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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_07_25_13_05_16_23599\RtmpyANOa4/GiBleed_5.3.zip to: D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4/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 0.0259 secs Fetching cohorts from server Loading cohorts took 0.044 secs Constructing features on server Executing SQL took 0.157 secs Fetching data from server Fetching data took 0.821 secs Fetching outcomes from server Loading outcomes took 0.0559 secs Use timeStamp: TRUE Creating save directory at: D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/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 6.5.0 Study started at: 2025-07-25 13:09:00.459217 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 0.0972 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 1.35 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 1.9 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 0.349 secs Time to fit model: 2.28 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.426 secs Prediction took 0.303 secs Prediction done in: 1.28 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:1.2182 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 67.80 95% lower AUC: 64.12 95% upper AUC: 71.48 AUPRC: 31.64 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: 1.15 intercept: -0.03 Average Precision: 0.32 Time to calculate evaluation metrics: 0.564 secs Calculating covariate summary @ 2025-07-25 13:09:08.581831 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 @ 2025-07-25 13:09:12.264533 Time to calculate covariate summary: 3.68 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/Test\plpResult runPlp time taken: 12 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 0.0801 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.075 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0652 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0608 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0764 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0762 secs Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.083 secs Saving diagnosePlp to D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/Test/diagnosePlp.rds Outcome is 0 or 1 Population created with: 1800 observations, 1800 unique subjects and 355 outcomes Population created in 0.0728 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 1.08 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 1.09 secs Use timeStamp: TRUE Creating save directory at: D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/tinyResults/tinyFit Patient-Level Prediction Package version 6.5.0 Study started at: 2025-07-25 13:09:18.502381 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 0.074 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 1.05 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 1.84 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 0.28 secs Time to fit model: 0.719 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.39 secs Prediction took 0.285 secs Prediction done in: 1.21 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 -0.0779 Weak calibration intercept: -0.0779 - gradient:0.8911 Hosmer-Lemeshow calibration gradient: 1.02 intercept: 0.01 Average Precision: 0.39 Time to calculate evaluation metrics: 0.384 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/tinyResults/tinyFit\plpResult runPlp time taken: 5.9 secs Warning: PredictionDistribution not available for survival models No databaseRefId specified so using schema as unique database identifier Calculating covariate summary @ 2025-07-25 13:09:28.567003 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 @ 2025-07-25 13:09:29.330082 Time to calculate covariate summary: 0.763 secs Calculating covariate summary @ 2025-07-25 13:09:29.347532 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 @ 2025-07-25 13:09:30.085198 Time to calculate covariate summary: 0.738 secs Calculating covariate summary @ 2025-07-25 13:09:30.114245 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 @ 2025-07-25 13:09:31.689562 Time to calculate covariate summary: 1.58 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 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: 80071 seed: 80071 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: 80071 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: 80071 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: 80071 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: 80071 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: 80071 seed: 80071 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: 80071 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: 0.67627 (0.529-0.823) E-statistic: 0.0442589692233843 E-statistic 90%: 0.0729933294597477 Warning: PredictionDistribution not available for survival models Time to calculate evaluation metrics: 0.75 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 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 requireDenseMatrix should be of class:logical predict risk probabilities using predictGlm Prediction took 0.228 secs Prediction done in: 0.25 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 2.00622081756592 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. 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_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/lcc/learningCurve_1 Patient-Level Prediction Package version 6.5.0 Study started at: 2025-07-25 13:09:45.137868 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 0.0969 secs seed: 17525 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 1.23 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 1.89 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 0.341 secs Time to fit model: 0.804 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.437 secs Prediction took 0.279 secs Prediction done in: 1.28 secs Calculating Performance for Test ============= AUC 71.38 95% lower AUC: 64.91 95% upper AUC: 77.86 AUPRC: 36.05 Brier: 0.14 Eavg: 0.01 Calibration in large- Mean predicted risk 0.2102 : observed risk 0.2011 Calibration in large- Intercept 0.0238 Weak calibration intercept: 0.0238 - gradient:1.0743 Hosmer-Lemeshow calibration gradient: 0.99 intercept: -0.01 Average Precision: 0.37 Calculating Performance for Train ============= AUC 74.18 95% lower AUC: 70.38 95% upper AUC: 77.98 AUPRC: 44.99 Brier: 0.14 Eavg: 0.02 Calibration in large- Mean predicted risk 0.2008 : observed risk 0.2008 Calibration in large- Intercept 0.1832 Weak calibration intercept: 0.1832 - gradient:1.1534 Hosmer-Lemeshow calibration gradient: 1.15 intercept: -0.03 Average Precision: 0.45 Calculating Performance for CV ============= AUC 68.63 95% lower AUC: 64.48 95% upper AUC: 72.78 AUPRC: 34.48 Brier: 0.15 Eavg: 0.02 Calibration in large- Mean predicted risk 0.2013 : observed risk 0.2008 Calibration in large- Intercept 0.0841 Weak calibration intercept: 0.0841 - gradient:1.0719 Hosmer-Lemeshow calibration gradient: 1.09 intercept: -0.02 Average Precision: 0.35 Time to calculate evaluation metrics: 0.596 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/lcc/learningCurve_1\plpResult runPlp time taken: 6.51 secs Creating save directory at: D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/lcc/learningCurve_2 Patient-Level Prediction Package version 6.5.0 Study started at: 2025-07-25 13:09:51.670214 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 0.102 secs seed: 17525 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 1.26 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 1.96 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 0.5 secs Time to fit model: 0.952 secs Removing infrequent and redundant covariates and normalizing Removing infrequent and redundant covariates covariates and normalizing took 0.465 secs Prediction took 0.348 secs Prediction done in: 1.36 secs Calculating Performance for Test ============= AUC 68.70 95% lower AUC: 62.22 95% upper AUC: 75.18 AUPRC: 30.86 Brier: 0.15 Eavg: 0.04 Calibration in large- Mean predicted risk 0.2139 : observed risk 0.2011 Calibration in large- Intercept -0.0407 Weak calibration intercept: -0.0407 - gradient:1.0401 Hosmer-Lemeshow calibration gradient: 0.64 intercept: 0.12 Average Precision: 0.31 Calculating Performance for Train ============= AUC 72.20 95% lower AUC: 68.57 95% upper AUC: 75.83 AUPRC: 41.60 Brier: 0.14 Eavg: 0.02 Calibration in large- Mean predicted risk 0.2005 : observed risk 0.2005 Calibration in large- Intercept 0.1631 Weak calibration intercept: 0.1631 - gradient:1.1358 Hosmer-Lemeshow calibration gradient: 1.25 intercept: -0.05 Average Precision: 0.42 Calculating Performance for CV ============= AUC 68.65 95% lower AUC: 64.81 95% upper AUC: 72.50 AUPRC: 33.24 Brier: 0.14 Eavg: 0.03 Calibration in large- Mean predicted risk 0.201 : observed risk 0.2005 Calibration in large- Intercept 0.2297 Weak calibration intercept: 0.2297 - gradient:1.1907 Hosmer-Lemeshow calibration gradient: 1.14 intercept: -0.03 Average Precision: 0.33 Time to calculate evaluation metrics: 0.533 secs Run finished successfully. Saving PlpResult Creating directory to save model plpResult saved to ..\D:\temp\2025_07_25_13_05_16_23599\RtmpyANOa4\saveLoc2608071b21faf/lcc/learningCurve_2\plpResult runPlp time taken: 6.81 secs Finished in 13 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 0.962 secs Applying min-max normalization of continuous features to test data Finished min-max normalization of continuous features in 0.478 secs Starting robust normalization of continuous features Finished robust normalization in 1.04 secs Applying robust normalization of continuous features to test data Finished robust normalization in 0.599 secs Starting robust normalization of continuous features Finished robust normalization in 1.01 secs Applying robust normalization of continuous features to test data Finished robust normalization in 0.603 secs Starting min-max normalization of continuous features Finished min-max normalization of continuous features in 0.861 secs Applying min-max normalization of continuous features to test data Finished min-max normalization of continuous features in 0.511 secs Starting robust normalization of continuous features Finished robust normalization in 1.08 secs Applying robust normalization of continuous features to test data Finished robust normalization in 0.573 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_07_25_13_05_16_23599\RtmpyANOa4\file260802e41467f 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 0.261 secs 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 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 123 | PASS 643 ] ══ Skipped tests (123) ═════════════════════════════════════════════════════════ • On CRAN (119): '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-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:33:3', 'test-formatting.R:103:3', 'test-helperfunctions.R:33:3', 'test-imputation.R:93:3', 'test-imputation.R:183:3', '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:129:3', '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: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:51:3', 'test-sklearnClassifier.R:62:3', 'test-sklearnClassifier.R:109:3', 'test-sklearnClassifier.R:125:3', 'test-sklearnClassifier.R:158:3', 'test-sklearnClassifier.R:196:3', 'test-sklearnClassifier.R:233:3', 'test-sklearnClassifier.R:263:3', 'test-sklearnClassifier.R:284:3', '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 123 | PASS 643 ] > > proc.time() user system elapsed 100.87 17.79 108.40