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Type 'q()' to quit R. > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/tests.html > # * https://testthat.r-lib.org/reference/test_package.html#special-files > > library(testthat) > library(invivoPKfit) > > test_check("invivoPKfit") stat_model(): stat_model already present; new stat_model will replace the existing one. Old models: model_flat, model_1comp, model_2comp New models: model_flat, model_1comp, model_2comp do_preprocess.pk(): Pre-processing data Species, Route, and Mediawill be coerced to lowercase. 26concentration vs. time observations loaded. Number of unique data groups (unique combinations of Chemical, Species): 1 Estimating 17 missing LOQs as 0.9* minimum non-NA, non-zero Value for each unique combination of Chemical, Species, Reference, Media Estimating missing concentration SDs (for data points with N_Subjects > 1) as minimum non-missing SD for each group of data given by the unique combination of variables in Chemical, Species, Reference, Media. If all SDs are missing in a group, SD for each observation will be imputed as 0. 22 missing SDs will be estimated. Remaining observations:26 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:26 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:26 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:26 Number of unique data groups (unique combinations of Chemical, Species): 1 Applying transformations to concentration variables: dose_norm = FALSE log10_trans = FALSE do_data_info.pk(): Getting data summary info do_data_info.pk(): Getting data summary statistics do_data_info.pk(): Doing dose-normalized non-compartmental analysis nca.pk(): Doing dose-normalized NCA by the following grouping: Chemical, Species, Reference, Route, Media, Dose do_data_info.pk(): Getting data flags based on NCA do_prefit.pk(): Assigning error SD groups to all observations do_prefit.pk():Getting bounds and starting guesses for each error SD to be fitted do_prefit.pk(): Getting bounds and starting guesses for all model parameters to be fitted do_prefit.pk():Checking whether sufficient observations to fit models do_prefit.pk():Checking whether sufficient observations to fit models do_prefit.pk():Checking whether sufficient observations to fit models do_fit.pk(): Begin fitting for model(s):model_flat model_1comp model_2comp do_fit.pk(): Now converting all rate constants to units of 1/hour, in case time has been scaled to units other than hours before fitting do_fit.pk: Fitting complete coef.pk(): Filtering by method(s): bobyqa L-BFGS-B coef.pk(): Filtering by model(s): model_flat model_1comp model_2comp get_tkstats.pk(): Here are the units for the estimated TK statistics: # A tibble: 9 x 2 param_name param_units 1 CLtot L/hours 2 CLtot/Fgutabs L/hours 3 Css mg/L 4 halflife hours 5 tmax hours 6 Cmax mg/L 7 AUC_infinity mg/L * hours 8 Vss L 9 Vss/Fgutabs L Adding missing grouping variables: `Dose` get_tkstats.pk(): Dose column removed because these TK statistics are dose normalized do_preprocess.pk(): Pre-processing data Species, Route, and Mediawill be coerced to lowercase. 16concentration vs. time observations loaded. Number of unique data groups (unique combinations of Chemical, Species): 1 If value <= LOQ, replacing Value with NA.1Values will be replaced with NA. Estimating missing concentration SDs (for data points with N_Subjects > 1) as minimum non-missing SD for each group of data given by the unique combination of variables in Chemical, Species, Reference, Media. If all SDs are missing in a group, SD for each observation will be imputed as 0. 6 missing SDs will be estimated. Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Excluding observations with N_Subjects > 1 where reported Value is NA (because log-likelihood for non-detect multi-subject observations has not been implemented). 1 observations will be excluded. Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Applying transformations to concentration variables: dose_norm = FALSE log10_trans = FALSE do_preprocess.pk(): Pre-processing data do_preprocess.pk(): Pre-processing data Species, Route, and Mediawill be coerced to lowercase. 16concentration vs. time observations loaded. Number of unique data groups (unique combinations of Chemical, Species): 1 If value <= LOQ, replacing Value with NA.1Values will be replaced with NA. Estimating missing concentration SDs (for data points with N_Subjects > 1) as minimum non-missing SD for each group of data given by the unique combination of variables in Chemical, Species, Reference, Media. If all SDs are missing in a group, SD for each observation will be imputed as 0. 6 missing SDs will be estimated. Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Excluding observations with N_Subjects > 1 where reported Value is NA (because log-likelihood for non-detect multi-subject observations has not been implemented). 1 observations will be excluded. Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Applying transformations to concentration variables: dose_norm = FALSE log10_trans = FALSE do_preprocess.pk(): Pre-processing data Species, Route, and Mediawill be coerced to lowercase. 16concentration vs. time observations loaded. Number of unique data groups (unique combinations of Chemical, Species): 1 If value <= LOQ, replacing Value with NA.1Values will be replaced with NA. Estimating missing concentration SDs (for data points with N_Subjects > 1) as minimum non-missing SD for each group of data given by the unique combination of variables in Chemical, Species, Reference, Media. If all SDs are missing in a group, SD for each observation will be imputed as 0. 6 missing SDs will be estimated. Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Excluding observations with N_Subjects > 1 where reported Value is NA (because log-likelihood for non-detect multi-subject observations has not been implemented). 1 observations will be excluded. Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Remaining observations:16 Number of unique data groups (unique combinations of Chemical, Species): 1 Applying transformations to concentration variables: dose_norm = FALSE log10_trans = FALSE [ FAIL 0 | WARN 0 | SKIP 3 | PASS 18 ] ══ Skipped tests (3) ═══════════════════════════════════════════════════════════ • empty test (3): 'test-get_tkstats.R:1:1', 'test-pk.R:1:1', 'test-pkmethods_preprocess_data.R:1:1' [ FAIL 0 | WARN 0 | SKIP 3 | PASS 18 ] > > proc.time() user system elapsed 11.67 0.60 12.21