R Under development (unstable) (2025-02-08 r87709 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > 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: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU/GiBleed_5.3.zip to: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU/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 0.0149 secs Fetching cohorts from server Loading cohorts took 0.0254 secs Constructing features on server Executing SQL took 0.08 secs Fetching data from server Fetching data took 0.3 secs Fetching outcomes from server Loading outcomes took 0.0289 secs Use timeStamp: TRUE Creating save directory at: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/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 6.4.0 Study started at: 2025-02-10 13:07:31.513208 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 0.053 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 0.345 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 0.942 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 0.142 secs Time to fit model: 1.36 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.185 secs Prediction took 0.114 secs Prediction done in: 0.413 secs Calculating Performance for Test ============= AUC 71.47 95% lower AUC: 65.41 95% upper AUC: 77.53 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:1.2181 Hosmer-Lemeshow calibration gradient: 1.23 intercept: -0.06 Average Precision: 0.37 Calculating Performance for Train ============= AUC 72.44 95% lower AUC: 69.00 95% upper AUC: 75.87 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 67.94 95% lower AUC: 64.27 95% upper AUC: 71.62 AUPRC: 31.81 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: 1.14 intercept: -0.03 Average Precision: 0.32 Time to calculate evaluation metrics: 0.323 secs Calculating covariate summary @ 2025-02-10 13:07:35.221926 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 @ 2025-02-10 13:07:36.149575 Time to calculate covariate summary: 0.928 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/Test\plpResult runPlp time taken: 4.73 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 0.0445 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0435 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0433 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.056 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0536 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0531 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0437 secs Saving diagnosePlp to D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/Test/diagnosePlp.rds Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.046 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 0.375 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 0.379 secs Use timeStamp: TRUE Creating save directory at: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/tinyResults/tinyFit Patient-Level Prediction Package version 6.4.0 Study started at: 2025-02-10 13:07:38.8632 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 0.042 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 0.361 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 0.784 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 0.121 secs Time to fit model: 0.356 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.171 secs Prediction took 0.12 secs Prediction done in: 0.393 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 -0.1163 Weak calibration intercept: -0.1163 - gradient:0.8358 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 0.0143 Weak calibration intercept: 0.0143 - gradient:1.0204 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 -0.0755 Weak calibration intercept: -0.0755 - gradient:0.8945 Hosmer-Lemeshow calibration gradient: 1.02 intercept: 0.01 Average Precision: 0.39 Time to calculate evaluation metrics: 0.259 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/tinyResults/tinyFit\plpResult runPlp time taken: 2.5 secs Warning: PredictionDistribution not available for survival models No databaseRefId specified so using schema as unique database identifier Calculating covariate summary @ 2025-02-10 13:07:42.966313 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 @ 2025-02-10 13:07:43.187548 Time to calculate covariate summary: 0.221 secs Calculating covariate summary @ 2025-02-10 13:07:43.200929 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 @ 2025-02-10 13:07:43.420115 Time to calculate covariate summary: 0.219 secs Calculating covariate summary @ 2025-02-10 13:07:43.439821 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 @ 2025-02-10 13:07:43.875609 Time to calculate covariate summary: 0.436 secs 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: 46612 seed: 46612 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: 46612 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: 46612 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: 46612 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: 46612 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: 46612 seed: 46612 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: 46612 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: 0.4227 (0.171-0.674) E-statistic: 0.0476871725988936 E-statistic 90%: 0.0694749123261976 Warning: PredictionDistribution not available for survival models Time to calculate evaluation metrics: 0.346 secs Setting levels: control = 0, case = 1 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 equal to logistic predict risk probabilities using predictGlm Prediction took 0.112 secs Prediction done in: 0.115 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 Removing features rarer than threshold: 0.1 from the data Finished rare feature removal in 0.19 secs Applying rare feature removal with rate below: 0.1 to test data Finished rare feature removal in 0.0709 secs 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 0.702001094818115 secs 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. 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. Use timeStamp: TRUE Creating save directory at: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/lcc/learningCurve_1 Patient-Level Prediction Package version 6.4.0 Study started at: 2025-02-10 13:07:48.346188 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 0.0672 secs seed: 91371 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 0.332 secs Train Set: Fold 1 531 patients with 107 outcomes - Fold 2 530 patients with 106 outcomes 74 covariates in train data Test Set: 353 patients with 71 outcomes Removing 2 redundant covariates Removing 0 infrequent covariates Normalizing covariates Tidying covariates took 0.803 secs Train Set: Fold 1 531 patients with 107 outcomes - Fold 2 530 patients with 106 outcomes 72 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 0.132 secs Time to fit model: 0.447 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.219 secs Prediction took 0.12 secs Prediction done in: 0.45 secs Calculating Performance for Test ============= AUC 72.07 95% lower AUC: 65.90 95% upper AUC: 78.24 AUPRC: 33.53 Brier: 0.14 Eavg: 0.03 Calibration in large- Mean predicted risk 0.2092 : observed risk 0.2011 Calibration in large- Intercept 0.1187 Weak calibration intercept: 0.1187 - gradient:1.1535 Hosmer-Lemeshow calibration gradient: 1.00 intercept: 0.01 Average Precision: 0.35 Calculating Performance for Train ============= AUC 72.74 95% lower AUC: 68.87 95% upper AUC: 76.61 AUPRC: 42.06 Brier: 0.14 Eavg: 0.02 Calibration in large- Mean predicted risk 0.2008 : observed risk 0.2008 Calibration in large- Intercept 0.1479 Weak calibration intercept: 0.1479 - gradient:1.1232 Hosmer-Lemeshow calibration gradient: 1.17 intercept: -0.02 Average Precision: 0.42 Calculating Performance for CV ============= AUC 70.35 95% lower AUC: 66.26 95% upper AUC: 74.44 AUPRC: 35.51 Brier: 0.14 Eavg: 0.02 Calibration in large- Mean predicted risk 0.2007 : observed risk 0.2008 Calibration in large- Intercept 0.2476 Weak calibration intercept: 0.2476 - gradient:1.2022 Hosmer-Lemeshow calibration gradient: 1.20 intercept: -0.04 Average Precision: 0.36 Time to calculate evaluation metrics: 0.341 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/lcc/learningCurve_1\plpResult runPlp time taken: 2.75 secs Creating save directory at: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/lcc/learningCurve_2 Patient-Level Prediction Package version 6.4.0 Study started at: 2025-02-10 13:07:51.099516 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 0.0624 secs seed: 91371 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 0.424 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 1.06 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 0.14 secs Time to fit model: 0.474 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.204 secs Prediction took 0.121 secs Prediction done in: 0.432 secs Calculating Performance for Test ============= AUC 73.55 95% lower AUC: 67.45 95% upper AUC: 79.65 AUPRC: 36.96 Brier: 0.14 Eavg: 0.03 Calibration in large- Mean predicted risk 0.2115 : observed risk 0.2011 Calibration in large- Intercept 0.0654 Weak calibration intercept: 0.0654 - gradient:1.124 Hosmer-Lemeshow calibration gradient: 1.04 intercept: -0.02 Average Precision: 0.37 Calculating Performance for Train ============= AUC 73.05 95% lower AUC: 69.49 95% upper AUC: 76.61 AUPRC: 42.92 Brier: 0.14 Eavg: 0.02 Calibration in large- Mean predicted risk 0.2005 : observed risk 0.2005 Calibration in large- Intercept 0.1295 Weak calibration intercept: 0.1295 - gradient:1.1085 Hosmer-Lemeshow calibration gradient: 1.10 intercept: -0.02 Average Precision: 0.43 Calculating Performance for CV ============= AUC 68.28 95% lower AUC: 64.48 95% upper AUC: 72.09 AUPRC: 32.94 Brier: 0.15 Eavg: 0.02 Calibration in large- Mean predicted risk 0.1995 : observed risk 0.2005 Calibration in large- Intercept 0.0532 Weak calibration intercept: 0.0532 - gradient:1.0376 Hosmer-Lemeshow calibration gradient: 0.98 intercept: 0.00 Average Precision: 0.33 Time to calculate evaluation metrics: 0.361 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\saveLoc140d45c169c7/lcc/learningCurve_2\plpResult runPlp time taken: 3.14 secs Finished in 6 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 0.272 secs Applying min-max normalization of continuous features to test data Finished min-max normalization of continuous features in 0.106 secs Starting robust normalization of continuous features Finished robust normalization in 0.37 secs Applying robust normalization of continuous features to test data Finished robust normalization in 0.14 secs Starting robust normalization of continuous features Finished robust normalization in 0.484 secs Applying robust normalization of continuous features to test data Finished robust normalization in 0.189 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 "dsdsds" should be dsds or double Creating save directory at: D:\temp\2025_02_10_13_05_17_11821\RtmpCUbwWU\file140d4577e63f5 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 recal initial baseline hazard: 0.9 recal initial offset: 0 recal initial timepoint: 365 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 Warning: The previous documentation for `numberOutcomestoNonOutcomes` used to not reflect the functionality and has now been changed. The user needs to make sure the code is not relying on what was in the docs previously. 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) Generating covariates Generating cohorts Generating outcomes 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 0 | WARN 0 | SKIP 117 | PASS 610 ] ══ Skipped tests (117) ═════════════════════════════════════════════════════════ • On CRAN (113): 'test-LightGBM.R:18:3', 'test-LightGBM.R:68:3', 'test-LightGBM.R:89:3', 'test-UploadToDatabase.R:202:3', 'test-UploadToDatabase.R:252:3', 'test-UploadToDatabase.R:307:3', 'test-UploadToDatabase.R:415:3', 'test-andromedahelperfunctions.R:19:3', 'test-cyclopsModels.R:93:3', 'test-cyclopsModels.R:149:3', 'test-cyclopsModels.R:166:3', 'test-cyclopsModels.R:216:3', 'test-cyclopsModels.R:228:3', 'test-dataSplitting.R:129:3', 'test-dataSplitting.R:232:3', 'test-dataSplitting.R:426:3', 'test-diagnostic.R:60:3', 'test-diagnostic.R:111:3', 'test-evaluation.R:17:3', 'test-evaluation.R:27:3', 'test-existingModel.R:18:3', 'test-existingModel.R:75:3', 'test-existingModel.R:123:3', '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-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-featureImportance.R:19:3', 'test-featureImportance.R:45:3', 'test-fitting.R:20:3', 'test-fitting.R:33:3', 'test-formatting.R:103:3', 'test-helperfunctions.R:33:3', 'test-imputation.R:91:3', 'test-imputation.R:181:3', 'test-learningCurves.R:35:3', 'test-learningCurves.R:50:3', 'test-learningCurves.R:74:3', 'test-multiplePlp.R:82:3', 'test-multiplePlp.R:128:3', '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:193:3', 'test-plotting.R:202:3', '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:62:3', 'test-rclassifier.R:82:3', 'test-rclassifier.R:116:3', 'test-recalibration.R:58:3', 'test-runPlpHelpers.R:18:3', 'test-runPlpHelpers.R:32:3', 'test-sampling.R:91:3', 'test-sampling.R:132:3', 'test-sampling.R:163:3', '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:51:3', 'test-sklearnClassifier.R:62:3', 'test-sklearnClassifier.R:95:3', 'test-sklearnClassifier.R:111:3', 'test-sklearnClassifier.R:131:3', 'test-sklearnClassifier.R:156:3', 'test-sklearnClassifier.R:180:3', 'test-sklearnClassifier.R:197:3', 'test-sklearnClassifier.R:218:3', 'test-sklearnClassifierSettings.R:19:3', 'test-sklearnClassifierSettings.R:51:3', 'test-sklearnClassifierSettings.R:61:3', 'test-sklearnClassifierSettings.R:116:3', 'test-sklearnClassifierSettings.R:135:3', 'test-sklearnClassifierSettings.R:181:3', 'test-sklearnJson.R:39:3', 'test-sklearnJson.R:57:3', 'test-sklearnJson.R:75:3', 'test-sklearnJson.R:93:3', 'test-sklearnJson.R:111:3', 'test-sklearnJson.R:130:3', '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' • only run on CI (4): 'test-UploadToDatabase.R:39:3', 'test-UploadToDatabase.R:92:3', 'test-UploadToDatabase.R:113:3', 'test-UploadToDatabase.R:163:3' [ FAIL 0 | WARN 0 | SKIP 117 | PASS 610 ] > > proc.time() user system elapsed 47.10 4.04 46.98