or robust
settings$clip needs to be a boolean
Starting min-max normalization of continuous features
Finished min-max normalization of continuous features in 1.15 secs
Applying min-max normalization of continuous features to test data
Finished min-max normalization of continuous features in 0.946 secs
Starting robust normalization of continuous features
Finished robust normalization in 1.28 secs
Applying robust normalization of continuous features to test data
Finished robust normalization in 0.92 secs
Starting robust normalization of continuous features
Finished robust normalization in 1.21 secs
Applying robust normalization of continuous features to test data
Finished robust normalization in 0.97 secs
Starting min-max normalization of continuous features
Finished min-max normalization of continuous features in 1.75 secs
Applying min-max normalization of continuous features to test data
Finished min-max normalization of continuous features in 1.1 secs
Starting robust normalization of continuous features
Finished robust normalization in 1.2 secs
Applying robust normalization of continuous features to test data
Finished robust normalization in 1.1 secs
1 needs to be a boolean
"tertet" needs to be a boolean
1 needs to be >= 2
"tertet" needs to be >= 1
2 needs to be <= 1
"tertet" needs to be <= 1
1 needs to be > 2
0 needs to be > 0
"tertet" needs to be > 1
2 needs to be < 1
0 needs to be < 0
"tertet" needs to be < 1
NULL cannot be empty
"dsdsds" should be of class:double
1.5 must be a single whole number
c(1, 2) must be a single whole number
"1" must be a single whole number
NA_real_ must be a single whole number
Inf must be a single whole number
c(1, 2) must be a single finite numeric value
"1" must be a single finite numeric value
NA_real_ must be a single finite numeric value
Inf must be a single finite numeric value
"dsdsds" should be dsds or double
c("dsdsds", "double") should be dsdsds or double
NA_character_ should be dsdsds or double
Creating save directory at: /home/hornik/tmp/scratch/Rtmp2T7cT4/file31801256fd8590
file does not exist
Column names of data.frame(a = 1:2, b = 1:2) are not correct
Column types of data.frame(a = 1:2, b = 1:2) are not correct
includeAllOutcomes should be of class:logical
includeAllOutcomes should be of class:logical
firstExposureOnly should be of class:logical
firstExposureOnly should be of class:logical
washoutPeriod should be of class:numericwashoutPeriod should be of class:integer
washoutPeriod needs to be >= 0
removeSubjectsWithPriorOutcome should be of class:logical
removeSubjectsWithPriorOutcome should be of class:logical
priorOutcomeLookback should be of class:numericpriorOutcomeLookback should be of class:integer
priorOutcomeLookback needs to be >= 0
requireTimeAtRisk should be of class:logical
requireTimeAtRisk should be of class:logical
minTimeAtRisk should be of class:numericminTimeAtRisk should be of class:integer
minTimeAtRisk needs to be >= 0
riskWindowStart should be of class:numericriskWindowStart should be of class:integer
riskWindowEnd should be of class:numericriskWindowEnd should be of class:integer
startAnchor should be of class:character
endAnchor should be of class:character
minFraction needs to be >= 0
minFraction should be of class:numericminFraction should be of class:integer
removeRedundancy should be of class:logical
removeRedundancy should be of class:logical
normalize should be of class:logical
normalize should be of class:logical
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 0.938 secs
recal initial baseline hazard: 0.8
recal initial offset: 0.15
recal initial timepoint: 220
Saving _problems/test-recalibration-244.R
recal initial baseline hazard: 0.85
recal initial offset: -0.05
recal initial timepoint: 240
Saving _problems/test-recalibration-279.R
recal initial baseline hazard: 0.9
recal initial offset: 0
recal initial timepoint: 365
Saving _problems/test-recalibration-306.R
Connecting using SQLite driver
All or some PLP result tables do not exist, tables being recreated
Deleting existing tables
Creating PLP results tables
Executing SQL took 0.0229 secs
PLP result migration being applied
Migrating data set
Migrator using SQL files in PatientLevelPrediction
Connecting using SQLite driver
Creating migrations table
Executing SQL took 0.0042 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.00382 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.00376 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00636 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00381 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Executing migration: Migration_3-add_model_name.sql
Executing SQL took 0.00411 secs
Saving migration: Migration_3-add_model_name.sql
Executing SQL took 0.00711 secs
Migration complete Migration_3-add_model_name.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.00469 secs
Connecting using SQLite driver
runSplitData should be of class:logical
runSampleData should be of class:logical
runFeatureEngineering should be of class:logical
runPreprocessData should be of class:logical
runModelDevelopment should be of class:logical
runCovariateSummary should be of class:logical
numberOutcomestoNonOutcomes should be of class:numericnumberOutcomestoNonOutcomes should be of class:integer
numberOutcomestoNonOutcomes needs to be > 0
sampleSeed should be of class:numericsampleSeed should be of class:integer
type should be of class:character
plpData object
At risk concept ID:
Outcome concept ID(s):
plpData object summary
At risk cohort concept ID:
Outcome concept ID(s):
People:
Outcome counts:
Creating directory to save model
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Computing covariate prevalence
Fitting outcome model(s)
Generating covariates
Generating cohorts
Generating outcomes
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 1.04 secs
Prediction took 0.339 secs
Prediction done in: 2.21 secs
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 1.01 secs
predict risk probabilities using predictGlm
Prediction took 0.324 secs
Prediction done in: 2.14 secs
predict risk probabilities using predictGlm
Prediction took 0.258 secs
Prediction done in: 0.284 secs
Restricting tarEnd to end of target cohort
Outcome is 0 or 1
Population created with: 20 observations, 20 unique subjects and 20 outcomes
Population created in 0.0836 secs
Warning: restrictPlpDataSettings are not the same in models and validationDesign,
using from design
restrictPlpDataSettings not set in design, using model's
targetId should be of class:numerictargetId should be of class:integer
outcomeId should be of class:numericoutcomeId should be of class:integer
targetId should be of class:numerictargetId should be of class:integer
outcomeId should be of class:numericoutcomeId should be of class:integer
populationSettings should be of class:populationSettings
recalibrate should be of class:characterrecalibrate should be of class:NULL
runCovariateSummary should be of class:logical
[ FAIL 3 | WARN 0 | SKIP 149 | PASS 1123 ]
══ Skipped tests (149) ═════════════════════════════════════════════════════════
• On CRAN (145): 'test-LightGBM.R:18:3', 'test-LightGBM.R:54:3',
'test-LightGBM.R:75:3', 'test-UploadToDatabase.R:210:3',
'test-UploadToDatabase.R:284:3', 'test-UploadToDatabase.R:456:3',
'test-UploadToDatabase.R:507:3', 'test-UploadToDatabase.R:571:3',
'test-UploadToDatabase.R:635:3', 'test-UploadToDatabase.R:743:3',
'test-andromedahelperfunctions.R:19:3', 'test-barFitState.R:42:3',
'test-barFitState.R:109:3', 'test-compatModelSettings.R:248:3',
'test-compatModelSettings.R:303:3', 'test-compatModelSettings.R:367:3',
'test-cyclopsModels.R:159:3', 'test-cyclopsModels.R:216:3',
'test-cyclopsModels.R:233:3', 'test-cyclopsModels.R:293:3',
'test-cyclopsModels.R:328:3', 'test-cyclopsModels.R:361:3',
'test-cyclopsModels.R:382:3', 'test-cyclopsModels.R:435:3',
'test-cyclopsModels.R:473:3', 'test-cyclopsModels.R:524:3',
'test-cyclopsModels.R:581:3', 'test-cyclopsModels.R:619:3',
'test-cyclopsModels.R:641:3', 'test-cyclopsModels.R:681:3',
'test-cyclopsModels.R:713:3', 'test-cyclopsModels.R:740:3',
'test-cyclopsModels.R:777:3', 'test-dataSplitting.R:154:3',
'test-dataSplitting.R:378:3', 'test-dataSplitting.R:401:3',
'test-dataSplitting.R:595:3', 'test-diagnostic.R:60:3',
'test-diagnostic.R:111:3', 'test-evaluation.R:17:3',
'test-evaluation.R:46:3', 'test-existingModel.R:18:3',
'test-existingModel.R:74:3', 'test-existingModel.R:124:3',
'test-extractData.R:17:3', 'test-extractData.R:43:3',
'test-extractData.R:62:3', 'test-extractData.R:93:3',
'test-extractData.R:112:3', 'test-extractData.R:156:3',
'test-extractData.R:185:3', 'test-extractData.R:204:3',
'test-extractData.R:231:3', 'test-featureEngineering.R:40:3',
'test-featureEngineering.R:56:3', 'test-featureEngineering.R:84:3',
'test-featureEngineering.R:130:3', 'test-featureEngineering.R:158:3',
'test-featureEngineering.R:203:3', 'test-featureEngineering.R:248:3',
'test-featureEngineering.R:316:3', 'test-featureEngineering.R:343:3',
'test-featureImportance.R:19:3', 'test-featureImportance.R:45:3',
'test-fitting.R:20:3', 'test-fitting.R:32:3', 'test-formatting.R:103:3',
'test-helperfunctions.R:39:3', 'test-imputation.R:261:3',
'test-imputation.R:282:3', 'test-imputation.R:333:3',
'test-learningCurves.R:35:3', 'test-learningCurves.R:50:3',
'test-learningCurves.R:74:3', 'test-multiplePlp.R:86:3',
'test-multiplePlp.R:132:3', 'test-plotting.R:21:3', 'test-plotting.R:60:3',
'test-plotting.R:74:3', 'test-plotting.R:91:3', 'test-plotting.R:126:3',
'test-plotting.R:148:3', 'test-plotting.R:155:3', 'test-plotting.R:165:3',
'test-plotting.R:174:3', 'test-plotting.R:182:3', 'test-plotting.R:199:3',
'test-plotting.R:208:3', 'test-plotting.R:218:3', 'test-plotting.R:225:3',
'test-population.R:263:3', 'test-population.R:471:3',
'test-prediction.R:18:3', 'test-prediction.R:39:3', 'test-prediction.R:78:3',
'test-preprocessingData.R:56:3', 'test-preprocessingData.R:118:3',
'test-rclassifier.R:18:3', 'test-rclassifier.R:51:3',
'test-rclassifier.R:71:3', 'test-rclassifier.R:134:3',
'test-recalibration.R:61:3', 'test-runPlpHelpers.R:18:3',
'test-runPlpHelpers.R:32:3', 'test-sampling.R:91:3', 'test-sampling.R:132:3',
'test-sampling.R:168:3', 'test-saveloadplp.R:31:3',
'test-saveloadplp.R:46:3', 'test-saveloadplp.R:87:3',
'test-saveloadplp.R:171:3', 'test-sklearnClassifier.R:19:3',
'test-sklearnClassifier.R:43:3', 'test-sklearnClassifier.R:54:3',
'test-sklearnClassifier.R:117:3', 'test-sklearnClassifier.R:132:3',
'test-sklearnClassifier.R:164:3', 'test-sklearnClassifier.R:201:3',
'test-sklearnClassifier.R:237:3', 'test-sklearnClassifier.R:281:3',
'test-sklearnClassifier.R:301:3', 'test-sklearnClassifierSettings.R:19:3',
'test-sklearnClassifierSettings.R:37:3',
'test-sklearnClassifierSettings.R:46:3',
'test-sklearnClassifierSettings.R:81:3',
'test-sklearnClassifierSettings.R:94:3',
'test-sklearnClassifierSettings.R:122:3', 'test-sklearnJson.R:39:3',
'test-sklearnJson.R:57:3', 'test-sklearnJson.R:78:3',
'test-sklearnJson.R:99:3', 'test-sklearnJson.R:117:3',
'test-sklearnJson.R:137:5', 'test-sklearnJson.R:137:5',
'test-sklearnJson.R:186:3', 'test-validation.R:44:3',
'test-validation.R:68:3', 'test-validation.R:201:3',
'test-validation.R:226:3', 'test-validation.R:241:3',
'test-validation.R:262:3', 'test-validation.R:287:3',
'test-validation.R:315:3', 'test-validation.R:345:3',
'test-validation.R:368:3'
• PostgreSQL test connection is not configured (4):
'test-UploadToDatabase.R:51:3', 'test-UploadToDatabase.R:103:3',
'test-UploadToDatabase.R:123:3', 'test-UploadToDatabase.R:172:3'
══ Failed tests ════════════════════════════════════════════════════════════════
── Error ('test-recalibration.R:241:3'): weakRecalibration survival uses baseline when missing raw values ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─base::suppressWarnings(...) at test-recalibration.R:241:3
2. │ └─base::withCallingHandlers(...)
3. └─PatientLevelPrediction:::weakRecalibration(...)
4. └─survival::basehaz(f.slope)
5. ├─survival::survfit(fit, se.fit = FALSE)
6. └─survival:::survfit.coxph(fit, se.fit = FALSE)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-recalibration.R:276:3'): weakRecalibration survival propagates raw values ──
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. ├─base::suppressWarnings(...) at test-recalibration.R:276:3
2. │ └─base::withCallingHandlers(...)
3. └─PatientLevelPrediction:::weakRecalibration(...)
4. └─survival::basehaz(f.slope)
5. ├─survival::survfit(fit, se.fit = FALSE)
6. └─survival:::survfit.coxph(fit, se.fit = FALSE)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
── Error ('test-recalibration.R:303:3'): survival ──────────────────────────────
<subscriptOutOfBoundsError/error/condition>
Error in `a$specials[[i]]`: subscript out of bounds
Backtrace:
▆
1. └─PatientLevelPrediction::recalibratePlp(...) at test-recalibration.R:303:3
2. ├─base::do.call(method, list(prediction = prediction, columnType = typeColumn))
3. └─PatientLevelPrediction:::weakRecalibration(...)
4. └─survival::basehaz(f.slope)
5. ├─survival::survfit(fit, se.fit = FALSE)
6. └─survival:::survfit.coxph(fit, se.fit = FALSE)
7. └─survival:::model.matrix.coxph(object, data = mf)
8. └─stats::delete.response(object$terms)
9. └─stats (local) off(a$specials[[i]])
[ FAIL 3 | WARN 0 | SKIP 149 | PASS 1123 ]
Error:
! Test failures.
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc