R Under development (unstable) (2024-08-15 r87022 ucrt) -- "Unsuffered Consequences" Copyright (C) 2024 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) > test_check("msm") Loading required package: msm Error in stat(res) : Error Error in stat(res) : Error Error in stat(res) : Error Error in stat(res) : Error Call: msm(formula = state ~ years, subject = PTNUM, data = cav, qmatrix = twoway4.q, deathexact = TRUE, fixedpars = TRUE, method = "BFGS", control = list(trace = 5, REPORT = 1)) -2 * log-likelihood: 4908.817 Call: msm(formula = state ~ years, subject = PTNUM, data = cav, qmatrix = twoway4.q, deathexact = TRUE, fixedpars = TRUE, method = "BFGS", control = list(trace = 5, REPORT = 1)) -2 * log-likelihood: 4908.817 Call: msm(formula = state ~ months, subject = ptnum, data = psor, qmatrix = psor.q, covariates = ~ollwsdrt + hieffusn, constraint = list(hieffusn = c(1, 1, 1), ollwsdrt = c(1, 1, 2)), control = list(fnscale = 1)) Maximum likelihood estimates Baselines are with covariates set to their means Transition intensities with hazard ratios for each covariate Baseline ollwsdrt State 1 - State 1 -0.09594 (-0.1216,-0.0757) State 1 - State 2 0.09594 ( 0.0757, 0.1216) 0.5652 (0.3853,0.829) State 2 - State 2 -0.16431 (-0.2076,-0.1300) State 2 - State 3 0.16431 ( 0.1300, 0.2076) 0.5652 (0.3853,0.829) State 3 - State 3 -0.25438 (-0.3396,-0.1905) State 3 - State 4 0.25438 ( 0.1905, 0.3396) 1.6408 (0.8154,3.302) hieffusn State 1 - State 1 State 1 - State 2 1.646 (1.148,2.359) State 2 - State 2 State 2 - State 3 1.646 (1.148,2.359) State 3 - State 3 State 3 - State 4 1.646 (1.148,2.359) -2 * log-likelihood: 1114.899 Call: msm(formula = state ~ months, subject = ptnum, data = psor, qmatrix = psor.q, covariates = ~ollwsdrt + hieffusn, constraint = list(hieffusn = c(1, 1, 1), ollwsdrt = c(1, 1, 2)), control = list(fnscale = 1)) Maximum likelihood estimates: Transition intensity matrix with covariates set to their means State 1 State 2 State 1 -0.09594 (-0.1216,-0.0757) 0.09594 ( 0.0757, 0.1216) State 2 0 -0.16431 (-0.2076,-0.1300) State 3 0 0 State 4 0 0 State 3 State 4 State 1 0 0 State 2 0.16431 ( 0.1300, 0.2076) 0 State 3 -0.25438 (-0.3396,-0.1905) 0.25438 ( 0.1905, 0.3396) State 4 0 0 Log-linear effects of ollwsdrt State 1 State 2 State 3 State 1 0 -0.5706 (-0.9536,-0.1876) 0 State 2 0 0 -0.5706 (-0.9536,-0.1876) State 3 0 0 0 State 4 0 0 0 State 4 State 1 0 State 2 0 State 3 0.4952 (-0.2041, 1.1944) State 4 0 Log-linear effects of hieffusn State 1 State 2 State 3 State 1 0 0.4983 (0.1383,0.8584) 0 State 2 0 0 0.4983 (0.1383,0.8584) State 3 0 0 0 State 4 0 0 0 State 4 State 1 0 State 2 0 State 3 0.4983 (0.1383,0.8584) State 4 0 -2 * log-likelihood: 1114.899 State 1 State 2 State 1 -0.09594 (-0.1216,-0.0757) 0.09594 ( 0.0757, 0.1216) State 2 0 -0.16431 (-0.2076,-0.1300) State 3 0 0 State 4 0 0 State 3 State 4 State 1 0 0 State 2 0.16431 ( 0.1300, 0.2076) 0 State 3 -0.25438 (-0.3396,-0.1905) 0.25438 ( 0.1905, 0.3396) State 4 0 0 Call: msm(formula = fev ~ days, subject = ptnum, data = fev[1:500, ], qmatrix = three.q, hmodel = hmodel1, hcovariates = list(~acute, ~acute, NULL), hcovinits = list(-8, -8, NULL), hconstraint = list(acute = c(1, 1)), death = 3, center = FALSE) Maximum likelihood estimates Baselines are with covariates set to 0 Transition intensities Baseline State 1 - State 1 -6.381e-04 (-1.276e-03,-3.192e-04) State 1 - State 2 6.373e-04 ( 3.183e-04, 1.276e-03) State 1 - State 3 8.440e-07 ( 3.375e-21, 2.111e+08) State 2 - State 2 -8.299e-04 (-1.749e-03,-3.938e-04) State 2 - State 3 8.299e-04 ( 3.938e-04, 1.749e-03) Hidden Markov model, 3 states State 1 - normal distribution Parameters: Estimate LCL UCL mean 106.234293 103.83878 108.629802 sd 17.066749 15.54712 18.734914 acute -6.993605 -10.10295 -3.884258 State 2 - normal distribution Parameters: Estimate LCL UCL mean 63.790749 61.40091 66.180592 sd 14.166004 12.98525 15.454123 acute -6.993605 -10.10295 -3.884258 State 3 - identity distribution Parameters: Estimate LCL UCL which 999 NA NA -2 * log-likelihood: 4269.772 Hidden Markov model binomial distribution Parameters: size = 40, prob = 0.2 Multivariate hidden Markov model with 2 outcomes: Hidden Markov model binomial distribution Parameters: size = 40, prob = 0.3 Hidden Markov model binomial distribution Parameters: size = 40, prob = 0.3 Call: msm(formula = state ~ years, subject = PTNUM, data = cav, qmatrix = oneway4.q, ematrix = ematrix, deathexact = 4, fixedpars = TRUE) -2 * log-likelihood: 4296.916 Call: msm(formula = state ~ years, subject = PTNUM, data = cav, qmatrix = oneway4.q, ematrix = ematrix, initprobs = c(0.8, 0.1, 0.1, 0), est.initprobs = TRUE, deathexact = 4, fixedpars = 1:9, method = "BFGS", control = list(fnscale = 4000, maxit = 10000)) Maximum likelihood estimates Transition intensities Baseline Well - Well -0.1651 Well - Mild 0.1480 Well - Death 0.0171 Mild - Mild -0.2830 Mild - Severe 0.2020 Mild - Death 0.0810 Severe - Severe -0.1260 (-0.126,-0.126) Severe - Death 0.1260 Misclassification probabilities Baseline Obs Well | Well 0.9 (0.9,0.9) Obs Mild | Well 0.1 Obs Well | Mild 0.1 Obs Mild | Mild 0.8 Obs Severe | Mild 0.1 Obs Mild | Severe 0.1 Obs Severe | Severe 0.9 (0.9,0.9) Initial state occupancy probabilities Estimate LCL UCL State 1 0.9995418040 8.909618e-01 0.99998008 State 2 0.0001849772 4.097924e-07 0.07835928 State 3 0.0002732188 2.500377e-06 0.03221842 State 4 0.0000000000 0.000000e+00 0.00000000 -2 * log-likelihood: 4297.466 Observed numbers of individuals occupying states at each time State 1 State 2 State 3 State 4 Total 0 622 0 0 0 622 1.94602739726027 537 4 5 54 600 3.89205479452054 356 35 24 87 502 5.83808219178081 208 41 28 127 404 7.78410958904108 122 44 27 158 351 9.73013698630135 71 25 22 187 305 11.6761643835616 31 11 13 218 273 13.6221917808219 12 6 5 236 259 15.5682191780822 5 1 3 244 253 17.5142465753424 1 0 2 249 252 19.4602739726027 0 0 0 251 251 Expected numbers of individuals occupying states at each time Well Mild Severe Death Total 0 454.060000 105.74000 62.20000 0.00000 622 1.94602739726027 325.710210 142.31588 85.87563 46.09828 600 3.89205479452054 201.519656 121.76444 94.55263 84.16327 502 5.83808219178081 119.419915 89.03744 88.74180 106.80084 404 7.78410958904108 76.147715 66.14910 82.14786 126.55532 351 9.73013698630135 48.439143 47.43163 71.31882 137.81040 305 11.6761643835616 31.676873 34.25326 60.87352 146.19635 273 13.6221917808219 21.922294 25.83213 53.18927 158.05631 259 15.5682191780822 15.602058 19.85855 46.59544 170.94396 253 17.5142465753424 11.311456 15.46051 40.76362 184.46441 252 19.4602739726027 8.194417 11.98037 35.07316 195.75205 251 Observed prevalences of states (percentages of population at risk) State 1 State 2 State 3 State 4 0 100.0000000 0.0000000 0.0000000 0.00000 1.94602739726027 89.5000000 0.6666667 0.8333333 9.00000 3.89205479452054 70.9163347 6.9721116 4.7808765 17.33068 5.83808219178081 51.4851485 10.1485149 6.9306931 31.43564 7.78410958904108 34.7578348 12.5356125 7.6923077 45.01425 9.73013698630135 23.2786885 8.1967213 7.2131148 61.31148 11.6761643835616 11.3553114 4.0293040 4.7619048 79.85348 13.6221917808219 4.6332046 2.3166023 1.9305019 91.11969 15.5682191780822 1.9762846 0.3952569 1.1857708 96.44269 17.5142465753424 0.3968254 0.0000000 0.7936508 98.80952 19.4602739726027 0.0000000 0.0000000 0.0000000 100.00000 Expected prevalences of states (percentages of population at risk) Well Mild Severe Death 0 73.000000 17.000000 10.00000 0.000000 1.94602739726027 54.285035 23.719313 14.31260 7.683047 3.89205479452054 40.143358 24.255864 18.83519 16.765592 5.83808219178081 29.559385 22.038971 21.96579 26.435852 7.78410958904108 21.694506 18.845898 23.40395 36.055648 9.73013698630135 15.881686 15.551355 23.38322 45.183739 11.6761643835616 11.603250 12.546980 22.29799 53.551777 13.6221917808219 8.464206 9.973795 20.53640 61.025602 15.5682191780822 6.166821 7.849229 18.41717 67.566782 17.5142465753424 4.488673 6.135124 16.17604 73.200163 19.4602739726027 3.264708 4.773057 13.97337 77.988863 Imputing sampling times after deaths... Calculating replicates of test statistics for imputations... [ FAIL 0 | WARN 0 | SKIP 7 | PASS 597 ] ══ Skipped tests (7) ═══════════════════════════════════════════════════════════ • On CRAN (7): 'test_draic.r:25:3', 'test_models.r:780:3', 'test_models_hmm.r:186:3', 'test_pearson.R:24:3', 'test_phase.R:9:3', 'test_simul.R:2:3', 'test_weights.R:42:3' [ FAIL 0 | WARN 0 | SKIP 7 | PASS 597 ] > > proc.time() user system elapsed 71.95 9.54 81.54