From c41381a961263c28d60976e68923157916c78b15 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 16 Sep 2021 15:31:13 +0200 Subject: Adapt and improve the dimethenamid vignette Adapt to the corrected data and unify control parameters for saemix and nlmixr with saem. Update docs --- docs/dev/reference/dimethenamid_2018.html | 320 +++++++++++++++--------------- 1 file changed, 157 insertions(+), 163 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 a77cf0f4..919e9363 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 - 1.0.5 + 1.1.0 @@ -162,7 +162,7 @@ constrained by data protection regulations.

Format

-

An mkindsg object grouping eight datasets with some meta information

+

An mkindsg object grouping seven datasets with some meta information

Source

Rapporteur Member State Germany, Co-Rapporteur Member State Bulgaria (2018) @@ -177,42 +177,36 @@ specific pieces of information in the comments.

Examples

print(dimethenamid_2018)
-
#> <mkindsg> holding 8 mkinds objects
+
#> <mkindsg> holding 7 mkinds objects
 #> Title $title:  Aerobic soil degradation data on dimethenamid-P from the EU assessment in 2018 
 #> Occurrence of observed compounds $observed_n:
 #> DMTAP   M23   M27   M31  DMTA 
-#>     4     7     7     7     4 
+#>     3     7     7     7     4 
 #> Time normalisation factors $f_time_norm:
-#> [1] 1.0000000 0.9706477 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922
-#> [8] 0.6733938
+#> [1] 1.0000000 0.9706477 1.2284784 1.2284784 0.6233856 0.7678922 0.6733938
 #> Meta information $meta:
-#>                       study  usda_soil_type study_moisture_ref_type
-#> Calke         Unsworth 2014      Sandy loam                     pF2
-#> Borstel 1 Staudenmaier 2013            Sand                     pF1
-#> Borstel 2 Staudenmaier 2009            Sand                     pF1
-#> Elliot 1         Wendt 1997       Clay loam                   pF2.5
-#> Elliot 2         Wendt 1997       Clay loam                   pF2.5
-#> Flaach           König 1996 Sandy clay loam                     pF1
-#> BBA 2.2          König 1995      Loamy sand                     pF1
-#> BBA 2.3          König 1995      Sandy loam                     pF1
-#>           rel_moisture study_ref_moisture temperature
-#> Calke             1.00                 NA          20
-#> Borstel 1         0.50              23.00          20
-#> Borstel 2         0.50              23.00          20
-#> Elliot 1          0.75              33.37          23
-#> 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
dmta_ds <- lapply(1:8, function(i) {
+#>                      study  usda_soil_type study_moisture_ref_type rel_moisture
+#> Calke        Unsworth 2014      Sandy loam                     pF2         1.00
+#> Borstel  Staudenmaier 2009            Sand                     pF1         0.50
+#> Elliot 1        Wendt 1997       Clay loam                   pF2.5         0.75
+#> Elliot 2        Wendt 1997       Clay loam                   pF2.5         0.75
+#> Flaach          König 1996 Sandy clay loam                     pF1         0.40
+#> BBA 2.2         König 1995      Loamy sand                     pF1         0.40
+#> BBA 2.3         König 1995      Sandy loam                     pF1         0.40
+#>          study_ref_moisture temperature
+#> Calke                    NA          20
+#> Borstel               23.00          20
+#> Elliot 1              33.37          23
+#> Elliot 2              33.37          23
+#> Flaach                   NA          20
+#> BBA 2.2                  NA          20
+#> BBA 2.3                  NA          20
dmta_ds <- lapply(1:7, 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
@@ -231,33 +225,33 @@ specific pieces of information in the comments.

 
#> 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
+#>         DMTA_0 = 98.7132391714013
+#>         eta.DMTA_0 ~ 2.32692496033921
 #>         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
+#>         log_k_M27 = -4.33057580082049
+#>         eta.log_k_M27 ~ 0.855184233768426
+#>         log_k_M31 = -4.24415516780733
+#>         eta.log_k_M31 ~ 0.745746058085877
+#>         log_k1 = -2.23515804885306
+#>         eta.log_k1 ~ 0.901033446532357
+#>         log_k2 = -3.77581484944379
+#>         eta.log_k2 ~ 1.57682329638124
+#>         g_qlogis = 0.436302910942805
+#>         eta.g_qlogis ~ 3.10190528862808
+#>         f_DMTA_tffm0_1_qlogis = -2.0914852208395
 #>         eta.f_DMTA_tffm0_1_qlogis ~ 0.3
-#>         f_DMTA_tffm0_2_qlogis = -2.18057573598794
+#>         f_DMTA_tffm0_2_qlogis = -2.17879574608926
 #>         eta.f_DMTA_tffm0_2_qlogis ~ 0.3
-#>         f_DMTA_tffm0_3_qlogis = -2.14267187609763
+#>         f_DMTA_tffm0_3_qlogis = -2.14036526460782
 #>         eta.f_DMTA_tffm0_3_qlogis ~ 0.3
-#>         sigma_low_DMTA = 0.697933852349996
+#>         sigma_low_DMTA = 0.700117227383809
 #>         rsd_high_DMTA = 0.0257724286053519
-#>         sigma_low_M23 = 0.697933852349996
+#>         sigma_low_M23 = 0.700117227383809
 #>         rsd_high_M23 = 0.0257724286053519
-#>         sigma_low_M27 = 0.697933852349996
+#>         sigma_low_M27 = 0.700117227383809
 #>         rsd_high_M27 = 0.0257724286053519
-#>         sigma_low_M31 = 0.697933852349996
+#>         sigma_low_M31 = 0.700117227383809
 #>         rsd_high_M31 = 0.0257724286053519
 #>     })
 #>     model({
@@ -295,7 +289,7 @@ specific pieces of information in the comments.

 #>         M31 ~ add(sigma_low_M31) + prop(rsd_high_M31)
 #>     })
 #> }
-#> <environment: 0x555559ac3820>
# The focei fit takes about four minutes on my system
+#> <environment: 0x555559e97ac0>
# 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))
@@ -308,7 +302,7 @@ specific pieces of information in the comments.

 #> 
#> → 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)
+#> 
#> → 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
@@ -324,12 +318,12 @@ specific pieces of information in the comments.

 #> |.....................|        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 
-#> 232.621  14.126 246.850 
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:     Wed Aug  4 15:53:54 2021 
-#> Date of summary: Wed Aug  4 15:53:54 2021 
+#> 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 *
@@ -346,23 +340,23 @@ specific pieces of information in the comments.

 #>            exp(-k2 * time))) * DMTA - k_M31 * M31
 #> 
 #> Data:
-#> 568 observations of 4 variable(s) grouped in 6 datasets
+#> 563 observations of 4 variable(s) grouped in 6 datasets
 #> 
 #> Degradation model predictions using RxODE
 #> 
-#> Fitted in 246.669 s
+#> 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 
-#>      98.7698      -3.9216      -4.3377      -4.2477       0.1380       0.1393 
+#>      98.7132      -3.9216      -4.3306      -4.2442       0.1376       0.1388 
 #> f_DMTA_ilr_3       log_k1       log_k2     g_qlogis 
-#>      -1.7571      -2.2341      -3.7763       0.4502 
+#>      -1.7554      -2.2352      -3.7758       0.4363 
 #> 
 #> Mean of starting values for error model parameters:
 #> sigma_low  rsd_high 
-#>   0.69793   0.02577 
+#>   0.70012   0.02577 
 #> 
 #> Fixed degradation parameter values:
 #> None
@@ -371,20 +365,20 @@ specific pieces of information in the comments.

 #> 
 #> Likelihood calculated by focei  
 #>    AIC  BIC logLik
-#>   1936 2031 -945.9
+#>   1918 2014 -937.2
 #> 
 #> 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
+#> 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
@@ -410,10 +404,10 @@ specific pieces of information in the comments.

 #> 
 #> 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.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        0.8645        0.0000
-#> eta.log_k_M31                  0.000        0.0000        0.0000        0.7503
+#> 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
@@ -425,9 +419,9 @@ specific pieces of information in the comments.

 #> 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_k1                     0.901      0.000        0.000
 #> eta.log_k2                     0.000      1.577        0.000
-#> eta.g_qlogis                   0.000      0.000        3.085
+#> 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
@@ -456,44 +450,44 @@ specific pieces of information in the comments.

 #> 
 #> Variance model:
 #> sigma_low  rsd_high 
-#>   0.69793   0.02577 
+#>   0.70012   0.02577 
 #> 
 #> Backtransformed parameters:
 #>                   est.    lower    upper
-#> DMTA_0        98.76976 98.73563 98.80390
+#> DMTA_0        98.71324 98.68012 98.74636
 #> 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
+#> 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  0.10984
-#> DMTA_M27  0.09036
-#> DMTA_M31  0.08399
-#> DMTA_sink 0.71581
+#> 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 10.66  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)
+#>       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
 system.time(
   f_dmta_saemix <- saem(f_dmta_mkin_tc, test_log_parms = TRUE)
 )
 
#> Running main SAEM algorithm
-#> [1] "Wed Aug  4 15:53:55 2021"
+#> [1] "Thu Sep 16 14:06:56 2021"
 #> ....
 #>     Minimisation finished
-#> [1] "Wed Aug  4 16:12:40 2021"
#>     user   system  elapsed 
-#> 1192.021    0.064 1192.182 

+#> [1] "Thu Sep 16 14:25:28 2021"
#>     user   system  elapsed 
+#> 1176.278    0.021 1176.388 

 # 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
@@ -504,15 +498,15 @@ 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:    98.3427   -3.5148   -3.3187   -3.7728   -2.1163   -2.8457    0.9482   -2.8064   -2.7412   -2.8745    2.7912    0.6805    0.8213    0.8055    0.8578    1.4980    2.9309    0.2850    0.2854    0.2850    4.0990    0.3821    3.5349    0.6537    5.4143    0.0002    4.5093    0.1905
-#> 500:    97.8277   -4.3506   -4.0318   -4.1520   -3.0553   -3.5843    1.1326   -2.0873   -2.0421   -2.0751    0.2960    1.2515    0.2531    0.3807    0.7928    0.8863    6.5211    0.1433    0.1082    0.3353    0.8960    0.0470    0.7501    0.0475    0.9527    0.0281    0.7321    0.0594
#> 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 
-#> 813.299   3.736 151.935 
traceplot(f_dmta_nlmixr_saem$nm)
+
#> With est = 'saem', a different error model is required for each observed variableChanging the error model to 'obs_tc' (Two-component error for each observed variable)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#>  
#> → generate SAEM model
#> ✔ done
#> 1:    98.3400   -3.5096   -3.3392   -3.7596   -2.2055   -2.7755    1.0281   -2.7872   -2.7223   -2.8341    2.6422    0.7027    0.8124    0.7085    0.8560    1.4980    3.2777    0.3063    0.2850    0.2850    4.1120    0.3716    4.4582    0.3994    4.4820    0.4025    3.7803    0.5780
+#> 500:    97.8212   -4.4030   -4.0872   -4.1289   -2.8278   -4.3505    2.6614   -2.1252   -2.1308   -2.0749    2.9463    1.2933    0.2802    0.3467    0.4814    0.7877    3.0743    0.1508    0.1523    0.3155    0.9557    0.0333    0.4787    0.1073    0.6826    0.0707    0.7849    0.0356
#> Calculating covariance matrix
#> 
#> Calculating -2LL by Gaussian quadrature (nnodes=3,nsd=1.6)
#> 
#> → creating full model...
#> → pruning branches (`if`/`else`)...
#> ✔ done
#> → loading into symengine environment...
#> ✔ done
#> → compiling EBE model...
#>  
#> ✔ done
#> Needed Covariates:
#> [1] "CMT"
#> Calculating residuals/tables
#> done
#>    user  system elapsed 
+#> 800.784   3.715 149.687 
traceplot(f_dmta_nlmixr_saem$nm)
 
#> Error in traceplot(f_dmta_nlmixr_saem$nm): could not find function "traceplot"
summary(f_dmta_nlmixr_saem)
-
#> nlmixr version used for fitting:    2.0.4 
-#> mkin version used for pre-fitting:  1.0.5 
-#> R version used for fitting:         4.1.0 
-#> Date of fit:     Wed Aug  4 16:16:18 2021 
-#> Date of summary: Wed Aug  4 16:16:18 2021 
+
#> 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 *
@@ -529,25 +523,25 @@ specific pieces of information in the comments.

 #>            exp(-k2 * time))) * DMTA - k_M31 * M31
 #> 
 #> Data:
-#> 568 observations of 4 variable(s) grouped in 6 datasets
+#> 563 observations of 4 variable(s) grouped in 6 datasets
 #> 
 #> Degradation model predictions using RxODE
 #> 
-#> Fitted in 151.67 s
+#> 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 
-#>      98.7698      -3.9216      -4.3377      -4.2477       0.1380       0.1393 
+#>      98.7132      -3.9216      -4.3306      -4.2442       0.1376       0.1388 
 #> f_DMTA_ilr_3       log_k1       log_k2     g_qlogis 
-#>      -1.7571      -2.2341      -3.7763       0.4502 
+#>      -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 
-#>        0.69793        0.02577        0.69793        0.02577        0.69793 
+#>        0.70012        0.02577        0.70012        0.02577        0.70012 
 #>   rsd_high_M27  sigma_low_M31   rsd_high_M31 
-#>        0.02577        0.69793        0.02577 
+#>        0.02577        0.70012        0.02577 
 #> 
 #> Fixed degradation parameter values:
 #> None
@@ -556,32 +550,32 @@ specific pieces of information in the comments.

 #> 
 #> Likelihood calculated by focei  
 #>    AIC  BIC logLik
-#>   2036 2157 -989.8
+#>   1953 2074 -948.3
 #> 
 #> Optimised parameters:
 #>                         est.  lower  upper
-#> DMTA_0                97.828 96.121 99.535
-#> log_k_M23             -4.351 -5.300 -3.401
-#> log_k_M27             -4.032 -4.470 -3.594
-#> log_k_M31             -4.152 -4.689 -3.615
-#> log_k1                -3.055 -3.785 -2.325
-#> log_k2                -3.584 -4.517 -2.651
-#> g_qlogis               1.133 -2.165  4.430
-#> f_DMTA_tffm0_1_qlogis -2.087 -2.407 -1.768
-#> f_DMTA_tffm0_2_qlogis -2.042 -2.336 -1.748
-#> f_DMTA_tffm0_3_qlogis -2.075 -2.557 -1.593
+#> 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             -0.031                                          
-#> log_k_M27             -0.050  0.004                                   
-#> log_k_M31             -0.032  0.003  0.078                            
-#> log_k1                 0.014 -0.002 -0.002 -0.001                     
-#> log_k2                 0.059  0.006 -0.001  0.002 -0.037              
-#> g_qlogis              -0.077  0.005  0.009  0.004  0.035 -0.201       
-#> f_DMTA_tffm0_1_qlogis -0.104  0.066  0.009  0.006  0.000 -0.011  0.014
-#> f_DMTA_tffm0_2_qlogis -0.120  0.013  0.081 -0.033 -0.002 -0.013  0.017
-#> f_DMTA_tffm0_3_qlogis -0.086  0.010  0.060  0.078 -0.002 -0.005  0.010
+#> 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                                  
@@ -590,15 +584,15 @@ specific pieces of information in the comments.

 #> log_k2                                     
 #> g_qlogis                                   
 #> f_DMTA_tffm0_1_qlogis                      
-#> f_DMTA_tffm0_2_qlogis  0.026               
-#> f_DMTA_tffm0_3_qlogis  0.019      0.002    
+#> 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                     0.296         0.000        0.0000        0.0000
-#> eta.log_k_M23                  0.000         1.252        0.0000        0.0000
-#> eta.log_k_M27                  0.000         0.000        0.2531        0.0000
-#> eta.log_k_M31                  0.000         0.000        0.0000        0.3807
+#> 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
@@ -610,9 +604,9 @@ specific pieces of information in the comments.

 #> 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                    0.7928     0.0000        0.000
-#> eta.log_k2                    0.0000     0.8863        0.000
-#> eta.g_qlogis                  0.0000     0.0000        6.521
+#> 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
@@ -624,8 +618,8 @@ specific pieces of information in the comments.

 #> 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                    0.1433                    0.0000
-#> eta.f_DMTA_tffm0_2_qlogis                    0.0000                    0.1082
+#> 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
@@ -637,40 +631,40 @@ specific pieces of information in the comments.

 #> 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                    0.3353
+#> 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 
-#>        0.89603        0.04704        0.75015        0.04753        0.95265 
+#>        0.95572        0.03325        0.47871        0.10733        0.68264 
 #>   rsd_high_M27  sigma_low_M31   rsd_high_M31 
-#>        0.02810        0.73212        0.05942 
+#>        0.07072        0.78486        0.03557 
 #> 
 #> Backtransformed parameters:
 #>                   est.     lower    upper
-#> DMTA_0        97.82774 96.120503 99.53498
-#> k_M23          0.01290  0.004991  0.03334
-#> k_M27          0.01774  0.011451  0.02749
-#> k_M31          0.01573  0.009195  0.02692
-#> f_DMTA_to_M23  0.11033        NA       NA
-#> f_DMTA_to_M27  0.10218        NA       NA
-#> f_DMTA_to_M31  0.08784        NA       NA
-#> k1             0.04711  0.022707  0.09773
-#> k2             0.02775  0.010918  0.07056
-#> g              0.75632  0.102960  0.98823
+#> 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  0.11033
-#> DMTA_M27  0.10218
-#> DMTA_M31  0.08784
-#> DMTA_sink 0.69965
+#> 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 16.59  57.44    17.29   14.71   24.97
-#> M23  53.74 178.51       NA      NA      NA
-#> M27  39.07 129.78       NA      NA      NA
-#> M31  44.06 146.36       NA      NA      NA
plot(f_dmta_nlmixr_saem)
+#> 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)
 
# }
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