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Diffstat (limited to 'docs/reference/dimethenamid_2018.html')
| -rw-r--r-- | docs/reference/dimethenamid_2018.html | 518 |
1 files changed, 15 insertions, 503 deletions
diff --git a/docs/reference/dimethenamid_2018.html b/docs/reference/dimethenamid_2018.html index 374d2287..f0bc23ee 100644 --- a/docs/reference/dimethenamid_2018.html +++ b/docs/reference/dimethenamid_2018.html @@ -31,7 +31,7 @@ constrained by data protection regulations."><!-- mathjax --><script src="https: <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> - <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false"> + <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false"> Articles <span class="caret"></span> @@ -166,321 +166,19 @@ specific pieces of information in the comments.</p> <span class="r-in"><span class="va">f_dmta_mkin_tc</span> <span class="op"><-</span> <span class="fu"><a href="mmkin.html">mmkin</a></span><span class="op">(</span></span> <span class="r-in"> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span><span class="st">"DFOP-SFO3+"</span> <span class="op">=</span> <span class="va">dfop_sfo3_plus</span><span class="op">)</span>,</span> <span class="r-in"> <span class="va">dmta_ds</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span>, error_model <span class="op">=</span> <span class="st">"tc"</span><span class="op">)</span></span> -<span class="r-in"><span class="fu"><a href="nlmixr.mmkin.html">nlmixr_model</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> 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)</span> -<span class="r-out co"><span class="r-pr">#></span> function () </span> -<span class="r-out co"><span class="r-pr">#></span> {</span> -<span class="r-out co"><span class="r-pr">#></span> ini({</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 = 99</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 ~ 2.3</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 = -3.9</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 ~ 0.55</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 = -4.3</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 ~ 0.86</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 = -4.2</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 ~ 0.75</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 = -2.2</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 ~ 0.9</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 = -3.8</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 ~ 1.6</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis = 0.44</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis ~ 3.1</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis = -2.1</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis ~ 0.3</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis = -2.2</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis ~ 0.3</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis = -2.1</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis ~ 0.3</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_DMTA = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_M23 = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M23 = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_M27 = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_M31 = 0.7</span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M31 = 0.026</span> -<span class="r-out co"><span class="r-pr">#></span> })</span> -<span class="r-out co"><span class="r-pr">#></span> model({</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0_model = DMTA_0 + eta.DMTA_0</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA(0) = DMTA_0_model</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 = exp(log_k_M23 + eta.log_k_M23)</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 = exp(log_k_M27 + eta.log_k_M27)</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 = exp(log_k_M31 + eta.log_k_M31)</span> -<span class="r-out co"><span class="r-pr">#></span> k1 = exp(log_k1 + eta.log_k1)</span> -<span class="r-out co"><span class="r-pr">#></span> k2 = exp(log_k2 + eta.log_k2)</span> -<span class="r-out co"><span class="r-pr">#></span> g = expit(g_qlogis + eta.g_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1 = expit(f_DMTA_tffm0_1_qlogis + eta.f_DMTA_tffm0_1_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2 = expit(f_DMTA_tffm0_2_qlogis + eta.f_DMTA_tffm0_2_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3 = expit(f_DMTA_tffm0_3_qlogis + eta.f_DMTA_tffm0_3_qlogis)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 = f_DMTA_tffm0_1</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 = f_DMTA_tffm0_2 * (1 - f_DMTA_tffm0_1)</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 = f_DMTA_tffm0_3 * (1 - f_DMTA_tffm0_2) * </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - f_DMTA_tffm0_1)</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(DMTA) = -((k1 * g * exp(-k1 * time) + k2 * (1 - </span> -<span class="r-out co"><span class="r-pr">#></span> g) * exp(-k2 * time))/(g * exp(-k1 * time) + (1 - </span> -<span class="r-out co"><span class="r-pr">#></span> g) * exp(-k2 * time))) * DMTA</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(M23) = +f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M23 * M23</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(M27) = +f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M27 * M27 + </span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> d/dt(M31) = +f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> k2 * (1 - g) * exp(-k2 * time))/(g * exp(-k1 * time) + </span> -<span class="r-out co"><span class="r-pr">#></span> (1 - g) * exp(-k2 * time))) * DMTA - k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA ~ add(sigma_low_DMTA) + prop(rsd_high_DMTA)</span> -<span class="r-out co"><span class="r-pr">#></span> M23 ~ add(sigma_low_M23) + prop(rsd_high_M23)</span> -<span class="r-out co"><span class="r-pr">#></span> M27 ~ add(sigma_low_M27) + prop(rsd_high_M27)</span> -<span class="r-out co"><span class="r-pr">#></span> M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)</span> -<span class="r-out co"><span class="r-pr">#></span> })</span> -<span class="r-out co"><span class="r-pr">#></span> }</span> -<span class="r-out co"><span class="r-pr">#></span> <environment: 0x555560091f40></span> +<span class="r-in"><span class="fu">nlmixr_model</span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span><span class="op">)</span></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmixr_model(f_dmta_mkin_tc):</span> could not find function "nlmixr_model"</span> <span class="r-in"><span class="co"># The focei fit takes about four minutes on my system</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span> -<span class="r-in"> <span class="va">f_dmta_nlmixr_focei</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span> +<span class="r-in"> <span class="va">f_dmta_nlmixr_focei</span> <span class="op"><-</span> <span class="fu">nlmixr</span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"focei"</span>,</span> <span class="r-in"> control <span class="op">=</span> <span class="fu">nlmixr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/foceiControl.html" class="external-link">foceiControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">500</span><span class="op">)</span><span class="op">)</span></span> <span class="r-in"><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span> -<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span> -<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span> -<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate jacobian</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:02 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate sensitivities</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:04 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate ∂(f)/∂(η)</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:01 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → calculate ∂(R²)/∂(η)</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:08 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in inner model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:07 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in inner model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:07 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in EBE model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:00 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in EBE model...</span> -<span class="r-out co"><span class="r-pr">#></span> [====|====|====|====|====|====|====|====|====|====] 0:00:00 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling inner model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → finding duplicate expressions in FD model...</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → optimizing duplicate expressions in FD model...</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling EBE model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling events FD model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> Needed Covariates:</span> -<span class="r-out co"><span class="r-pr">#></span> [1] "CMT"</span> -<span class="r-msg co"><span class="r-pr">#></span> RxODE 1.1.4 using 8 threads (see ?getRxThreads)</span> -<span class="r-msg co"><span class="r-pr">#></span> no cache: create with `rxCreateCache()`</span> -<span class="r-out co"><span class="r-pr">#></span> <span style="font-weight: bold;">Key:</span> U: Unscaled Parameters; X: Back-transformed parameters; G: Gill difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> F: Forward difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> C: Central difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> M: Mixed forward and central difference gradient approximation</span> -<span class="r-out co"><span class="r-pr">#></span> Unscaled parameters for Omegas=chol(solve(omega));</span> -<span class="r-out co"><span class="r-pr">#></span> Diagonals are transformed, as specified by foceiControl(diagXform=)</span> -<span class="r-out co"><span class="r-pr">#></span> |-----+---------------+-----------+-----------+-----------+-----------|</span> -<span class="r-out co"><span class="r-pr">#></span> | #| Objective Fun | DMTA_0 | log_k_M23 | log_k_M27 | log_k_M31 |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................| log_k1 | log_k2 | g_qlogis |f_DMTA_tffm0_1_qlogis |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................|f_DMTA_tffm0_2_qlogis |f_DMTA_tffm0_3_qlogis | sigma_low | rsd_high |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................| o1 | o2 | o3 | o4 |</span> -<span class="r-out co"><span class="r-pr">#></span> |.....................| o5 | o6 | o7 | o8 |</span> -<span class="r-out co"><span class="r-pr">#></span> <span style="text-decoration: underline;">|.....................| o9 | o10 |...........|...........|</span></span> -<span class="r-out co"><span class="r-pr">#></span> calculating covariance matrix</span> -<span class="r-out co"><span class="r-pr">#></span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating residuals/tables</span> -<span class="r-msg co"><span class="r-pr">#></span> done</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>initial ETAs were nudged; (can control by foceiControl(etaNudge=., etaNudge2=))</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>ETAs were reset to zero during optimization; (Can control by foceiControl(resetEtaP=.))</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>last objective function was not at minimum, possible problems in optimization</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>S matrix non-positive definite</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>using R matrix to calculate covariance</span> -<span class="r-wrn co"><span class="r-pr">#></span> <span class="warning">Warning: </span>gradient problems with initial estimate and covariance; see $scaleInfo</span> -<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 553.721 10.570 564.258 </span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmixr(f_dmta_mkin_tc, est = "focei", control = nlmixr::foceiControl(print = 500)):</span> could not find function "nlmixr"</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_focei</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> nlmixr version used for fitting: 2.0.6 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:27:22 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:27:22 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Equations:</span> -<span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * DMTA</span> -<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M23 * M23</span> -<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 563 observations of 4 variable(s) grouped in 6 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Degradation model predictions using RxODE</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 564.08 s</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 </span> -<span class="r-out co"><span class="r-pr">#></span> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> -1.7554 -2.2352 -3.7758 0.4363 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for error model parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low rsd_high </span> -<span class="r-out co"><span class="r-pr">#></span> 0.70012 0.02577 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> -<span class="r-out co"><span class="r-pr">#></span> None</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Results:</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood calculated by focei </span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 1857 1952 -906.5</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 98.0116 95.243 100.780</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.0184 -5.213 -2.824</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -4.2033 -5.013 -3.394</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -4.1728 -4.999 -3.347</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.4831 -3.398 -1.568</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -3.8423 -5.450 -2.235</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 0.4682 -2.188 3.124</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -2.0823 -2.591 -1.574</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -2.1265 -2.686 -1.567</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -2.0795 -2.735 -1.424</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 lg__M23 lg__M27 lg__M31 log_k1 log_k2 g_qlogs</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0154 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0164 0.0031 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0131 0.0018 0.0541 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -0.0306 0.0045 0.0019 0.0011 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0527 -0.0043 -0.0037 -0.0003 0.0375 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.1005 0.0076 0.0074 0.0013 0.0910 0.1151 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -0.0308 0.0362 0.0024 0.0021 0.0058 -0.0070 0.0145</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -0.0309 0.0062 0.0353 -0.0229 0.0047 -0.0082 0.0146</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -0.0308 0.0061 0.0419 0.0547 0.0033 -0.0055 0.0104</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_0_1 f_DMTA_0_2</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis 0.0118 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis 0.0086 -0.0057 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Random effects (omega):</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 4.224 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 1.041 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.4609 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.0000 0.4728</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 eta.log_k2 eta.g_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.635 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 1.662 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 4.36</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.00</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.1909 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.2232</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.3149</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low rsd_high </span> -<span class="r-out co"><span class="r-pr">#></span> 0.82408 0.03045 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 98.01163 95.243379 100.77988</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01798 0.005443 0.05940</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 0.01495 0.006652 0.03358</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01541 0.006746 0.03520</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.11083 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.09474 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.08827 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.08348 0.033429 0.20848</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.02144 0.004296 0.10704</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.61496 0.100857 0.95788</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> -<span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.11083</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.09474</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.08827</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.70616</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> -<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA 12.96 64.24 19.34 8.303 32.32</span> -<span class="r-out co"><span class="r-pr">#></span> M23 38.55 128.06 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M27 46.38 154.06 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M31 44.98 149.43 NA NA NA</span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in summary(f_dmta_nlmixr_focei):</span> object 'f_dmta_nlmixr_focei' not found</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_focei</span><span class="op">)</span></span> -<span class="r-plt img"><img src="dimethenamid_2018-1.png" alt="" width="700" height="433"></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_dmta_nlmixr_focei):</span> object 'f_dmta_nlmixr_focei' not found</span> <span class="r-in"><span class="co"># Using saemix takes about 18 minutes</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span> <span class="r-in"> <span class="va">f_dmta_saemix</span> <span class="op"><-</span> <span class="fu"><a href="saem.html">saem</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, test_log_parms <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> @@ -496,8 +194,8 @@ specific pieces of information in the comments.</p> <span class="r-out co"><span class="r-pr">#></span> In above message, R1 = 55.3899, R2 = nan</span> <span class="r-out co"><span class="r-pr">#></span> </span> <span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in out[available, var]:</span> (subscript) logical subscript too long</span> -<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.58 0 12.58</span> -<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.99 0.008 13</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 12.76 3.069 11.79</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 13.77 4.719 12.37</span> <span class="r-in"></span> <span class="r-in"><span class="co"># nlmixr with est = "saem" is pretty fast with default iteration numbers, most</span></span> <span class="r-in"><span class="co"># of the time (about 2.5 minutes) is spent for calculating the log likelihood at the end</span></span> @@ -506,203 +204,17 @@ specific pieces of information in the comments.</p> <span class="r-in"><span class="co"># convincing for the parent. It seems we are fitting an overparameterised</span></span> <span class="r-in"><span class="co"># model, so the result we get strongly depends on starting parameters and control settings.</span></span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/system.time.html" class="external-link">system.time</a></span><span class="op">(</span></span> -<span class="r-in"> <span class="va">f_dmta_nlmixr_saem</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/nlmixr.html" class="external-link">nlmixr</a></span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span> +<span class="r-in"> <span class="va">f_dmta_nlmixr_saem</span> <span class="op"><-</span> <span class="fu">nlmixr</span><span class="op">(</span><span class="va">f_dmta_mkin_tc</span>, est <span class="op">=</span> <span class="st">"saem"</span>,</span> <span class="r-in"> control <span class="op">=</span> <span class="fu">nlmixr</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/nlmixr/man/saemControl.html" class="external-link">saemControl</a></span><span class="op">(</span>print <span class="op">=</span> <span class="fl">500</span>, logLik <span class="op">=</span> <span class="cn">TRUE</span>, nmc <span class="op">=</span> <span class="fl">9</span><span class="op">)</span><span class="op">)</span></span> <span class="r-in"><span class="op">)</span></span> -<span class="r-msg co"><span class="r-pr">#></span> 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)</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> parameter labels from comments are typically ignored in non-interactive mode</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BBBB;">ℹ</span> Need to run with the source intact to parse comments</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → generate SAEM model</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-out co"><span class="r-pr">#></span> 1: 98.7179 -3.4492 -3.2592 -3.6952 -2.1629 -2.7824 0.8990 -2.8080 -2.7380 -2.8041 2.7789 0.6848 0.8170 0.7125 0.8550 1.5200 2.9882 0.3073 0.2850 0.2877 4.0480 0.4153 4.5214 0.3775 4.4419 0.4181 3.7069 0.5935</span> -<span class="r-out co"><span class="r-pr">#></span> 500: 97.8519 -4.3891 -4.0888 -4.1247 -2.9246 -4.2755 2.6294 -2.1212 -2.1380 -2.0739 3.1293 1.2665 0.2763 0.3429 0.5743 1.5561 4.4991 0.1499 0.1551 0.3103 0.9514 0.0341 0.4846 0.1068 0.6597 0.0767 0.7836 0.0360</span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating covariance matrix</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> → creating full model...</span> -<span class="r-msg co"><span class="r-pr">#></span> → pruning branches (`if`/`else`)...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → loading into <span style="color: #0000BB;">symengine</span> environment...</span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> → compiling EBE model...</span> -<span class="r-msg co"><span class="r-pr">#></span> </span> -<span class="r-msg co"><span class="r-pr">#></span> <span style="color: #00BB00;">✔</span> done</span> -<span class="r-msg co"><span class="r-pr">#></span> Needed Covariates:</span> -<span class="r-out co"><span class="r-pr">#></span> [1] "CMT"</span> -<span class="r-msg co"><span class="r-pr">#></span> Calculating residuals/tables</span> -<span class="r-msg co"><span class="r-pr">#></span> done</span> -<span class="r-out co"><span class="r-pr">#></span> user system elapsed </span> -<span class="r-out co"><span class="r-pr">#></span> 785.825 3.841 153.598 </span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in nlmixr(f_dmta_mkin_tc, est = "saem", control = nlmixr::saemControl(print = 500, logLik = TRUE, nmc = 9)):</span> could not find function "nlmixr"</span> +<span class="r-msg co"><span class="r-pr">#></span> Timing stopped at: 0 0 0.001</span> <span class="r-in"><span class="fu">traceplot</span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">$</span><span class="va">nm</span><span class="op">)</span></span> <span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in traceplot(f_dmta_nlmixr_saem$nm):</span> could not find function "traceplot"</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">)</span></span> -<span class="r-out co"><span class="r-pr">#></span> nlmixr version used for fitting: 2.0.6 </span> -<span class="r-out co"><span class="r-pr">#></span> mkin version used for pre-fitting: 1.1.0 </span> -<span class="r-out co"><span class="r-pr">#></span> R version used for fitting: 4.1.2 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of fit: Wed Mar 2 13:30:09 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> Date of summary: Wed Mar 2 13:30:09 2022 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Equations:</span> -<span class="r-out co"><span class="r-pr">#></span> d_DMTA/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *</span> -<span class="r-out co"><span class="r-pr">#></span> time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))</span> -<span class="r-out co"><span class="r-pr">#></span> * DMTA</span> -<span class="r-out co"><span class="r-pr">#></span> d_M23/dt = + f_DMTA_to_M23 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M23 * M23</span> -<span class="r-out co"><span class="r-pr">#></span> d_M27/dt = + f_DMTA_to_M27 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M27 * M27 + k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> d_M31/dt = + f_DMTA_to_M31 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)</span> -<span class="r-out co"><span class="r-pr">#></span> * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *</span> -<span class="r-out co"><span class="r-pr">#></span> exp(-k2 * time))) * DMTA - k_M31 * M31</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Data:</span> -<span class="r-out co"><span class="r-pr">#></span> 563 observations of 4 variable(s) grouped in 6 datasets</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Degradation model predictions using RxODE</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fitted in 153.313 s</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model: Two-component variance function </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for individual parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 log_k_M23 log_k_M27 log_k_M31 f_DMTA_ilr_1 f_DMTA_ilr_2 </span> -<span class="r-out co"><span class="r-pr">#></span> 98.7132 -3.9216 -4.3306 -4.2442 0.1376 0.1388 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_ilr_3 log_k1 log_k2 g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> -1.7554 -2.2352 -3.7758 0.4363 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Mean of starting values for error model parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.70012 0.02577 0.70012 0.02577 0.70012 </span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 sigma_low_M31 rsd_high_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.02577 0.70012 0.02577 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Fixed degradation parameter values:</span> -<span class="r-out co"><span class="r-pr">#></span> None</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Results:</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Likelihood calculated by focei </span> -<span class="r-out co"><span class="r-pr">#></span> AIC BIC logLik</span> -<span class="r-out co"><span class="r-pr">#></span> 1966 2088 -955.2</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Optimised parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 97.852 95.86386 99.840</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -4.389 -5.35084 -3.427</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -4.089 -4.54432 -3.633</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -4.125 -4.63280 -3.617</span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 -2.925 -3.54158 -2.308</span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 -4.275 -5.81760 -2.733</span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis 2.629 -0.01785 5.277</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -2.121 -2.44462 -1.798</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -2.138 -2.47804 -1.798</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -2.074 -2.53581 -1.612</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Correlation: </span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 lg__M23 lg__M27 lg__M31 log_k1 log_k2 g_qlogs</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 -0.0164 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 -0.0267 0.0028 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 -0.0179 0.0023 0.0755 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 0.0385 -0.0034 -0.0054 -0.0029 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 0.0381 0.0115 0.0087 0.0093 0.0786 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis -0.0656 0.0021 0.0051 0.0001 -0.1177 -0.4389 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis -0.0604 0.0554 0.0054 0.0039 -0.0082 -0.0022 0.0119</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis -0.0601 0.0091 0.0577 -0.0350 -0.0081 -0.0057 0.0137</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis -0.0515 0.0083 0.0569 0.0729 -0.0059 0.0005 0.0073</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_0_1 f_DMTA_0_2</span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M23 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k1 </span> -<span class="r-out co"><span class="r-pr">#></span> log_k2 </span> -<span class="r-out co"><span class="r-pr">#></span> g_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_1_qlogis </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_2_qlogis 0.0167 </span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_tffm0_3_qlogis 0.0145 -0.0060 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Random effects (omega):</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 eta.log_k_M23 eta.log_k_M27 eta.log_k_M31</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 3.129 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.000 1.266 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.000 0.000 0.2763 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.000 0.000 0.0000 0.3429</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.000 0.000 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 eta.log_k2 eta.g_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.5743 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 1.556 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.000 4.499</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.000 0.000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis eta.f_DMTA_tffm0_2_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.1499 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000 0.1551</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.0000 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis</span> -<span class="r-out co"><span class="r-pr">#></span> eta.DMTA_0 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M23 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M27 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k_M31 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k1 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.log_k2 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.g_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_1_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_2_qlogis 0.0000</span> -<span class="r-out co"><span class="r-pr">#></span> eta.f_DMTA_tffm0_3_qlogis 0.3103</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Variance model:</span> -<span class="r-out co"><span class="r-pr">#></span> sigma_low_DMTA rsd_high_DMTA sigma_low_M23 rsd_high_M23 sigma_low_M27 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.95135 0.03412 0.48455 0.10682 0.65969 </span> -<span class="r-out co"><span class="r-pr">#></span> rsd_high_M27 sigma_low_M31 rsd_high_M31 </span> -<span class="r-out co"><span class="r-pr">#></span> 0.07670 0.78365 0.03598 </span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Backtransformed parameters:</span> -<span class="r-out co"><span class="r-pr">#></span> est. lower upper</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_0 97.85189 95.863863 99.83992</span> -<span class="r-out co"><span class="r-pr">#></span> k_M23 0.01241 0.004744 0.03247</span> -<span class="r-out co"><span class="r-pr">#></span> k_M27 0.01676 0.010627 0.02643</span> -<span class="r-out co"><span class="r-pr">#></span> k_M31 0.01617 0.009727 0.02687</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M23 0.10705 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M27 0.09417 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> f_DMTA_to_M31 0.08919 NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> k1 0.05369 0.028968 0.09950</span> -<span class="r-out co"><span class="r-pr">#></span> k2 0.01391 0.002975 0.06500</span> -<span class="r-out co"><span class="r-pr">#></span> g 0.93273 0.495538 0.99492</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Resulting formation fractions:</span> -<span class="r-out co"><span class="r-pr">#></span> ff</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M23 0.10705</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M27 0.09417</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_M31 0.08919</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA_sink 0.70959</span> -<span class="r-out co"><span class="r-pr">#></span> </span> -<span class="r-out co"><span class="r-pr">#></span> Estimated disappearance times:</span> -<span class="r-out co"><span class="r-pr">#></span> DT50 DT90 DT50back DT50_k1 DT50_k2</span> -<span class="r-out co"><span class="r-pr">#></span> DMTA 13.81 49.3 14.84 12.91 49.85</span> -<span class="r-out co"><span class="r-pr">#></span> M23 55.85 185.5 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M27 41.36 137.4 NA NA NA</span> -<span class="r-out co"><span class="r-pr">#></span> M31 42.87 142.4 NA NA NA</span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in summary(f_dmta_nlmixr_saem):</span> object 'f_dmta_nlmixr_saem' not found</span> <span class="r-in"><span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html" class="external-link">plot</a></span><span class="op">(</span><span class="va">f_dmta_nlmixr_saem</span><span class="op">)</span></span> -<span class="r-plt img"><img src="dimethenamid_2018-2.png" alt="" width="700" height="433"></span> +<span class="r-err co"><span class="r-pr">#></span> <span class="error">Error in plot(f_dmta_nlmixr_saem):</span> object 'f_dmta_nlmixr_saem' not found</span> <span class="r-in"><span class="co"># }</span></span> </code></pre></div> </div> 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