From ff83d8b2ba623513d92ac90fac4a1b0ec98c2cb5 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 5 Oct 2021 17:33:52 +0200 Subject: Update docs --- docs/dev/reference/dimethenamid_2018.html | 463 +++++++----------------------- 1 file changed, 108 insertions(+), 355 deletions(-) (limited to 'docs/dev/reference/dimethenamid_2018.html') diff --git a/docs/dev/reference/dimethenamid_2018.html b/docs/dev/reference/dimethenamid_2018.html index 919e9363..60c15ade 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -222,7 +222,7 @@ specific pieces of information in the comments.

list("DFOP-SFO3+" = dfop_sfo3_plus), dmta_ds, quiet = TRUE, error_model = "tc") nlmixr_model(f_dmta_mkin_tc) -
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> function () +
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> Warning: number of items to replace is not a multiple of replacement length
#> function () #> { #> ini({ #> DMTA_0 = 98.7132391714013 @@ -263,9 +263,9 @@ specific pieces of information in the comments.

#> k1 = exp(log_k1 + eta.log_k1) #> k2 = exp(log_k2 + eta.log_k2) #> g = expit(g_qlogis + eta.g_qlogis) -#> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) -#> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) -#> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) +#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis) +#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis) +#> f_DMTA_to_M23 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis) #> f_DMTA_to_M23 = f_DMTA_tffm0_1 #> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1) #> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * @@ -289,205 +289,122 @@ specific pieces of information in the comments.

#> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) #> }) #> } -#> <environment: 0x555559e97ac0>
# The focei fit takes about four minutes on my system +#> <environment: 0x555559d89920>
# The focei fit takes about four minutes on my system system.time( f_dmta_nlmixr_focei <- nlmixr(f_dmta_mkin_tc, est = "focei", control = nlmixr::foceiControl(print = 500)) ) -
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 +
#> Warning: number of items to replace is not a multiple of replacement length
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → calculate jacobian
#> [====|====|====|====|====|====|====|====|====|====] 0:00:02 #>
#> → calculate sensitivities
#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 #>
#> → calculate ∂(f)/∂(η)
#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 -#>
#> → calculate ∂(R²)/∂(η)
#> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#>
#> → calculate ∂(R²)/∂(η)
#> [====|====|====|====|====|====|====|====|====|====] 0:00:09 #>
#> → finding duplicate expressions in inner model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 -#>
#> → optimizing duplicate expressions in inner model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#>
#> → optimizing duplicate expressions in inner model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:06 #>
#> → finding duplicate expressions in EBE model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 #>
#> → optimizing duplicate expressions in EBE model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:00 -#>
#> → compiling inner model...
#>
#> ✔ done
#> → finding duplicate expressions in FD model...
#>
#> → optimizing duplicate expressions in FD model...
#>
#> → compiling EBE model...
#>
#> ✔ done
#> → compiling events FD model...
#>
#> ✔ done
#> Needed Covariates:
#> [1] "CMT"
#> RxODE 1.1.1 using 8 threads (see ?getRxThreads) -#> no cache: create with `rxCreateCache()`
#> Key: U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation -#> F: Forward difference gradient approximation -#> C: Central difference gradient approximation -#> M: Mixed forward and central difference gradient approximation -#> Unscaled parameters for Omegas=chol(solve(omega)); -#> Diagonals are transformed, as specified by foceiControl(diagXform=) -#> |-----+---------------+-----------+-----------+-----------+-----------| -#> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 | -#> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis | -#> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high | -#> |.....................| o1 | o2 | o3 | o4 | -#> |.....................| o5 | o6 | o7 | o8 | -#> |.....................| o9 | o10 |...........|...........| -#> calculating covariance matrix -#> done
#> Calculating residuals/tables
#> done
#> Warning: initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))
#> Warning: last objective function was not at minimum, possible problems in optimization
#> Warning: S matrix non-positive definite
#> Warning: using R matrix to calculate covariance
#> Warning: gradient problems with initial estimate and covariance; see $scaleInfo
#> user system elapsed -#> 230.015 8.962 238.957
summary(f_dmta_nlmixr_focei) -
#> nlmixr version used for fitting: 2.0.5 -#> mkin version used for pre-fitting: 1.1.0 -#> R version used for fitting: 4.1.1 -#> Date of fit: Thu Sep 16 14:06:55 2021 -#> Date of summary: Thu Sep 16 14:06:55 2021 -#> -#> Equations: -#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * DMTA -#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * DMTA - k_M23 * M23 -#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 -#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * DMTA - k_M31 * M31 -#> -#> Data: -#> 563 observations of 4 variable(s) grouped in 6 datasets -#> -#> Degradation model predictions using RxODE -#> -#> Fitted in 238.792 s -#> -#> Variance model: Two-component variance function -#> -#> Mean of starting values for individual parameters: -#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 -#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 -#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7554 -2.2352 -3.7758 0.4363 -#> -#> Mean of starting values for error model parameters: -#> sigma_low rsd_high -#> 0.70012 0.02577 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood calculated by focei -#> AIC BIC logLik -#> 1918 2014 -937.2 -#> -#> Optimised parameters: -#> est. lower upper -#> DMTA_0 98.7132 98.6801 98.7464 -#> log_k_M23 -3.9216 -3.9235 -3.9198 -#> log_k_M27 -4.3306 -4.3326 -4.3286 -#> log_k_M31 -4.2442 -4.2461 -4.2422 -#> log_k1 -2.2352 -2.2364 -2.2340 -#> log_k2 -3.7758 -3.7776 -3.7740 -#> g_qlogis 0.4363 0.4358 0.4368 -#> f_DMTA_tffm0_1_qlogis -2.0915 -2.0926 -2.0903 -#> f_DMTA_tffm0_2_qlogis -2.1788 -2.1800 -2.1776 -#> f_DMTA_tffm0_3_qlogis -2.1404 -2.1415 -2.1392 -#> -#> Correlation: -#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs -#> log_k_M23 0 -#> log_k_M27 0 0 -#> log_k_M31 0 0 0 -#> log_k1 0 0 0 0 -#> log_k2 0 0 0 0 0 -#> g_qlogis 0 0 0 0 0 0 -#> f_DMTA_tffm0_1_qlogis 0 0 0 0 0 0 0 -#> f_DMTA_tffm0_2_qlogis 0 0 0 0 0 0 0 -#> f_DMTA_tffm0_3_qlogis 0 0 0 0 0 0 0 -#> f_DMTA_0_1 f_DMTA_0_2 -#> log_k_M23 -#> log_k_M27 -#> log_k_M31 -#> log_k1 -#> log_k2 -#> g_qlogis -#> f_DMTA_tffm0_1_qlogis -#> f_DMTA_tffm0_2_qlogis 0 -#> f_DMTA_tffm0_3_qlogis 0 0 -#> -#> Random effects (omega): -#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 2.327 0.0000 0.0000 0.0000 -#> eta.log_k_M23 0.000 0.5493 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.0000 0.8552 0.0000 -#> eta.log_k_M31 0.000 0.0000 0.0000 0.7457 -#> eta.log_k1 0.000 0.0000 0.0000 0.0000 -#> eta.log_k2 0.000 0.0000 0.0000 0.0000 -#> eta.g_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.0000 0.0000 0.0000 -#> eta.log_k1 eta.log_k2 eta.g_qlogis -#> eta.DMTA_0 0.000 0.000 0.000 -#> eta.log_k_M23 0.000 0.000 0.000 -#> eta.log_k_M27 0.000 0.000 0.000 -#> eta.log_k_M31 0.000 0.000 0.000 -#> eta.log_k1 0.901 0.000 0.000 -#> eta.log_k2 0.000 1.577 0.000 -#> eta.g_qlogis 0.000 0.000 3.102 -#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.000 -#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.000 -#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.000 -#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis -#> eta.DMTA_0 0.0 0.0 -#> eta.log_k_M23 0.0 0.0 -#> eta.log_k_M27 0.0 0.0 -#> eta.log_k_M31 0.0 0.0 -#> eta.log_k1 0.0 0.0 -#> eta.log_k2 0.0 0.0 -#> eta.g_qlogis 0.0 0.0 -#> eta.f_DMTA_tffm0_1_qlogis 0.3 0.0 -#> eta.f_DMTA_tffm0_2_qlogis 0.0 0.3 -#> eta.f_DMTA_tffm0_3_qlogis 0.0 0.0 -#> eta.f_DMTA_tffm0_3_qlogis -#> eta.DMTA_0 0.0 -#> eta.log_k_M23 0.0 -#> eta.log_k_M27 0.0 -#> eta.log_k_M31 0.0 -#> eta.log_k1 0.0 -#> eta.log_k2 0.0 -#> eta.g_qlogis 0.0 -#> eta.f_DMTA_tffm0_1_qlogis 0.0 -#> eta.f_DMTA_tffm0_2_qlogis 0.0 -#> eta.f_DMTA_tffm0_3_qlogis 0.3 -#> -#> Variance model: -#> sigma_low rsd_high -#> 0.70012 0.02577 -#> -#> Backtransformed parameters: -#> est. lower upper -#> DMTA_0 98.71324 98.68012 98.74636 -#> k_M23 0.01981 0.01977 0.01985 -#> k_M27 0.01316 0.01313 0.01319 -#> k_M31 0.01435 0.01432 0.01438 -#> f_DMTA_to_M23 0.10993 NA NA -#> f_DMTA_to_M27 0.09049 NA NA -#> f_DMTA_to_M31 0.08414 NA NA -#> k1 0.10698 0.10685 0.10710 -#> k2 0.02292 0.02288 0.02296 -#> g 0.60738 0.60725 0.60751 -#> -#> Resulting formation fractions: -#> ff -#> DMTA_M23 0.10993 -#> DMTA_M27 0.09049 -#> DMTA_M31 0.08414 -#> DMTA_sink 0.71543 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 10.72 60.1 18.09 6.48 30.24 -#> M23 34.99 116.2 NA NA NA -#> M27 52.67 175.0 NA NA NA -#> M31 48.31 160.5 NA NA NA
plot(f_dmta_nlmixr_focei) -
# Using saemix takes about 18 minutes +#>
#> → compiling inner model...
#>
#> ✔ done
#> → finding duplicate expressions in FD model...
#>
#> → optimizing duplicate expressions in FD model...
#>
#> → compiling EBE model...
#>
#> ✔ done
#> → compiling events FD model...
#>
#> ✔ done
#> Model:
#> cmt(DMTA); +#> cmt(M23); +#> cmt(M27); +#> cmt(M31); +#> rx_expr_14~ETA[1]+THETA[1]; +#> DMTA(0)=rx_expr_14; +#> rx_expr_15~ETA[5]+THETA[5]; +#> rx_expr_16~ETA[7]+THETA[7]; +#> rx_expr_17~ETA[6]+THETA[6]; +#> rx_expr_24~exp(rx_expr_15); +#> rx_expr_25~exp(rx_expr_17); +#> rx_expr_29~t*rx_expr_24; +#> rx_expr_30~t*rx_expr_25; +#> rx_expr_31~exp(-(rx_expr_16)); +#> rx_expr_35~1+rx_expr_31; +#> rx_expr_40~1/(rx_expr_35); +#> rx_expr_42~(rx_expr_40); +#> rx_expr_43~1-rx_expr_42; +#> d/dt(DMTA)=-DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43)); +#> rx_expr_18~ETA[2]+THETA[2]; +#> rx_expr_26~exp(rx_expr_18); +#> d/dt(M23)=-rx_expr_26*M23+DMTA*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_1/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43)); +#> rx_expr_19~ETA[3]+THETA[3]; +#> rx_expr_20~ETA[4]+THETA[4]; +#> rx_expr_21~1-f_DMTA_tffm0_1; +#> rx_expr_27~exp(rx_expr_19); +#> rx_expr_28~exp(rx_expr_20); +#> d/dt(M27)=-rx_expr_27*M27+rx_expr_28*M31+DMTA*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_2/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43)); +#> rx_expr_22~1-f_DMTA_tffm0_2; +#> d/dt(M31)=-rx_expr_28*M31+DMTA*(rx_expr_22)*(rx_expr_21)*(exp(rx_expr_15-rx_expr_29)/(rx_expr_35)+exp(rx_expr_17-rx_expr_30)*(rx_expr_43))*f_DMTA_tffm0_3/(exp(-t*rx_expr_24)/(rx_expr_35)+exp(-t*rx_expr_25)*(rx_expr_43)); +#> rx_expr_0~CMT==4; +#> rx_expr_1~CMT==2; +#> rx_expr_2~CMT==1; +#> rx_expr_3~CMT==3; +#> rx_expr_4~1-(rx_expr_0); +#> rx_expr_5~1-(rx_expr_1); +#> rx_expr_6~1-(rx_expr_3); +#> rx_yj_~(rx_expr_4)*((2*(rx_expr_5)*(rx_expr_2)+2*(rx_expr_1))*(rx_expr_6)+2*(rx_expr_3))+2*(rx_expr_0); +#> rx_expr_7~(rx_expr_1); +#> rx_expr_8~(rx_expr_3); +#> rx_expr_9~(rx_expr_0); +#> rx_expr_13~(rx_expr_5); +#> rx_expr_32~rx_expr_13*(rx_expr_2); +#> rx_lambda_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9; +#> rx_hi_~(rx_expr_4)*((rx_expr_32+rx_expr_7)*(rx_expr_6)+rx_expr_8)+rx_expr_9; +#> rx_low_~0; +#> rx_expr_10~M31*(rx_expr_0); +#> rx_expr_11~M27*(rx_expr_3); +#> rx_expr_12~M23*(rx_expr_1); +#> rx_expr_23~DMTA*(rx_expr_5); +#> rx_expr_36~rx_expr_23*(rx_expr_2); +#> rx_pred_=(rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6))); +#> rx_expr_33~Rx_pow_di(THETA[12],2); +#> rx_expr_34~Rx_pow_di(THETA[11],2); +#> rx_r_=(rx_expr_4)*((rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6)),2)+rx_expr_34)*(rx_expr_3)+((rx_expr_1)*(rx_expr_33*Rx_pow_di(((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)+(rx_expr_33*Rx_pow_di(((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2)),2)+rx_expr_34)*(rx_expr_5)*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_33*Rx_pow_di(((rx_expr_4)*((rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_3)+((rx_expr_1)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))+(rx_expr_5)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))*(rx_expr_2))*(rx_expr_6))+(rx_expr_0)*(rx_expr_10+(rx_expr_4)*(rx_expr_11+(rx_expr_12+rx_expr_36)*(rx_expr_6)))),2)+rx_expr_34); +#> DMTA_0=THETA[1]; +#> log_k_M23=THETA[2]; +#> log_k_M27=THETA[3]; +#> log_k_M31=THETA[4]; +#> log_k1=THETA[5]; +#> log_k2=THETA[6]; +#> g_qlogis=THETA[7]; +#> f_DMTA_tffm0_1_qlogis=THETA[8]; +#> f_DMTA_tffm0_2_qlogis=THETA[9]; +#> f_DMTA_tffm0_3_qlogis=THETA[10]; +#> sigma_low=THETA[11]; +#> rsd_high=THETA[12]; +#> eta.DMTA_0=ETA[1]; +#> eta.log_k_M23=ETA[2]; +#> eta.log_k_M27=ETA[3]; +#> eta.log_k_M31=ETA[4]; +#> eta.log_k1=ETA[5]; +#> eta.log_k2=ETA[6]; +#> eta.g_qlogis=ETA[7]; +#> eta.f_DMTA_tffm0_1_qlogis=ETA[8]; +#> eta.f_DMTA_tffm0_2_qlogis=ETA[9]; +#> eta.f_DMTA_tffm0_3_qlogis=ETA[10]; +#> DMTA_0_model=rx_expr_14; +#> k_M23=rx_expr_26; +#> k_M27=rx_expr_27; +#> k_M31=rx_expr_28; +#> k1=rx_expr_24; +#> k2=rx_expr_25; +#> g=1/(rx_expr_35); +#> f_DMTA_to_M23=1/(1+exp(-(ETA[8]+THETA[8]))); +#> f_DMTA_to_M23=1/(1+exp(-(ETA[9]+THETA[9]))); +#> f_DMTA_to_M23=1/(1+exp(-(ETA[10]+THETA[10]))); +#> f_DMTA_to_M23=f_DMTA_tffm0_1; +#> f_DMTA_to_M27=(rx_expr_21)*f_DMTA_tffm0_2; +#> f_DMTA_to_M31=(rx_expr_22)*(rx_expr_21)*f_DMTA_tffm0_3; +#> tad=tad(); +#> dosenum=dosenum();
#> Needed Covariates:
#> [1] "f_DMTA_tffm0_1" "f_DMTA_tffm0_2" "f_DMTA_tffm0_3" "CMT"
#> Error in (function (data, inits, PKpars, model = NULL, pred = NULL, err = NULL, lower = -Inf, upper = Inf, fixed = NULL, skipCov = NULL, control = foceiControl(), thetaNames = NULL, etaNames = NULL, etaMat = NULL, ..., env = NULL, keep = NULL, drop = NULL) { set.seed(control$seed) .pt <- proc.time() RxODE::.setWarnIdSort(FALSE) on.exit(RxODE::.setWarnIdSort(TRUE)) loadNamespace("n1qn1") if (!RxODE::rxIs(control, "foceiControl")) { control <- do.call(foceiControl, control) } if (is.null(env)) { .ret <- new.env(parent = emptyenv()) } else { .ret <- env } .ret$origData <- data .ret$etaNames <- etaNames .ret$thetaFixed <- fixed .ret$control <- control .ret$control$focei.mu.ref <- integer(0) if (is(model, "RxODE") || is(model, "character")) { .ret$ODEmodel <- TRUE if (class(pred) != "function") { stop("pred must be a function specifying the prediction variables in this model.") } } else { .ret$ODEmodel <- TRUE model <- RxODE::rxGetLin(PKpars) pred <- eval(parse(text = "function(){return(Central);}")) } .square <- function(x) x * x .ret$diagXformInv <- c(sqrt = ".square", log = "exp", identity = "identity")[control$diagXform] if (is.null(err)) { err <- eval(parse(text = paste0("function(){err", paste(inits$ERROR[[1]], collapse = ""), "}"))) } .covNames <- .parNames <- c() .ret$adjLik <- control$adjLik .mixed <- !is.null(inits$OMGA) && length(inits$OMGA) > 0 if (!exists("noLik", envir = .ret)) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ssAtol <- rep(control$ssAtol, length(RxODE::rxModelVars(model)$state)) .ssRtol <- rep(control$ssRtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = (control$derivMethod == 2L), pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, interaction = (control$interaction == 1L), only.numeric = !.mixed, run.internal = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol .ssAtol <- c(.ssAtol, rep(control$ssAtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssAtol))) .ssRtol <- c(.ssRtol, rep(control$ssRtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.ssRtol))) .ret$control$rxControl$ssAtol <- .ssAtol .ret$control$rxControl$ssRtol <- .ssRtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { if (.ret$noLik) { .atol <- rep(control$atol, length(RxODE::rxModelVars(model)$state)) .rtol <- rep(control$rtol, length(RxODE::rxModelVars(model)$state)) .ret$model <- RxODE::rxSymPySetupPred(model, pred, PKpars, err, grad = FALSE, pred.minus.dv = TRUE, sum.prod = control$sumProd, theta.derivs = FALSE, optExpression = control$optExpression, run.internal = TRUE, only.numeric = TRUE, addProp = control$addProp) if (!is.null(.ret$model$inner)) { .atol <- c(.atol, rep(control$atolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.atol))) .rtol <- c(.rtol, rep(control$rtolSens, length(RxODE::rxModelVars(.ret$model$inner)$state) - length(.rtol))) .ret$control$rxControl$atol <- .atol .ret$control$rxControl$rtol <- .rtol } .covNames <- .parNames <- RxODE::rxParams(.ret$model$pred.only) .covNames <- .covNames[regexpr(rex::rex(start, or("THETA", "ETA"), "[", numbers, "]", end), .covNames) == -1] colnames(data) <- sapply(names(data), function(x) { if (any(x == .covNames)) { return(x) } else { return(toupper(x)) } }) .lhs <- c(names(RxODE::rxInits(.ret$model$pred.only)), RxODE::rxLhs(.ret$model$pred.only)) if (length(.lhs) > 0) { .covNames <- .covNames[regexpr(rex::rex(start, or(.lhs), end), .covNames) == -1] } if (length(.covNames) > 0) { if (!all(.covNames %in% names(data))) { message("Model:") RxODE::rxCat(.ret$model$pred.only) message("Needed Covariates:") nlmixrPrint(.covNames) stop("Not all the covariates are in the dataset.") } message("Needed Covariates:") print(.covNames) } .extraPars <- .ret$model$extra.pars } else { .extraPars <- NULL } } .ret$skipCov <- skipCov if (is.null(skipCov)) { if (is.null(fixed)) { .tmp <- rep(FALSE, length(inits$THTA)) } else { if (length(fixed) < length(inits$THTA)) { .tmp <- c(fixed, rep(FALSE, length(inits$THTA) - length(fixed))) } else { .tmp <- fixed[1:length(inits$THTA)] } } if (exists("uif", envir = .ret)) { .uifErr <- .ret$uif$ini$err[!is.na(.ret$uif$ini$ntheta)] .uifErr <- sapply(.uifErr, function(x) { if (is.na(x)) { return(FALSE) } return(!any(x == c("pow2", "tbs", "tbsYj"))) }) .tmp <- (.tmp | .uifErr) } .ret$skipCov <- c(.tmp, rep(TRUE, length(.extraPars))) .ret$control$focei.mu.ref <- .ret$uif$focei.mu.ref } if (is.null(.extraPars)) { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA))) } else { .nms <- c(sprintf("THETA[%s]", seq_along(inits$THTA)), sprintf("ERR[%s]", seq_along(.extraPars))) } if (!is.null(thetaNames) && (length(inits$THTA) + length(.extraPars)) == length(thetaNames)) { .nms <- thetaNames } .ret$thetaNames <- .nms .thetaReset$thetaNames <- .nms if (length(lower) == 1) { lower <- rep(lower, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { print(inits$THTA) print(lower) stop("Lower must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (length(upper) == 1) { upper <- rep(upper, length(inits$THTA)) } else if (length(lower) != length(inits$THTA)) { stop("Upper must be a single constant for all the THETA lower bounds, or match the dimension of THETA.") } if (!is.null(.extraPars)) { .ret$model$extra.pars <- eval(call(control$diagXform, .ret$model$extra.pars)) if (length(.ret$model$extra.pars) > 0) { inits$THTA <- c(inits$THTA, .ret$model$extra.pars) .lowerErr <- rep(control$atol[1] * 10, length(.ret$model$extra.pars)) .upperErr <- rep(Inf, length(.ret$model$extra.pars)) lower <- c(lower, .lowerErr) upper <- c(upper, .upperErr) } } if (is.null(data$ID)) stop("\"ID\" not found in data") if (is.null(data$DV)) stop("\"DV\" not found in data") if (is.null(data$EVID)) data$EVID <- 0 if (is.null(data$AMT)) data$AMT <- 0 for (.v in c("TIME", "AMT", "DV", .covNames)) { data[[.v]] <- as.double(data[[.v]]) } .ret$dataSav <- data .ds <- data[data$EVID != 0 & data$EVID != 2, c("ID", "TIME", "AMT", "EVID", .covNames)] .w <- which(tolower(names(data)) == "limit") .limitName <- NULL if (length(.w) == 1L) { .limitName <- names(data)[.w] } .censName <- NULL .w <- which(tolower(names(data)) == "cens") if (length(.w) == 1L) { .censName <- names(data[.w]) } data <- data[data$EVID == 0 | data$EVID == 2, c("ID", "TIME", "DV", "EVID", .covNames, .limitName, .censName)] .w <- which(!(names(.ret$dataSav) %in% c(.covNames, keep))) names(.ret$dataSav)[.w] <- tolower(names(.ret$dataSav[.w])) if (.mixed) { .lh <- .parseOM(inits$OMGA) .nlh <- sapply(.lh, length) .osplt <- rep(1:length(.lh), .nlh) .lini <- list(inits$THTA, unlist(.lh)) .nlini <- sapply(.lini, length) .nsplt <- rep(1:length(.lini), .nlini) .om0 <- .genOM(.lh) if (length(etaNames) == dim(.om0)[1]) { .ret$etaNames <- .ret$etaNames } else { .ret$etaNames <- sprintf("ETA[%d]", seq(1, dim(.om0)[1])) } .ret$rxInv <- RxODE::rxSymInvCholCreate(mat = .om0, diag.xform = control$diagXform) .ret$xType <- .ret$rxInv$xType .om0a <- .om0 .om0a <- .om0a/control$diagOmegaBoundLower .om0b <- .om0 .om0b <- .om0b * control$diagOmegaBoundUpper .om0a <- RxODE::rxSymInvCholCreate(mat = .om0a, diag.xform = control$diagXform) .om0b <- RxODE::rxSymInvCholCreate(mat = .om0b, diag.xform = control$diagXform) .omdf <- data.frame(a = .om0a$theta, m = .ret$rxInv$theta, b = .om0b$theta, diag = .om0a$theta.diag) .omdf$lower <- with(.omdf, ifelse(a > b, b, a)) .omdf$lower <- with(.omdf, ifelse(lower == m, -Inf, lower)) .omdf$lower <- with(.omdf, ifelse(!diag, -Inf, lower)) .omdf$upper <- with(.omdf, ifelse(a < b, b, a)) .omdf$upper <- with(.omdf, ifelse(upper == m, Inf, upper)) .omdf$upper <- with(.omdf, ifelse(!diag, Inf, upper)) .ret$control$nomega <- length(.omdf$lower) .ret$control$neta <- sum(.omdf$diag) .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) lower <- c(lower, .omdf$lower) upper <- c(upper, .omdf$upper) } else { .ret$control$nomega <- 0 .ret$control$neta <- 0 .ret$xType <- -1 .ret$control$ntheta <- length(lower) .ret$control$nfixed <- sum(fixed) } .ret$lower <- lower .ret$upper <- upper .ret$thetaIni <- inits$THTA .scaleC <- double(length(lower)) if (is.null(control$scaleC)) { .scaleC <- rep(NA_real_, length(lower)) } else { .scaleC <- as.double(control$scaleC) if (length(lower) > length(.scaleC)) { .scaleC <- c(.scaleC, rep(NA_real_, length(lower) - length(.scaleC))) } else if (length(lower) < length(.scaleC)) { .scaleC <- .scaleC[seq(1, length(lower))] warning("scaleC control option has more options than estimated population parameters, please check.") } } .ret$scaleC <- .scaleC if (exists("uif", envir = .ret)) { .ini <- as.data.frame(.ret$uif$ini)[!is.na(.ret$uif$ini$err), c("est", "err", "ntheta")] for (.i in seq_along(.ini$err)) { if (is.na(.ret$scaleC[.ini$ntheta[.i]])) { if (any(.ini$err[.i] == c("boxCox", "yeoJohnson", "pow2", "tbs", "tbsYj"))) { .ret$scaleC[.ini$ntheta[.i]] <- 1 } else if (any(.ini$err[.i] == c("prop", "add", "norm", "dnorm", "logn", "dlogn", "lnorm", "dlnorm"))) { .ret$scaleC[.ini$ntheta[.i]] <- 0.5 * abs(.ini$est[.i]) } } } for (.i in .ini$model$extraProps$powTheta) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- 1 } .ini <- as.data.frame(.ret$uif$ini) for (.i in .ini$model$extraProps$factorial) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i] + 1)) } for (.i in .ini$model$extraProps$gamma) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- abs(1/digamma(.ini$est[.i])) } for (.i in .ini$model$extraProps$log) { if (is.na(.ret$scaleC[.i])) .ret$scaleC[.i] <- log(abs(.ini$est[.i])) * abs(.ini$est[.i]) } for (.i in .ret$logitThetas) { .b <- .ret$logitThetasLow[.i] .c <- .ret$logitThetasHi[.i] .a <- .ini$est[.i] if (is.na(.ret$scaleC[.i])) { .ret$scaleC[.i] <- 1 * (-.b + .c) * exp(-.a)/((1 + exp(-.a))^2 * (.b + 1 * (-.b + .c)/(1 + exp(-.a)))) } } } names(.ret$thetaIni) <- sprintf("THETA[%d]", seq_along(.ret$thetaIni)) if (is.null(etaMat) & !is.null(control$etaMat)) { .ret$etaMat <- control$etaMat } else { .ret$etaMat <- etaMat } .ret$setupTime <- (proc.time() - .pt)["elapsed"] if (exists("uif", envir = .ret)) { .tmp <- .ret$uif$logThetasList .ret$logThetas <- .tmp[[1]] .ret$logThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasList .ret$logitThetas <- .tmp[[1]] .ret$logitThetasF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListLow .ret$logitThetasLow <- .tmp[[1]] .ret$logitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$logitThetasListHi .ret$logitThetasHi <- .tmp[[1]] .ret$logitThetasHiF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasList .ret$probitThetas <- .tmp[[1]] .ret$probitThetasF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListLow .ret$probitThetasLow <- .tmp[[1]] .ret$probitThetasLowF <- .tmp[[2]] .tmp <- .ret$uif$probitThetasListHi .ret$probitThetasHi <- .tmp[[1]] .ret$probitThetasHiF <- .tmp[[2]] } else { .ret$logThetasF <- integer(0) .ret$logitThetasF <- integer(0) .ret$logitThetasHiF <- numeric(0) .ret$logitThetasLowF <- numeric(0) .ret$logitThetas <- integer(0) .ret$logitThetasHi <- numeric(0) .ret$logitThetasLow <- numeric(0) .ret$probitThetasF <- integer(0) .ret$probitThetasHiF <- numeric(0) .ret$probitThetasLowF <- numeric(0) .ret$probitThetas <- integer(0) .ret$probitThetasHi <- numeric(0) .ret$probitThetasLow <- numeric(0) } if (exists("noLik", envir = .ret)) { if (!.ret$noLik) { .ret$.params <- c(sprintf("THETA[%d]", seq_along(.ret$thetaIni)), sprintf("ETA[%d]", seq(1, dim(.om0)[1]))) .ret$.thetan <- length(.ret$thetaIni) .ret$nobs <- sum(data$EVID == 0) } } .ret$control$printTop <- TRUE .ret$control$nF <- 0 .est0 <- .ret$thetaIni if (!is.null(.ret$model$pred.nolhs)) { .ret$control$predNeq <- length(.ret$model$pred.nolhs$state) } else { .ret$control$predNeq <- 0L } .fitFun <- function(.ret) { this.env <- environment() assign("err", "theta reset", this.env) while (this.env$err == "theta reset") { assign("err", "", this.env) .ret0 <- tryCatch({ foceiFitCpp_(.ret) }, error = function(e) { if (regexpr("theta reset", e$message) != -1) { assign("zeroOuter", FALSE, this.env) assign("zeroGrad", FALSE, this.env) if (regexpr("theta reset0", e$message) != -1) { assign("zeroGrad", TRUE, this.env) } else if (regexpr("theta resetZ", e$message) != -1) { assign("zeroOuter", TRUE, this.env) } assign("err", "theta reset", this.env) } else { assign("err", e$message, this.env) } }) if (this.env$err == "theta reset") { .nm <- names(.ret$thetaIni) .ret$thetaIni <- setNames(.thetaReset$thetaIni + 0, .nm) .ret$rxInv$theta <- .thetaReset$omegaTheta .ret$control$printTop <- FALSE .ret$etaMat <- .thetaReset$etaMat .ret$control$etaMat <- .thetaReset$etaMat .ret$control$maxInnerIterations <- .thetaReset$maxInnerIterations .ret$control$nF <- .thetaReset$nF .ret$control$gillRetC <- .thetaReset$gillRetC .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillRet <- .thetaReset$gillRet .ret$control$gillDf <- .thetaReset$gillDf .ret$control$gillDf2 <- .thetaReset$gillDf2 .ret$control$gillErr <- .thetaReset$gillErr .ret$control$rEps <- .thetaReset$rEps .ret$control$aEps <- .thetaReset$aEps .ret$control$rEpsC <- .thetaReset$rEpsC .ret$control$aEpsC <- .thetaReset$aEpsC .ret$control$c1 <- .thetaReset$c1 .ret$control$c2 <- .thetaReset$c2 if (this.env$zeroOuter) { message("Posthoc reset") .ret$control$maxOuterIterations <- 0L } else if (this.env$zeroGrad) { message("Theta reset (zero gradient values); Switch to bobyqa") RxODE::rxReq("minqa") .ret$control$outerOptFun <- .bobyqa .ret$control$outerOpt <- -1L } else { message("Theta reset (ETA drift)") } } } if (this.env$err != "") { stop(this.env$err) } else { return(.ret0) } } .ret0 <- try(.fitFun(.ret)) .n <- 1 while (inherits(.ret0, "try-error") && control$maxOuterIterations != 0 && .n <= control$nRetries) { message(sprintf("Restart %s", .n)) .ret$control$nF <- 0 .estNew <- .est0 + 0.2 * .n * abs(.est0) * stats::runif(length(.est0)) - 0.1 * .n .estNew <- sapply(seq_along(.est0), function(.i) { if (.ret$thetaFixed[.i]) { return(.est0[.i]) } else if (.estNew[.i] < lower[.i]) { return(lower + (.Machine$double.eps)^(1/7)) } else if (.estNew[.i] > upper[.i]) { return(upper - (.Machine$double.eps)^(1/7)) } else { return(.estNew[.i]) } }) .ret$thetaIni <- .estNew .ret0 <- try(.fitFun(.ret)) .n <- .n + 1 } if (inherits(.ret0, "try-error")) stop("Could not fit data.") .ret <- .ret0 if (exists("parHistData", .ret)) { .tmp <- .ret$parHistData .tmp <- .tmp[.tmp$type == "Unscaled", names(.tmp) != "type"] .iter <- .tmp$iter .tmp <- .tmp[, names(.tmp) != "iter"] .ret$parHistStacked <- data.frame(stack(.tmp), iter = .iter) names(.ret$parHistStacked) <- c("val", "par", "iter") .ret$parHist <- data.frame(iter = .iter, .tmp) } if (.mixed) { .etas <- .ret$ranef .thetas <- .ret$fixef .pars <- .Call(`_nlmixr_nlmixrParameters`, .thetas, .etas) .ret$shrink <- .Call(`_nlmixr_calcShrinkOnly`, .ret$omega, .pars$eta.lst, length(.etas$ID)) .updateParFixed(.ret) } else { .updateParFixed(.ret) } if (!exists("table", .ret)) { .ret$table <- tableControl() } if (control$calcTables) { .ret <- addTable(.ret, updateObject = "no", keep = keep, drop = drop, table = .ret$table) } .ret})(data = dat, inits = .FoceiInits, PKpars = .pars, model = .mod, pred = function() { return(nlmixr_pred) }, err = uif$error, lower = uif$focei.lower, upper = uif$focei.upper, fixed = uif$focei.fixed, thetaNames = uif$focei.names, etaNames = uif$eta.names, control = control, env = env, keep = .keep, drop = .drop): Not all the covariates are in the dataset.
#> Timing stopped at: 121.4 8.294 129.7
#> Timing stopped at: 121.5 8.294 129.9
summary(f_dmta_nlmixr_focei) +
#> Error in summary(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found
plot(f_dmta_nlmixr_focei) +
#> Error in plot(f_dmta_nlmixr_focei): object 'f_dmta_nlmixr_focei' not found
# Using saemix takes about 18 minutes system.time( f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE) )
#> Running main SAEM algorithm -#> [1] "Thu Sep 16 14:06:56 2021" +#> [1] "Tue Oct 5 16:58:50 2021" #> .... #> Minimisation finished -#> [1] "Thu Sep 16 14:25:28 2021"
#> user system elapsed -#> 1176.278 0.021 1176.388
+#> [1] "Tue Oct 5 17:17:24 2021"
#> user system elapsed +#> 1181.365 0.031 1181.470
# nlmixr with est = "saem" is pretty fast with default iteration numbers, most # of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end # The likelihood calculated for the nlmixr fit is much lower than that found by saemix @@ -498,174 +415,10 @@ specific pieces of information in the comments.

f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)) ) -
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>
#> → generate SAEM model
#> ✔ done
#> 1: 98.3400 -3.5096 -3.3392 -3.7596 -2.2055 -2.7755 1.0281 -2.7872 -2.7223 -2.8341 2.6422 0.7027 0.8124 0.7085 0.8560 1.4980 3.2777 0.3063 0.2850 0.2850 4.1120 0.3716 4.4582 0.3994 4.4820 0.4025 3.7803 0.5780 -#> 500: 97.8212 -4.4030 -4.0872 -4.1289 -2.8278 -4.3505 2.6614 -2.1252 -2.1308 -2.0749 2.9463 1.2933 0.2802 0.3467 0.4814 0.7877 3.0743 0.1508 0.1523 0.3155 0.9557 0.0333 0.4787 0.1073 0.6826 0.0707 0.7849 0.0356
#> Calculating covariance matrix
#>
#> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
#>
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → compiling EBE model...
#>
#> ✔ done
#> Needed Covariates:
#> [1] "CMT"
#> Calculating residuals/tables
#> done
#> user system elapsed -#> 800.784 3.715 149.687
traceplot(f_dmta_nlmixr_saem$nm) +
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> Warning: number of items to replace is not a multiple of replacement length
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> Error in eval(substitute(expr), data, enclos = parent.frame()): Cannot run SAEM since some of the parameters are not mu-referenced (eta.f_DMTA_tffm0_1_qlogis, eta.f_DMTA_tffm0_2_qlogis, eta.f_DMTA_tffm0_3_qlogis)
#> Timing stopped at: 0.849 0.016 0.864
#> Timing stopped at: 1.041 0.016 1.058
traceplot(f_dmta_nlmixr_saem$nm)
#> Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"
summary(f_dmta_nlmixr_saem) -
#> nlmixr version used for fitting: 2.0.5 -#> mkin version used for pre-fitting: 1.1.0 -#> R version used for fitting: 4.1.1 -#> Date of fit: Thu Sep 16 14:29:02 2021 -#> Date of summary: Thu Sep 16 14:29:02 2021 -#> -#> Equations: -#> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -#> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) -#> * DMTA -#> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * DMTA - k_M23 * M23 -#> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31 -#> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g) -#> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * -#> exp(-k2 * time))) * DMTA - k_M31 * M31 -#> -#> Data: -#> 563 observations of 4 variable(s) grouped in 6 datasets -#> -#> Degradation model predictions using RxODE -#> -#> Fitted in 149.421 s -#> -#> Variance model: Two-component variance function -#> -#> Mean of starting values for individual parameters: -#> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 -#> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 -#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis -#> -1.7554 -2.2352 -3.7758 0.4363 -#> -#> Mean of starting values for error model parameters: -#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.70012 0.02577 0.70012 0.02577 0.70012 -#> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.02577 0.70012 0.02577 -#> -#> Fixed degradation parameter values: -#> None -#> -#> Results: -#> -#> Likelihood calculated by focei -#> AIC BIC logLik -#> 1953 2074 -948.3 -#> -#> Optimised parameters: -#> est. lower upper -#> DMTA_0 97.821 95.862 99.780 -#> log_k_M23 -4.403 -5.376 -3.430 -#> log_k_M27 -4.087 -4.545 -3.629 -#> log_k_M31 -4.129 -4.639 -3.618 -#> log_k1 -2.828 -3.389 -2.266 -#> log_k2 -4.351 -5.472 -3.229 -#> g_qlogis 2.661 0.824 4.499 -#> f_DMTA_tffm0_1_qlogis -2.125 -2.449 -1.801 -#> f_DMTA_tffm0_2_qlogis -2.131 -2.468 -1.794 -#> f_DMTA_tffm0_3_qlogis -2.075 -2.540 -1.610 -#> -#> Correlation: -#> DMTA_0 l__M23 l__M27 l__M31 log_k1 log_k2 g_qlgs -#> log_k_M23 -0.019 -#> log_k_M27 -0.028 0.004 -#> log_k_M31 -0.019 0.003 0.075 -#> log_k1 0.038 -0.004 -0.006 -0.003 -#> log_k2 0.046 0.011 0.008 0.009 0.068 -#> g_qlogis -0.067 0.004 0.006 0.001 -0.076 -0.409 -#> f_DMTA_tffm0_1_qlogis -0.062 0.055 0.006 0.004 -0.008 -0.004 0.012 -#> f_DMTA_tffm0_2_qlogis -0.062 0.010 0.058 -0.034 -0.008 -0.007 0.014 -#> f_DMTA_tffm0_3_qlogis -0.052 0.009 0.056 0.071 -0.006 -0.001 0.008 -#> f_DMTA_0_1 f_DMTA_0_2 -#> log_k_M23 -#> log_k_M27 -#> log_k_M31 -#> log_k1 -#> log_k2 -#> g_qlogis -#> f_DMTA_tffm0_1_qlogis -#> f_DMTA_tffm0_2_qlogis 0.017 -#> f_DMTA_tffm0_3_qlogis 0.014 -0.005 -#> -#> Random effects (omega): -#> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31 -#> eta.DMTA_0 2.946 0.000 0.0000 0.0000 -#> eta.log_k_M23 0.000 1.293 0.0000 0.0000 -#> eta.log_k_M27 0.000 0.000 0.2802 0.0000 -#> eta.log_k_M31 0.000 0.000 0.0000 0.3467 -#> eta.log_k1 0.000 0.000 0.0000 0.0000 -#> eta.log_k2 0.000 0.000 0.0000 0.0000 -#> eta.g_qlogis 0.000 0.000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000 -#> eta.log_k1 eta.log_k2 eta.g_qlogis -#> eta.DMTA_0 0.0000 0.0000 0.000 -#> eta.log_k_M23 0.0000 0.0000 0.000 -#> eta.log_k_M27 0.0000 0.0000 0.000 -#> eta.log_k_M31 0.0000 0.0000 0.000 -#> eta.log_k1 0.4814 0.0000 0.000 -#> eta.log_k2 0.0000 0.7877 0.000 -#> eta.g_qlogis 0.0000 0.0000 3.074 -#> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.0000 0.000 -#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.0000 0.000 -#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 0.000 -#> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis -#> eta.DMTA_0 0.0000 0.0000 -#> eta.log_k_M23 0.0000 0.0000 -#> eta.log_k_M27 0.0000 0.0000 -#> eta.log_k_M31 0.0000 0.0000 -#> eta.log_k1 0.0000 0.0000 -#> eta.log_k2 0.0000 0.0000 -#> eta.g_qlogis 0.0000 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.1508 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1523 -#> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis -#> eta.DMTA_0 0.0000 -#> eta.log_k_M23 0.0000 -#> eta.log_k_M27 0.0000 -#> eta.log_k_M31 0.0000 -#> eta.log_k1 0.0000 -#> eta.log_k2 0.0000 -#> eta.g_qlogis 0.0000 -#> eta.f_DMTA_tffm0_1_qlogis 0.0000 -#> eta.f_DMTA_tffm0_2_qlogis 0.0000 -#> eta.f_DMTA_tffm0_3_qlogis 0.3155 -#> -#> Variance model: -#> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 -#> 0.95572 0.03325 0.47871 0.10733 0.68264 -#> rsd_high_M27 sigma_low_M31 rsd_high_M31 -#> 0.07072 0.78486 0.03557 -#> -#> Backtransformed parameters: -#> est. lower upper -#> DMTA_0 97.82122 95.862233 99.78020 -#> k_M23 0.01224 0.004625 0.03239 -#> k_M27 0.01679 0.010615 0.02654 -#> k_M31 0.01610 0.009664 0.02683 -#> f_DMTA_to_M23 0.10668 NA NA -#> f_DMTA_to_M27 0.09481 NA NA -#> f_DMTA_to_M31 0.08908 NA NA -#> k1 0.05914 0.033731 0.10370 -#> k2 0.01290 0.004204 0.03958 -#> g 0.93471 0.695081 0.98900 -#> -#> Resulting formation fractions: -#> ff -#> DMTA_M23 0.10668 -#> DMTA_M27 0.09481 -#> DMTA_M31 0.08908 -#> DMTA_sink 0.70943 -#> -#> Estimated disappearance times: -#> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> DMTA 12.57 45.43 13.67 11.72 53.73 -#> M23 56.63 188.11 NA NA NA -#> M27 41.29 137.18 NA NA NA -#> M31 43.05 143.01 NA NA NA
plot(f_dmta_nlmixr_saem) -
# } +
#> Error in summary(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found
plot(f_dmta_nlmixr_saem) +
#> Error in plot(f_dmta_nlmixr_saem): object 'f_dmta_nlmixr_saem' not found
# }