R Under development (unstable) (2025-06-12 r88305 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. > # 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(calibmsm) > > test_check("calibmsm") In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.The method used to assess calibration was BLR-IPCW There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2,3,4,5,6 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A confidence interval was not estimated The estimated data for calibration plots are stored in list element `plotdata`: $state1 id pred obs 2 2 0.1140189 0.1095897 4 4 0.1383878 0.1036308 $state2 id pred obs 2 2 0.2316569 0.1698031 4 4 0.1836189 0.1855591 $state3 id pred obs 2 2 0.08442692 0.1248583 4 4 0.07579429 0.1166606 $state4 id pred obs 2 2 0.2328398 0.2427580 4 4 0.2179331 0.2243106 $state5 id pred obs 2 2 0.1481977 0.1909795 4 4 0.1538475 0.1654523 $state6 id pred obs 2 2 0.1888598 0.2069354 4 4 0.2304185 0.2542212 The estimated mean calibration are stored in list element `mean`: state1 state2 state3 state4 state5 -0.0216273416 -0.0152282576 0.0254839288 0.0097158314 -0.0003011927 state6 0.0032309988 The method used to assess calibration was BLR-IPCW There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2,3,4,5,6 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A confidence interval was not estimated The estimated data for calibration plots are stored in list element `plotdata`: $state1 id pred obs 2 2 0.1140189 0.1245082 4 4 0.1383878 0.1143276 $state2 id pred obs 2 2 0.2316569 0.1693576 4 4 0.1836189 0.2008740 $state3 id pred obs 2 2 0.08442692 0.1390050 4 4 0.07579429 0.1294378 $state4 id pred obs 2 2 0.2328398 0.2097518 4 4 0.2179331 0.1871859 $state5 id pred obs 2 2 0.1481977 0.1838697 4 4 0.1538475 0.1451690 $state6 id pred obs 2 2 0.1888598 0.2048238 4 4 0.2304185 0.2302551 The estimated mean calibration are stored in list element `mean`: state1 state2 state3 state4 state5 -0.0216273416 -0.0152282576 0.0254839288 0.0097158314 -0.0003011927 state6 0.0032309988 In the landmark cohort of individuals uncensored and in state j at time s, states {4,5} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.The method used to assess calibration was MLR-IPCW There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2,3,4,5,6 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A confidence interval was not estimated The estimated data for calibration plots are stored in list element `plotdata`: $state1 id pred obs 1 2 0.1140189 0.03440096 2 4 0.1383878 0.04099915 $state2 id pred obs 1 2 0.2316569 0.1780035 2 4 0.1836189 0.2657628 $state3 id pred obs 1 2 0.08442692 0.09247000 2 4 0.07579429 0.02384399 $state4 id pred obs 1 2 0.2328398 0.1459938 2 4 0.2179331 0.1780838 $state5 id pred obs 1 2 0.1481977 0.2907131 2 4 0.1538475 0.3122518 $state6 id pred obs 1 2 0.1888598 0.2584187 2 4 0.2304185 0.1790584 The estimated mean calibration are stored in list element `mean`: state1 state2 state3 state4 state5 state6 -0.06928883 0.02306952 0.01460542 -0.01347641 0.02781160 0.01727869 In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.The method used to assess calibration was MLR-IPCW There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2,3,4,5,6 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A 95% confidence interval was estimated with bootstrapping with 5 bootstrap replicates The estimated mean calibration are stored in list element `mean`: $state1 mean mean_lower mean_upper -0.06928883 -0.10161300 -0.02561107 $state2 mean mean_lower mean_upper 0.023069517 -0.001774114 0.065526838 $state3 mean mean_lower mean_upper 0.01460542 -0.01117697 0.03288594 $state4 mean mean_lower mean_upper -0.01347641 -0.03353517 -0.01128189 $state5 mean mean_lower mean_upper 0.027811604 0.004683087 0.081454408 $state6 mean mean_lower mean_upper 0.01727869 -0.01638862 0.04585449 In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.The method used to assess calibration was Pseudo-values with Aalen-Johansen estimator There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2,3,4,5,6 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A confidence interval was not estimated The estimated data for calibration plots are stored in list element `plotdata`: $state1 id pred obs pv 1 1 0.1139726 0.0007884881 -0.0034597686 2 2 0.1140189 0.0008356659 0.0004375128 $state2 id pred obs pv 1 1 0.2295006 0.2472482 1.018092475 2 2 0.2316569 0.2648225 0.002282975 $state3 id pred obs pv 1 1 0.08450376 0.3425900 -0.009054339 2 2 0.08442692 0.3414199 -0.028732251 $state4 id pred obs pv 1 1 0.2326861 0.2251249 -0.007747993 2 2 0.2328398 0.2232169 0.014129269 $state5 id pred obs pv 1 1 0.1504855 0.3384060 0.00371276 2 2 0.1481977 0.3174861 1.01908125 $state6 id pred obs pv 1 1 0.1888514 0.1074200 -0.001543135 2 2 0.1888598 0.1074443 -0.007198751 In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.The method used to assess calibration was Pseudo-values with Aalen-Johansen estimator There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A 95% confidence interval was estimated with a parametric approach The estimated data for calibration plots are stored in list element `plotdata`: $state1 id pred obs obs_lower obs_upper pv 1 1 0.1139726 0.0007884881 -0.1029245 0.1045015 -0.0034597686 2 2 0.1140189 0.0008356659 -0.1030055 0.1046769 0.0004375128 $state2 id pred obs obs_lower obs_upper pv 1 1 0.2295006 0.2472482 0.01381089 0.4806856 1.018092475 2 2 0.2316569 0.2648225 0.03932820 0.4903169 0.002282975 In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.List of 2 $ plotdata:List of 6 ..$ state1:'data.frame': 50 obs. of 5 variables: .. ..$ pred : num [1:50] 0.114 0.114 0.114 0.138 0.123 ... .. ..$ obs : num [1:50] 0.000788 0.000836 0.000501 0.055485 0.023953 ... .. ..$ obs_lower: num [1:50] -0.1029 -0.103 -0.1023 -0.0433 -0.1223 ... .. ..$ obs_upper: num [1:50] 0.105 0.105 0.103 0.154 0.17 ... .. ..$ pv : num [1:50] -0.00346 0.000438 -0.005317 0.005581 -0.00346 ... ..$ state2:'data.frame': 50 obs. of 5 variables: .. ..$ pred : num [1:50] 0.23 0.232 0.232 0.184 0.161 ... .. ..$ obs : num [1:50] 0.247 0.265 0.265 0.235 0.26 ... .. ..$ obs_lower: num [1:50] 0.0138 0.0393 0.0404 0.0421 0.0247 ... .. ..$ obs_upper: num [1:50] 0.481 0.49 0.49 0.429 0.495 ... .. ..$ pv : num [1:50] 1.01809 0.00228 -0.01821 0.00921 0.01928 ... ..$ state3:'data.frame': 50 obs. of 5 variables: .. ..$ pred : num [1:50] 0.0845 0.0844 0.0827 0.0758 0.0551 ... .. ..$ obs : num [1:50] 0.3426 0.3414 0.306 0.1044 -0.0086 ... .. ..$ obs_lower: num [1:50] 0.138 0.137 0.107 -0.192 -0.179 ... .. ..$ obs_upper: num [1:50] 0.547 0.545 0.505 0.401 0.161 ... .. ..$ pv : num [1:50] -0.00905 -0.02873 0.92094 -0.01839 -0.00905 ... ..$ state4:'data.frame': 50 obs. of 5 variables: .. ..$ pred : num [1:50] 0.233 0.233 0.233 0.218 0.183 ... .. ..$ obs : num [1:50] 0.225 0.223 0.227 0.395 0.268 ... .. ..$ obs_lower: num [1:50] 0.02097 0.01902 0.02248 0.17467 0.00874 ... .. ..$ obs_upper: num [1:50] 0.429 0.427 0.431 0.616 0.528 ... .. ..$ pv : num [1:50] -0.00775 0.01413 0.01326 -0.02199 -0.00775 ... ..$ state5:'data.frame': 50 obs. of 5 variables: .. ..$ pred : num [1:50] 0.15 0.148 0.15 0.154 0.143 ... .. ..$ obs : num [1:50] 0.338 0.317 0.338 0.333 0.239 ... .. ..$ obs_lower: num [1:50] 0.093 0.0762 0.0929 0.1159 -0.1323 ... .. ..$ obs_upper: num [1:50] 0.584 0.559 0.584 0.55 0.611 ... .. ..$ pv : num [1:50] 0.00371 1.01908 -0.01677 1.03286 1.00275 ... ..$ state6:'data.frame': 50 obs. of 5 variables: .. ..$ pred : num [1:50] 0.189 0.189 0.189 0.23 0.335 ... .. ..$ obs : num [1:50] 0.107 0.107 0.107 0.216 0.144 ... .. ..$ obs_lower: num [1:50] -0.0598 -0.0597 -0.06 0.0467 -0.069 ... .. ..$ obs_upper: num [1:50] 0.275 0.275 0.274 0.386 0.356 ... .. ..$ pv : num [1:50] -0.00154 -0.0072 0.1061 -0.00728 -0.00177 ... $ metadata:List of 10 ..$ valid_transitions : num [1:6] 1 2 3 4 5 6 ..$ assessed_transitions: num [1:6] 1 2 3 4 5 6 ..$ CI : num 95 ..$ CI_type : chr "parametric" ..$ CI_R_boot : NULL ..$ j : num 1 ..$ s : num 0 ..$ t : num 1826 ..$ calib_type : chr "pv" ..$ curve_type : chr "loess" - attr(*, "class")= chr [1:2] "calib_pv" "calib_msm" The method used to assess calibration was Pseudo-values with Aalen-Johansen estimator There were non-zero predicted transition probabilities into states 1,2,3,4,5,6 Calibration curves have been estimated for transitions into states 1,2,3,4,5,6 Calibration was assessed at time 1826 and calibration was assessed in a landmarked cohort of individuals in state j = 1 at time s = 0 A 95% confidence interval was estimated with a parametric approach The estimated data for calibration plots are stored in list element `plotdata`: $state1 pred obs obs_lower obs_upper pv 1 0.1139726 0.0007884881 -0.1029245 0.1045015 -0.0034597686 2 0.1140189 0.0008356659 -0.1030055 0.1046769 0.0004375128 $state2 pred obs obs_lower obs_upper pv 1 0.2295006 0.2472482 0.01381089 0.4806856 1.018092475 2 0.2316569 0.2648225 0.03932820 0.4903169 0.002282975 $state3 pred obs obs_lower obs_upper pv 1 0.08450376 0.3425900 0.1383243 0.5468557 -0.009054339 2 0.08442692 0.3414199 0.1373466 0.5454931 -0.028732251 $state4 pred obs obs_lower obs_upper pv 1 0.2326861 0.2251249 0.02097406 0.4292758 -0.007747993 2 0.2328398 0.2232169 0.01902400 0.4274098 0.014129269 $state5 pred obs obs_lower obs_upper pv 1 0.1504855 0.3384060 0.09295346 0.5838585 0.00371276 2 0.1481977 0.3174861 0.07618096 0.5587912 1.01908125 $state6 pred obs obs_lower obs_upper pv 1 0.1888514 0.1074200 -0.05976468 0.2746047 -0.001543135 2 0.1888598 0.1074443 -0.05973294 0.2746216 -0.007198751 In the landmark cohort of individuals uncensored and in state j at time s, states {4,5} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {1,2,3,4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {4,5,6} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.In the landmark cohort of individuals uncensored and in state j at time s, states {4,5} have less than 30 people in them at the time calibration is being assessed (t). Warnings and errors may occur when the models to estimate the calibration curves are fitted, due to small sample size. The number to flag this warning (30) has been chosen arbitrarily, and does not constitute a sufficient sample size from a statistical point of view.[ FAIL 0 | WARN 0 | SKIP 15 | PASS 214 ] ══ Skipped tests (15) ══════════════════════════════════════════════════════════ • On CRAN (15): 'test-calib_aj.R:106:3', 'test-calib_aj.R:158:3', 'test-calib_blr.R:137:3', 'test-calib_blr.R:267:3', 'test-calib_blr.R:465:13', 'test-calib_blr.R:510:13', 'test-calib_pv.R:10:3', 'test-calib_pv.R:223:3', 'test-calib_pv.R:275:3', 'test-calib_pv.R:328:3', 'test-calib_pv.R:482:3', 'test-calib_pv.R:525:3', 'test-calib_pv.R:556:3', 'test-calib_pv.R:586:3', 'test-calib_pv.R:617:3' [ FAIL 0 | WARN 0 | SKIP 15 | PASS 214 ] > > proc.time() user system elapsed 293.17 3.28 296.43