Last updated on 2026-04-14 09:55:34 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 6.6.0 | 18.92 | 473.15 | 492.07 | OK | |
| r-devel-linux-x86_64-debian-gcc | 6.6.0 | 11.60 | 329.97 | 341.57 | OK | |
| r-devel-linux-x86_64-fedora-clang | 6.6.0 | 36.00 | 541.79 | 577.79 | ERROR | |
| r-devel-linux-x86_64-fedora-gcc | 6.6.0 | 36.00 | 769.46 | 805.46 | OK | |
| r-devel-macos-arm64 | 6.6.0 | 4.00 | 109.00 | 113.00 | OK | |
| r-devel-windows-x86_64 | 6.6.0 | 21.00 | 544.00 | 565.00 | OK | |
| r-patched-linux-x86_64 | 6.6.0 | 17.22 | 449.83 | 467.05 | OK | |
| r-release-linux-x86_64 | 6.6.0 | 18.32 | 455.90 | 474.22 | OK | |
| r-release-macos-arm64 | 6.6.0 | 4.00 | 106.00 | 110.00 | OK | |
| r-release-macos-x86_64 | 6.6.0 | 13.00 | 421.00 | 434.00 | OK | |
| r-release-windows-x86_64 | 6.6.0 | 22.00 | 0.00 | 22.00 | OK | |
| r-oldrel-macos-arm64 | 6.6.0 | 4.00 | 110.00 | 114.00 | OK | |
| r-oldrel-macos-x86_64 | 6.6.0 | 13.00 | 459.00 | 472.00 | OK | |
| r-oldrel-windows-x86_64 | 6.6.0 | 30.00 | 726.00 | 756.00 | OK |
Version: 6.6.0
Check: tests
Result: ERROR
Running ‘testthat.R’ [4m/10m]
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_5.3.zip'
Content type 'application/zip' length 6861852 bytes (6.5 MB)
==================================================
downloaded 6.5 MB
attempting to extract and load: /tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/GiBleed_5.3.zip to: /tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/GiBleed_5.3.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 0.0465 secs
Fetching cohorts from server
Loading cohorts took 1.42 secs
Constructing features on server
Executing SQL took 1.46 secs
Fetching data from server
Fetching data took 3.17 secs
Fetching outcomes from server
Loading outcomes took 0.298 secs
Use timeStamp: TRUE
Creating save directory at: /tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/saveLoca3b9f3e9c8785/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 6.6.0
Study started at: 2026-04-14 07:02:24.302064
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 0.208 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 11.2 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
Removing 2 redundant covariates
Removing 0 infrequent covariates
Normalizing covariates
Tidying covariates took 12.7 secs
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 1.51 secs
Time to fit model: 25.8 secs
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 3.46 secs
Prediction took 0.763 secs
Prediction done in: 7.39 secs
Calculating Performance for Test
=============
AUC 71.50
95% lower AUC: 65.39
95% upper AUC: 77.60
AUPRC: 34.74
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.19 : observed risk 0.196
Calibration in large- Intercept 0.2628
Weak calibration intercept: 0.2628 - gradient:1.1745
Hosmer-Lemeshow calibration gradient: 1.08 intercept: -0.02
Average Precision: 0.36
Calculating Performance for Train
=============
AUC 72.94
95% lower AUC: 69.45
95% upper AUC: 76.43
AUPRC: 40.92
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.1976 : observed risk 0.1976
Calibration in large- Intercept 0.1551
Weak calibration intercept: 0.1551 - gradient:1.1274
Hosmer-Lemeshow calibration gradient: 1.14 intercept: -0.02
Average Precision: 0.41
Calculating Performance for CV
=============
AUC 67.71
95% lower AUC: 63.99
95% upper AUC: 71.43
AUPRC: 33.44
Brier: 0.14
Eavg: 0.02
Calibration in large- Mean predicted risk 0.1981 : observed risk 0.1976
Calibration in large- Intercept 0.1222
Weak calibration intercept: 0.1222 - gradient:1.1017
Hosmer-Lemeshow calibration gradient: 1.06 intercept: -0.01
Average Precision: 0.34
Time to calculate evaluation metrics: 7.18 secs
Calculating covariate summary @ 2026-04-14 07:03:33.556348
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
Calculating summary for subgroup TestWithNoOutcome
Restricting to subgroup
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 @ 2026-04-14 07:04:13.324947
Time to calculate covariate summary: 39.8 secs
Run finished successfully.
Saving PlpResult
Creating directory to save model
plpResult saved to ..\/tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/saveLoca3b9f3e9c8785/Test\plpResult
runPlp time taken: 1.82 mins
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 0.187 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 4.76 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.674 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.306 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.23 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 3.03 secs
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.155 secs
Saving diagnosePlp to /tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/saveLoca3b9f3e9c8785/Test/diagnosePlp.rds
Outcome is 0 or 1
Population created with: 1800 observations, 1800 unique subjects and 355 outcomes
Population created in 0.145 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 6.77 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 5.93 secs
Use timeStamp: TRUE
Creating save directory at: /tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/saveLoca3b9f3e9c8785/tinyResults/tinyFit
Patient-Level Prediction Package version 6.6.0
Study started at: 2026-04-14 07:05:21.491952
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 0.163 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 9.04 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 15.3 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 1.06 secs
Time to fit model: 6.57 secs
Removing infrequent and redundant covariates and normalizing
Removing infrequent and redundant covariates covariates and normalizing took 2.83 secs
Prediction took 1.09 secs
Prediction done in: 27.6 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 0.3019 : observed risk 0.3023
Calibration in large- Intercept -0.1268
Weak calibration intercept: -0.1268 - gradient:0.8213
Hosmer-Lemeshow calibration gradient: 0.89 intercept: 0.04
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 9e-04
Weak calibration intercept: 9e-04 - gradient:1.0014
Hosmer-Lemeshow calibration gradient: 1.00 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 -0.0908
Weak calibration intercept: -0.0908 - gradient:0.8729
Hosmer-Lemeshow calibration gradient: 1.00 intercept: 0.02
Average Precision: 0.39
Time to calculate evaluation metrics: 1.1 secs
Run finished successfully.
Saving PlpResult
Creating directory to save model
plpResult saved to ..\/tmp/RtmpPiq9l7/working_dir/RtmpMNzOOe/saveLoca3b9f3e9c8785/tinyResults/tinyFit\plpResult
runPlp time taken: 1.07 mins
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 0.0637 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 0.0113 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.0325 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.0272 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.0169 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.00971 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.0126 secs
Connecting using SQLite driver
Adding new model settings
Inserting data took 0.0382 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 0.06 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 0.011 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.0124 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.0108 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.0175 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.011 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.0121 secs
Connecting using SQLite driver
Adding new model settings
Inserting data took 0.0245 secs
Adding new model settings
Inserting data took 0.0434 secs
Adding new model settings
Inserting data took 0.0248 secs
Adding new model settings
Inserting data took 0.0255 secs
Adding new model settings
Inserting data took 0.0401 secs
Adding new model settings
Inserting data took 0.0295 secs
Adding new model settings
Inserting data took 0.0264 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 0.0519 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 0.0135 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.028 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.0259 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.027 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.0339 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.0466 secs
Connecting using SQLite driver
Adding new hyperparameter settings
Inserting data took 0.0252 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 0.162 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 0.0259 secs
Migrations table created
Executing migration: Migration_1-store_version.sql
Executing SQL took 0.136 secs
Saving migration: Migration_1-store_version.sql
Executing SQL took 0.0839 secs
Migration complete Migration_1-store_version.sql
Executing migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.164 secs
Saving migration: Migration_2-add_hyperparameter_settings.sql
Executing SQL took 0.112 secs
Migration complete Migration_2-add_hyperparameter_settings.sql
Closing database connection
Updating version number
Connecting using SQLite driver
Executing SQL took 0.0381 secs
Connecting using SQLite driver
Adding TAR
Executing SQL took 0.00981 secs
tarId: 1
Adding cohort target
Inserting data took 0.0241 secs
Adding cohort target
Inserting data took 0.0643 secs
tId: 1
Adding cohort outcome
Inserting data took 0.054 secs
Adding cohort outcome
Inserting data took 0.0699 secs
oId: 2
Adding new population settings
Inserting data took 0.0602 secs
popSetId: 1
Adding new covariate settings
Inserting data took 0.0575 secs
covSetId: 1
Adding new model settings
Inserting data took 0.0326 secs
modSetId: 1
Adding new plp data settings
Inserting data took 0.0286 secs
plpDataSetId: 1
Adding new feature_engineering settings
Inserting data took 0.0901 secs
FESetId: 1
Adding new sample settings
Inserting data took 0.0945 secs
sampleSetId: 1
Adding new tidy covariates settings
Inserting data took 0.0643 secs
tidySetId: 1
Adding new split settings
Inserting data took 0.0555 secs
splitId: 1
Adding new hyperparameter settings
Inserting data took 0.0312 secs
hyperparameterSetId: 2
Executing SQL took 0.0125 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 0.261 secs
splitId: 2
Adding new hyperparameter settings
Inserting data took 0.0253 secs
hyperparameterSetId: 3
Executing SQL took 0.0112 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: 0.00133 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 @ 2026-04-14 07:07:33.142415
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 @ 2026-04-14 07:07:35.704345
Time to calculate covariate summary: 2.56 secs
Calculating covariate summary @ 2026-04-14 07:07:35.729517
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 @ 2026-04-14 07:07:39.464519
Time to calculate covariate summary: 3.74 secs
Calculating covariate summary @ 2026-04-14 07:07:39.501577
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 @ 2026-04-14 07:07:55.811994
Time to calculate covariate summary: 16.3 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
seed: 52583
seed: 52583
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: 52583
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: 52583
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: 52583
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: 52583
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: 52583
seed: 52583
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: 52583
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)
Evaluating survival model at time: 365 days
C-statistic: 0.39291 (0.226-0.56)
E-statistic: 0.026553307531677
E-statistic 90%: 0.0519477918330355
Warning: PredictionDistribution not available for survival models
Time to calculate evaluation metrics: 7.25 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: 0.34
Eavg: 0.51
Calibration in large- Mean predicted risk 0.5135 : 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: 0.35 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 0.757 secs
Prediction done in: 0.791 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 8.14659452438354 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 2 features with missing values
Imputation done in time: 12.7 secs
Applying imputation to test data with simpleImputer
using method: mean and missing threshold: 0.3
Imputation done in time: 20.3 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 31 features with missing values
Imputation done in time: 25.6 secs
Applying imputation to test data with simpleImputer
using method: mean and missing threshold: 0.95
Imputation done in time: 15.2 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 2 features with missing values
Imputation done in time: 25 secs
Flavor: r-devel-linux-x86_64-fedora-clang