> fit.nlme.lin <- nlmixr(uif.lin, theo_sd, est="nlme") > fit.nlme.lin nlmixr nlme fit by maximum likelihood (Solved) FOCEi-based goodness of fit metrics: OBJF AIC BIC Log-likelihood 3055.975 3069.975 3090.155 -1527.988 nlme-based goodness of fit metrics: AIC BIC Log-likelihood 372.6492 392.8288 -179.3246 Time (sec; $time): Parameters ($par.fixed): Omega ($omega): Fit Data (object is a modified data.frame): > > fit <- nlmixr(uif.lin, theo_sd, est="nlme") > fit nlmixr nlme fit by maximum likelihood (Solved) FOCEi-based goodness of fit metrics: OBJF AIC BIC Log-likelihood 296.5986 310.5986 330.7782 -148.2993 nlme-based goodness of fit metrics: AIC BIC Log-likelihood 372.6492 392.8288 -179.3246 Time (sec; $time): nlme setup FOCEi Evaulate covariance table elapsed 38.79 7.94 0.73 0 2.77 Parameters ($par.fixed): Parameter Estimate SE CV Untransformed logtcl log typical value Cl (L/hr) -3.21 0.0845 2.6% 0.0403 logtka log typical value Ka (1/hr) 0.445 0.198 44.4% 1.56 logtv log typical value V (L) -0.786 0.0463 5.9% 0.456 add.err add.err 0.692 0.692 (95%CI) logtcl (0.0341, 0.0475) logtka (1.06, 2.30) logtv (0.416, 0.499) add.err Omega ($omega): eta.cl eta.ka eta.v eta.cl 0.06993729 0.0000000 0.00000000 eta.ka 0.00000000 0.4124682 0.00000000 eta.v 0.00000000 0.0000000 0.01810991 Fit Data (object is a modified data.frame): # A tibble: 132 x 15 ID TIME DV IPRED PRED IRES RES IWRES WRES CWRES CPRED CRES 1 1 0 0.740 0 0 0.740 0.740 1.07 2.07e-5 -1.00 4.81e4 -4.81e+4 2 1 0.250 2.84 3.86 2.82 -1.02 0.0218 -1.47 3.15e-2 -1.47 3.86e0 -1.02e+0 3 1 0.570 6.57 6.78 5.05 -0.207 1.52 -0.299 2.19e+0 -0.299 6.78e0 -2.07e-1 # ... with 129 more rows, and 3 more variables: eta.cl , eta.ka , # eta.v > fit.nlme.lin nlmixr nlme fit by maximum likelihood (Solved) FOCEi-based goodness of fit metrics: OBJF AIC BIC Log-likelihood 3055.975 3069.975 3090.155 -1527.988 nlme-based goodness of fit metrics: AIC BIC Log-likelihood 372.6492 392.8288 -179.3246 Time (sec; $time): nlme setup FOCEi Evaulate covariance table elapsed 5.09 5.24 5.54 0 1.46 Parameters ($par.fixed): Parameter Estimate SE CV Untransformed logtcl log typical value Cl (L/hr) -3.21 0.0845 2.6% 0.0403 logtka log typical value Ka (1/hr) 0.445 0.198 44.4% 1.56 logtv log typical value V (L) -0.786 0.0463 5.9% 0.456 add.err add.err 0.692 0.692 (95%CI) logtcl (0.0341, 0.0475) logtka (1.06, 2.30) logtv (0.416, 0.499) add.err Omega ($omega): eta.cl eta.ka eta.v eta.cl 0.06993729 0.0000000 0.00000000 eta.ka 0.00000000 0.4124682 0.00000000 eta.v 0.00000000 0.0000000 0.01810991 Fit Data (object is a modified data.frame): # A tibble: 132 x 15 ID TIME DV IPRED PRED IRES RES IWRES WRES CWRES CPRED CRES 1 1 0 0.740 0 0 0.740 0.740 1.07 2.07e-5 -1.00 4.81e4 -4.81e+4 2 1 0.250 2.84 3.86 2.82 -1.02 0.0218 -1.47 3.15e-2 -1.47 3.86e0 -1.02e+0 3 1 0.570 6.57 6.78 5.05 -0.207 1.52 -0.299 2.19e+0 -0.299 6.78e0 -2.07e-1 # ... with 129 more rows, and 3 more variables: eta.cl , eta.ka , # eta.v >