From 255430279d65bfe92093d48c9a586b062a38303d Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 22 Jun 2021 15:15:02 +0200 Subject: Update development version of online docs --- docs/dev/reference/dimethenamid_2018.html | 288 +++++++++++++++++++++++++++++- 1 file changed, 286 insertions(+), 2 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 a06599df..e255765e 100644 --- a/docs/dev/reference/dimethenamid_2018.html +++ b/docs/dev/reference/dimethenamid_2018.html @@ -77,7 +77,7 @@ constrained by data protection regulations." /> mkin - 1.0.3.9000 + 1.0.5 @@ -203,7 +203,291 @@ specific pieces of information in the comments.

#> Elliot 2 0.75 33.37 23 #> Flaach 0.40 NA 20 #> BBA 2.2 0.40 NA 20 -#> BBA 2.3 0.40 NA 20 +#> BBA 2.3 0.40 NA 20
dmta_ds <- lapply(1:8, function(i) { + ds_i <- dimethenamid_2018$ds[[i]]$data + ds_i[ds_i$name == "DMTAP", "name"] <- "DMTA" + ds_i$time <- ds_i$time * dimethenamid_2018$f_time_norm[i] + ds_i +}) +names(dmta_ds) <- sapply(dimethenamid_2018$ds, function(ds) ds$title) +dmta_ds[["Borstel"]] <- rbind(dmta_ds[["Borstel 1"]], dmta_ds[["Borstel 2"]]) +dmta_ds[["Borstel 1"]] <- NULL +dmta_ds[["Borstel 2"]] <- NULL +dmta_ds[["Elliot"]] <- rbind(dmta_ds[["Elliot 1"]], dmta_ds[["Elliot 2"]]) +dmta_ds[["Elliot 1"]] <- NULL +dmta_ds[["Elliot 2"]] <- NULL +# \dontrun{ +dfop_sfo3_plus <- mkinmod( + DMTA = mkinsub("DFOP", c("M23", "M27", "M31")), + M23 = mkinsub("SFO"), + M27 = mkinsub("SFO"), + M31 = mkinsub("SFO", "M27", sink = FALSE), + quiet = TRUE +) +f_dmta_mkin_tc <- mmkin( + 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 () +#> { +#> ini({ +#> DMTA_0 = 98.7697627680706 +#> eta.DMTA_0 ~ 2.35171765917765 +#> log_k_M23 = -3.92162409637283 +#> eta.log_k_M23 ~ 0.549278519419884 +#> log_k_M27 = -4.33774620773911 +#> eta.log_k_M27 ~ 0.864474956685295 +#> log_k_M31 = -4.24767627688461 +#> eta.log_k_M31 ~ 0.750297149164171 +#> log_k1 = -2.2341008812259 +#> eta.log_k1 ~ 0.902976221565793 +#> log_k2 = -3.7762779983269 +#> eta.log_k2 ~ 1.57684519529298 +#> g_qlogis = 0.450175725479389 +#> eta.g_qlogis ~ 3.0851335687675 +#> f_DMTA_tffm0_1_qlogis = -2.09240906629456 +#> eta.f_DMTA_tffm0_1_qlogis ~ 0.3 +#> f_DMTA_tffm0_2_qlogis = -2.18057573598794 +#> eta.f_DMTA_tffm0_2_qlogis ~ 0.3 +#> f_DMTA_tffm0_3_qlogis = -2.14267187609763 +#> eta.f_DMTA_tffm0_3_qlogis ~ 0.3 +#> sigma_low_DMTA = 0.697933852349996 +#> rsd_high_DMTA = 0.0257724286053519 +#> sigma_low_M23 = 0.697933852349996 +#> rsd_high_M23 = 0.0257724286053519 +#> sigma_low_M27 = 0.697933852349996 +#> rsd_high_M27 = 0.0257724286053519 +#> sigma_low_M31 = 0.697933852349996 +#> rsd_high_M31 = 0.0257724286053519 +#> }) +#> model({ +#> DMTA_0_model = DMTA_0 + eta.DMTA_0 +#> DMTA(0) = DMTA_0_model +#> k_M23 = exp(log_k_M23 + eta.log_k_M23) +#> k_M27 = exp(log_k_M27 + eta.log_k_M27) +#> k_M31 = exp(log_k_M31 + eta.log_k_M31) +#> 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 = 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) * +#> (1 - f_DMTA_tffm0_1) +#> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - +#> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - +#> g) * exp(-k2 * time))) * DMTA +#> d/dt(M23) = +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/dt(M27) = +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/dt(M31) = +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 +#> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA) +#> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23) +#> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27) +#> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31) +#> }) +#> } +#> <environment: 0x555559c2bd78>
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 +#>
#> → calculate sensitivities
#> [====|====|====|====|====|====|====|====|====|====] 0:00:04 +#>
#> → calculate ∂(f)/∂(η)
#> [====|====|====|====|====|====|====|====|====|====] 0:00:01 +#>
#> → calculate ∂(R²)/∂(η)
#> [====|====|====|====|====|====|====|====|====|====] 0:00:08 +#>
#> → finding duplicate expressions in inner model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#>
#> → optimizing duplicate expressions in inner model...
#> [====|====|====|====|====|====|====|====|====|====] 0:00:07 +#>
#> → 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 1.1.0 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
summary(f_dmta_nlmixr_focei) +
#> nlmixr version used for fitting: 2.0.4 +#> mkin version used for pre-fitting: 1.0.5 +#> R version used for fitting: 4.1.0 +#> Date of fit: Thu Jun 17 14:04:58 2021 +#> Date of summary: Thu Jun 17 14:04:58 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: +#> 568 observations of 4 variable(s) grouped in 6 datasets +#> +#> Degradation model predictions using RxODE +#> +#> Fitted in 242.937 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 +#> 98.7698 -3.9216 -4.3377 -4.2477 0.1380 0.1393 +#> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis +#> -1.7571 -2.2341 -3.7763 0.4502 +#> +#> Mean of starting values for error model parameters: +#> sigma_low rsd_high +#> 0.69793 0.02577 +#> +#> Fixed degradation parameter values: +#> None +#> +#> Results: +#> +#> Likelihood calculated by focei +#> AIC BIC logLik +#> 1936 2031 -945.9 +#> +#> Optimised parameters: +#> est. lower upper +#> DMTA_0 98.7698 98.7356 98.8039 +#> log_k_M23 -3.9216 -3.9235 -3.9197 +#> log_k_M27 -4.3377 -4.3398 -4.3357 +#> log_k_M31 -4.2477 -4.2497 -4.2457 +#> log_k1 -2.2341 -2.2353 -2.2329 +#> log_k2 -3.7763 -3.7781 -3.7744 +#> g_qlogis 0.4502 0.4496 0.4507 +#> f_DMTA_tffm0_1_qlogis -2.0924 -2.0936 -2.0912 +#> f_DMTA_tffm0_2_qlogis -2.1806 -2.1818 -2.1794 +#> f_DMTA_tffm0_3_qlogis -2.1427 -2.1439 -2.1415 +#> +#> 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 2.352 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 0.8645 0.0000 +#> eta.log_k_M31 0.000 0.0000 0.0000 0.7503 +#> 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 0.903 0.000 0.000 +#> eta.log_k2 0.000 1.577 0.000 +#> eta.g_qlogis 0.000 0.000 3.085 +#> 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 +#> 0.69793 0.02577 +#> +#> Backtransformed parameters: +#> est. lower upper +#> DMTA_0 98.76976 98.73563 98.80390 +#> k_M23 0.01981 0.01977 0.01985 +#> k_M27 0.01307 0.01304 0.01309 +#> k_M31 0.01430 0.01427 0.01433 +#> f_DMTA_to_M23 0.10984 NA NA +#> f_DMTA_to_M27 0.09036 NA NA +#> f_DMTA_to_M31 0.08399 NA NA +#> k1 0.10709 0.10696 0.10722 +#> k2 0.02291 0.02287 0.02295 +#> g 0.61068 0.61055 0.61081 +#> +#> Resulting formation fractions: +#> ff +#> DMTA_M23 0.10984 +#> DMTA_M27 0.09036 +#> DMTA_M31 0.08399 +#> DMTA_sink 0.71581 +#> +#> Estimated disappearance times: +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> DMTA 10.66 59.78 18 6.473 30.26 +#> M23 34.99 116.24 NA NA NA +#> M27 53.05 176.23 NA NA NA +#> M31 48.48 161.05 NA NA NA
plot(f_dmta_nlmixr_focei) +
# saem has a problem with this model/data combination, maybe because of the +# overparameterised error model, to be investigated +#f_dmta_nlmixr_saem <- nlmixr(f_dmta_mkin_tc, est = "saem", +# control = saemControl(print = 500)) +#summary(f_dmta_nlmixr_saem) +#plot(f_dmta_nlmixr_saem) +# } +