From 6178249bbb5e9de7cb7f34287ee7de28a68fed6c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 10 Aug 2022 15:38:17 +0200 Subject: Change dev branch used for docs, update static docs --- docs/dev/reference/saem.html | 613 +++++++++++++++++++++++++++++++++---------- 1 file changed, 477 insertions(+), 136 deletions(-) (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html index c132647b..b55f29a8 100644 --- a/docs/dev/reference/saem.html +++ b/docs/dev/reference/saem.html @@ -19,7 +19,7 @@ Expectation Maximisation algorithm (SAEM)."> mkin - 1.1.0 + 1.1.2 @@ -28,7 +28,7 @@ Expectation Maximisation algorithm (SAEM).">Functions and data
  • -
    • +
  • + Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models +
  • Example evaluation of FOCUS Example Dataset Z
    • @@ -88,39 +91,40 @@ Expectation Maximisation algorithm (SAEM).

    - 
    saem(object, ...)
-
-# S3 method for mmkin
-saem(
-  object,
-  transformations = c("mkin", "saemix"),
-  degparms_start = numeric(),
-  test_log_parms = TRUE,
-  conf.level = 0.6,
-  solution_type = "auto",
-  nbiter.saemix = c(300, 100),
-  control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,
-    save = FALSE, save.graphs = FALSE),
-  fail_with_errors = TRUE,
-  verbose = FALSE,
-  quiet = FALSE,
-  ...
-)
-
-# S3 method for saem.mmkin
-print(x, digits = max(3, getOption("digits") - 3), ...)
-
-saemix_model(
-  object,
-  solution_type = "auto",
-  transformations = c("mkin", "saemix"),
-  degparms_start = numeric(),
-  test_log_parms = FALSE,
-  verbose = FALSE,
-  ...
-)
-
-saemix_data(object, verbose = FALSE, ...)
    + 
    saem(object, ...)
+
+# S3 method for mmkin
+saem(
+  object,
+  transformations = c("mkin", "saemix"),
+  degparms_start = numeric(),
+  test_log_parms = TRUE,
+  conf.level = 0.6,
+  solution_type = "auto",
+  nbiter.saemix = c(300, 100),
+  control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,
+    save = FALSE, save.graphs = FALSE),
+  fail_with_errors = TRUE,
+  verbose = FALSE,
+  quiet = FALSE,
+  ...
+)
+
+# S3 method for saem.mmkin
+print(x, digits = max(3, getOption("digits") - 3), ...)
+
+saemix_model(
+  object,
+  solution_type = "auto",
+  transformations = c("mkin", "saemix"),
+  degparms_start = numeric(),
+  test_log_parms = FALSE,
+  conf.level = 0.6,
+  verbose = FALSE,
+  ...
+)
+
+saemix_data(object, verbose = FALSE, ...)
    @@ -128,54 +132,87 @@ Expectation Maximisation algorithm (SAEM).

    object

    An mmkin row object containing several fits of the same mkinmod model to different datasets

    + +
    ...

    Further parameters passed to saemix::saemixModel.

    + +
    transformations

    Per default, all parameter transformations are done in mkin. If this argument is set to 'saemix', parameter transformations -are done in 'saemix' for the supported cases. Currently this is only -supported in cases where the initial concentration of the parent is not fixed, -SFO or DFOP is used for the parent and there is either no metabolite or one.

    +are done in 'saemix' for the supported cases, i.e. (as of version 1.1.2) +SFO, FOMC, DFOP and HS without fixing parent_0, and SFO or DFOP with +one SFO metabolite.

    + +
    degparms_start

    Parameter values given as a named numeric vector will be used to override the starting values obtained from the 'mmkin' object.

    + +
    test_log_parms

    If TRUE, an attempt is made to use more robust starting values for population parameters fitted as log parameters in mkin (like rate constants) by only considering rate constants that pass the t-test when calculating mean degradation parameters using mean_degparms.

    + +
    conf.level

    Possibility to adjust the required confidence level for parameter that are tested if requested by 'test_log_parms'.

    + +
    solution_type

    Possibility to specify the solution type in case the automatic choice is not desired

    + +
    nbiter.saemix

    Convenience option to increase the number of iterations

    + +
    control

    Passed to saemix::saemix.

    + +
    fail_with_errors

    Should a failure to compute standard errors from the inverse of the Fisher Information Matrix be a failure?

    + +
    verbose

    Should we print information about created objects of type saemix::SaemixModel and saemix::SaemixData?

    + +
    quiet

    Should we suppress the messages saemix prints at the beginning and the end of the optimisation process?

    + +
    x

    An saem.mmkin object to print

    + +
    digits

    Number of digits to use for printing

    +

    Value

    -

    An S3 object of class 'saem.mmkin', containing the fitted + + +

    An S3 object of class 'saem.mmkin', containing the fitted saemix::SaemixObject as a list component named 'so'. The -object also inherits from 'mixed.mmkin'. -An saemix::SaemixModel object. -An saemix::SaemixData object.

    +object also inherits from 'mixed.mmkin'.

    + + +

    An saemix::SaemixModel object.

    + + +

    An saemix::SaemixData object.

    Details

    @@ -192,109 +229,413 @@ using mmkin.

    Examples

    - 
    # \dontrun{
-ds <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) subset(x$data[c("name", "time", "value")]))
-names(ds) <- paste("Dataset", 6:10)
-f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds,
-  state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
-f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-
-f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
-f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-
-# The returned saem.mmkin object contains an SaemixObject, therefore we can use
-# functions from saemix
-library(saemix)
+    
    # \dontrun{
+ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")]))
+names(ds) <- paste("Dataset", 6:10)
+f_mmkin_parent_p0_fixed <- mmkin("FOMC", ds,
+  state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
+f_saem_p0_fixed <- saem(f_mmkin_parent_p0_fixed)
+
+f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
+f_saem_sfo <- saem(f_mmkin_parent["SFO", ])
+f_saem_fomc <- saem(f_mmkin_parent["FOMC", ])
+f_saem_dfop <- saem(f_mmkin_parent["DFOP", ])
+illparms(f_saem_dfop)
+#> [1] "sd(g_qlogis)"
+update(f_saem_dfop, covariance.model = diag(c(1, 1, 1, 0)))
+#> Kinetic nonlinear mixed-effects model fit by SAEM
+#> Structural model:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> 
+#> Data:
+#> 90 observations of 1 variable(s) grouped in 5 datasets
+#> 
+#> Likelihood computed by importance sampling
+#>     AIC   BIC logLik
+#>   490.6 487.5 -237.3
+#> 
+#> Fitted parameters:
+#>             estimate   lower   upper
+#> parent_0      93.902 91.3695 96.4339
+#> log_k1        -2.936 -3.9950 -1.8762
+#> log_k2        -3.091 -4.9290 -1.2523
+#> g_qlogis      -0.366 -0.6484 -0.0836
+#> a.1            2.385  2.0033  2.7664
+#> SD.parent_0    2.476  0.3890  4.5623
+#> SD.log_k1      1.195  0.4381  1.9517
+#> SD.log_k2      2.092  0.7906  3.3932
+AIC(f_saem_dfop)
+#> [1] 493.9811
+
+# The returned saem.mmkin object contains an SaemixObject, therefore we can use
+# functions from saemix
+library(saemix)
 #> Loading required package: npde
-#> 
-#> Attaching package: ‘npde’
-#> The following object is masked from ‘package:nlmixr’:
-#> 
-#>     warfarin
-#> Package saemix, version 3.0
+#> Package saemix, version 3.1
 #>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
 #> 
 #> Attaching package: ‘saemix’
 #> The following objects are masked from ‘package:npde’:
 #> 
 #>     kurtosis, skewness
-#> The following object is masked from ‘package:RxODE’:
-#> 
-#>     phi
-compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
-#> Error in compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so): object 'f_saem_sfo' not found
-plot(f_saem_fomc$so, plot.type = "convergence")
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
-plot(f_saem_fomc$so, plot.type = "individual.fit")
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
-plot(f_saem_fomc$so, plot.type = "npde")
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
-plot(f_saem_fomc$so, plot.type = "vpc")
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_fomc' not found
-
-f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
-f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
-#> Error in compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so): object 'f_saem_fomc' not found
-
-sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-  A1 = mkinsub("SFO"))
+compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
+#> Likelihoods calculated by importance sampling
+#>        AIC      BIC
+#> 1 624.2598 622.3070
+#> 2 467.8664 465.1324
+#> 3 493.9811 490.4660
+plot(f_saem_fomc$so, plot.type = "convergence")
+
+plot(f_saem_fomc$so, plot.type = "individual.fit")
+
+plot(f_saem_fomc$so, plot.type = "npde")
+#> Simulating data using nsim = 1000 simulated datasets
+#> Computing WRES and npde .
+#> Please use npdeSaemix to obtain VPC and npde
+plot(f_saem_fomc$so, plot.type = "vpc")
+
+
+f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
+f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ])
+compare.saemix(f_saem_fomc$so, f_saem_fomc_tc$so)
+#> Likelihoods calculated by importance sampling
+#>        AIC      BIC
+#> 1 467.8664 465.1324
+#> 2 469.9096 466.7851
+
+sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+  A1 = mkinsub("SFO"))
 #> Temporary DLL for differentials generated and loaded
-fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
-  A1 = mkinsub("SFO"))
+fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+  A1 = mkinsub("SFO"))
 #> Temporary DLL for differentials generated and loaded
-dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-  A1 = mkinsub("SFO"))
+dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+  A1 = mkinsub("SFO"))
 #> Temporary DLL for differentials generated and loaded
-# The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,
-# and compiled ODEs for FOMC that are much slower
-f_mmkin <- mmkin(list(
-    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
-  ds, quiet = TRUE)
-# saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds
-# each on this system, as we use analytical solutions written for saemix.
-# When using the analytical solutions written for mkin this took around
-# four minutes
-f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
-#>  
-#> Error in rxModelVars_(obj): Not compatible with STRSXP: [type=NULL].
-# We can use print, plot and summary methods to check the results
-print(f_saem_dfop_sfo)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'print': object 'f_saem_dfop_sfo' not found
-plot(f_saem_dfop_sfo)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'plot': object 'f_saem_dfop_sfo' not found
-summary(f_saem_dfop_sfo, data = TRUE)
-#> Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'summary': object 'f_saem_dfop_sfo' not found
-
-# The following takes about 6 minutes
-#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
-#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
-
-#saemix::compare.saemix(list(
-#  f_saem_dfop_sfo$so,
-#  f_saem_dfop_sfo_deSolve$so))
-
-# If the model supports it, we can also use eigenvalue based solutions, which
-# take a similar amount of time
-#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",
-#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
-# }
+# The following fit uses analytical solutions for SFO-SFO and DFOP-SFO,
+# and compiled ODEs for FOMC that are much slower
+f_mmkin <- mmkin(list(
+    "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
+  ds, quiet = TRUE)
+# saem fits of SFO-SFO and DFOP-SFO to these data take about five seconds
+# each on this system, as we use analytical solutions written for saemix.
+# When using the analytical solutions written for mkin this took around
+# four minutes
+f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ])
+f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ])
+# We can use print, plot and summary methods to check the results
+print(f_saem_dfop_sfo)
+#> Kinetic nonlinear mixed-effects model fit by SAEM
+#> Structural model:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
+#>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
+#>            exp(-k2 * time))) * parent - k_A1 * A1
+#> 
+#> Data:
+#> 170 observations of 2 variable(s) grouped in 5 datasets
+#> 
+#> Likelihood computed by importance sampling
+#>   AIC   BIC logLik
+#>   842 836.9   -408
+#> 
+#> Fitted parameters:
+#>                    estimate   lower   upper
+#> parent_0            93.7701 91.1458 96.3945
+#> log_k_A1            -5.8116 -7.5998 -4.0234
+#> f_parent_qlogis     -0.9608 -1.3654 -0.5562
+#> log_k1              -2.5841 -3.6876 -1.4805
+#> log_k2              -3.5228 -5.3254 -1.7203
+#> g_qlogis            -0.1027 -0.8719  0.6665
+#> a.1                  1.8856  1.6676  2.1037
+#> SD.parent_0          2.7682  0.7668  4.7695
+#> SD.log_k_A1          1.7447  0.4047  3.0848
+#> SD.f_parent_qlogis   0.4525  0.1620  0.7431
+#> SD.log_k1            1.2423  0.4560  2.0285
+#> SD.log_k2            2.0390  0.7601  3.3180
+#> SD.g_qlogis          0.4439 -0.3069  1.1947
+plot(f_saem_dfop_sfo)
+
+summary(f_saem_dfop_sfo, data = TRUE)
+#> saemix version used for fitting:      3.1 
+#> mkin version used for pre-fitting:  1.1.2 
+#> R version used for fitting:         4.2.1 
+#> Date of fit:     Wed Aug 10 15:27:26 2022 
+#> Date of summary: Wed Aug 10 15:27:26 2022 
+#> 
+#> Equations:
+#> d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 *
+#>            time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time)))
+#>            * parent
+#> d_A1/dt = + f_parent_to_A1 * ((k1 * g * exp(-k1 * time) + k2 * (1 - g)
+#>            * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) *
+#>            exp(-k2 * time))) * parent - k_A1 * A1
+#> 
+#> Data:
+#> 170 observations of 2 variable(s) grouped in 5 datasets
+#> 
+#> Model predictions using solution type analytical 
+#> 
+#> Fitted in 9.438 s
+#> Using 300, 100 iterations and 10 chains
+#> 
+#> Variance model: Constant variance 
+#> 
+#> Mean of starting values for individual parameters:
+#>        parent_0        log_k_A1 f_parent_qlogis          log_k1          log_k2 
+#>         93.8102         -5.3734         -0.9711         -1.8799         -4.2708 
+#>        g_qlogis 
+#>          0.1356 
+#> 
+#> Fixed degradation parameter values:
+#> None
+#> 
+#> Results:
+#> 
+#> Likelihood computed by importance sampling
+#>   AIC   BIC logLik
+#>   842 836.9   -408
+#> 
+#> Optimised parameters:
+#>                    est.   lower   upper
+#> parent_0        93.7701 91.1458 96.3945
+#> log_k_A1        -5.8116 -7.5998 -4.0234
+#> f_parent_qlogis -0.9608 -1.3654 -0.5562
+#> log_k1          -2.5841 -3.6876 -1.4805
+#> log_k2          -3.5228 -5.3254 -1.7203
+#> g_qlogis        -0.1027 -0.8719  0.6665
+#> 
+#> Correlation: 
+#>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2 
+#> log_k_A1        -0.0160                                
+#> f_parent_qlogis -0.0263  0.0612                        
+#> log_k1           0.0100 -0.0014 -0.0033                
+#> log_k2           0.0131  0.0050 -0.0011  0.0071        
+#> g_qlogis        -0.0419 -0.0199  0.0026 -0.0765 -0.0707
+#> 
+#> Random effects:
+#>                      est.   lower  upper
+#> SD.parent_0        2.7682  0.7668 4.7695
+#> SD.log_k_A1        1.7447  0.4047 3.0848
+#> SD.f_parent_qlogis 0.4525  0.1620 0.7431
+#> SD.log_k1          1.2423  0.4560 2.0285
+#> SD.log_k2          2.0390  0.7601 3.3180
+#> SD.g_qlogis        0.4439 -0.3069 1.1947
+#> 
+#> Variance model:
+#>      est. lower upper
+#> a.1 1.886 1.668 2.104
+#> 
+#> Backtransformed parameters:
+#>                     est.     lower    upper
+#> parent_0       93.770115 9.115e+01 96.39447
+#> k_A1            0.002993 5.005e-04  0.01789
+#> f_parent_to_A1  0.276720 2.034e-01  0.36443
+#> k1              0.075467 2.503e-02  0.22753
+#> k2              0.029516 4.867e-03  0.17902
+#> g               0.474353 2.949e-01  0.66073
+#> 
+#> Resulting formation fractions:
+#>                 ff
+#> parent_A1   0.2767
+#> parent_sink 0.7233
+#> 
+#> Estimated disappearance times:
+#>          DT50   DT90 DT50back DT50_k1 DT50_k2
+#> parent  14.56  58.26    17.54   9.185   23.48
+#> A1     231.62 769.41       NA      NA      NA
+#> 
+#> Data:
+#>          ds   name time observed predicted residual   std standardized
+#>   Dataset 6 parent    0     97.2  95.78623  1.41377 1.886     0.749758
+#>   Dataset 6 parent    0     96.4  95.78623  0.61377 1.886     0.325498
+#>   Dataset 6 parent    3     71.1  71.34666 -0.24666 1.886    -0.130812
+#>   Dataset 6 parent    3     69.2  71.34666 -2.14666 1.886    -1.138429
+#>   Dataset 6 parent    6     58.1  56.49768  1.60232 1.886     0.849749
+#>   Dataset 6 parent    6     56.6  56.49768  0.10232 1.886     0.054262
+#>   Dataset 6 parent   10     44.4  44.53511 -0.13511 1.886    -0.071650
+#>   Dataset 6 parent   10     43.4  44.53511 -1.13511 1.886    -0.601974
+#>   Dataset 6 parent   20     33.3  29.77451  3.52549 1.886     1.869656
+#>   Dataset 6 parent   20     29.2  29.77451 -0.57451 1.886    -0.304675
+#>   Dataset 6 parent   34     17.6  19.32540 -1.72540 1.886    -0.915023
+#>   Dataset 6 parent   34     18.0  19.32540 -1.32540 1.886    -0.702894
+#>   Dataset 6 parent   55     10.5  10.42781  0.07219 1.886     0.038282
+#>   Dataset 6 parent   55      9.3  10.42781 -1.12781 1.886    -0.598107
+#>   Dataset 6 parent   90      4.5   3.74190  0.75810 1.886     0.402037
+#>   Dataset 6 parent   90      4.7   3.74190  0.95810 1.886     0.508102
+#>   Dataset 6 parent  112      3.0   1.96485  1.03515 1.886     0.548966
+#>   Dataset 6 parent  112      3.4   1.96485  1.43515 1.886     0.761096
+#>   Dataset 6 parent  132      2.3   1.09395  1.20605 1.886     0.639596
+#>   Dataset 6 parent  132      2.7   1.09395  1.60605 1.886     0.851726
+#>   Dataset 6     A1    3      4.3   4.72702 -0.42702 1.886    -0.226458
+#>   Dataset 6     A1    3      4.6   4.72702 -0.12702 1.886    -0.067361
+#>   Dataset 6     A1    6      7.0   7.51314 -0.51314 1.886    -0.272128
+#>   Dataset 6     A1    6      7.2   7.51314 -0.31314 1.886    -0.166063
+#>   Dataset 6     A1   10      8.2   9.63719 -1.43719 1.886    -0.762179
+#>   Dataset 6     A1   10      8.0   9.63719 -1.63719 1.886    -0.868244
+#>   Dataset 6     A1   20     11.0  11.84931 -0.84931 1.886    -0.450409
+#>   Dataset 6     A1   20     13.7  11.84931  1.85069 1.886     0.981468
+#>   Dataset 6     A1   34     11.5  12.82336 -1.32336 1.886    -0.701808
+#>   Dataset 6     A1   34     12.7  12.82336 -0.12336 1.886    -0.065418
+#>   Dataset 6     A1   55     14.9  12.89456  2.00544 1.886     1.063533
+#>   Dataset 6     A1   55     14.5  12.89456  1.60544 1.886     0.851403
+#>   Dataset 6     A1   90     12.1  11.55919  0.54081 1.886     0.286806
+#>   Dataset 6     A1   90     12.3  11.55919  0.74081 1.886     0.392871
+#>   Dataset 6     A1  112      9.9  10.42334 -0.52334 1.886    -0.277539
+#>   Dataset 6     A1  112     10.2  10.42334 -0.22334 1.886    -0.118442
+#>   Dataset 6     A1  132      8.8   9.37987 -0.57987 1.886    -0.307519
+#>   Dataset 6     A1  132      7.8   9.37987 -1.57987 1.886    -0.837844
+#>   Dataset 7 parent    0     93.6  90.95702  2.64298 1.886     1.401639
+#>   Dataset 7 parent    0     92.3  90.95702  1.34298 1.886     0.712217
+#>   Dataset 7 parent    3     87.0  84.77506  2.22494 1.886     1.179942
+#>   Dataset 7 parent    3     82.2  84.77506 -2.57506 1.886    -1.365616
+#>   Dataset 7 parent    7     74.0  77.60962 -3.60962 1.886    -1.914268
+#>   Dataset 7 parent    7     73.9  77.60962 -3.70962 1.886    -1.967301
+#>   Dataset 7 parent   14     64.2  67.50646 -3.30646 1.886    -1.753499
+#>   Dataset 7 parent   14     69.5  67.50646  1.99354 1.886     1.057221
+#>   Dataset 7 parent   30     54.0  52.48909  1.51091 1.886     0.801271
+#>   Dataset 7 parent   30     54.6  52.48909  2.11091 1.886     1.119465
+#>   Dataset 7 parent   60     41.1  39.54372  1.55628 1.886     0.825335
+#>   Dataset 7 parent   60     38.4  39.54372 -1.14372 1.886    -0.606542
+#>   Dataset 7 parent   90     32.5  33.87968 -1.37968 1.886    -0.731676
+#>   Dataset 7 parent   90     35.5  33.87968  1.62032 1.886     0.859298
+#>   Dataset 7 parent  120     28.1  30.41071 -2.31071 1.886    -1.225427
+#>   Dataset 7 parent  120     29.0  30.41071 -1.41071 1.886    -0.748135
+#>   Dataset 7 parent  180     26.5  25.36386  1.13614 1.886     0.602524
+#>   Dataset 7 parent  180     27.6  25.36386  2.23614 1.886     1.185881
+#>   Dataset 7     A1    3      3.9   2.74863  1.15137 1.886     0.610600
+#>   Dataset 7     A1    3      3.1   2.74863  0.35137 1.886     0.186341
+#>   Dataset 7     A1    7      6.9   5.92686  0.97314 1.886     0.516081
+#>   Dataset 7     A1    7      6.6   5.92686  0.67314 1.886     0.356983
+#>   Dataset 7     A1   14     10.4  10.38800  0.01200 1.886     0.006362
+#>   Dataset 7     A1   14      8.3  10.38800 -2.08800 1.886    -1.107320
+#>   Dataset 7     A1   30     14.4  16.93529 -2.53529 1.886    -1.344524
+#>   Dataset 7     A1   30     13.7  16.93529 -3.23529 1.886    -1.715751
+#>   Dataset 7     A1   60     22.1  22.33044 -0.23044 1.886    -0.122209
+#>   Dataset 7     A1   60     22.3  22.33044 -0.03044 1.886    -0.016144
+#>   Dataset 7     A1   90     27.5  24.42300  3.07700 1.886     1.631809
+#>   Dataset 7     A1   90     25.4  24.42300  0.97700 1.886     0.518127
+#>   Dataset 7     A1  120     28.0  25.51140  2.48860 1.886     1.319768
+#>   Dataset 7     A1  120     26.6  25.51140  1.08860 1.886     0.577313
+#>   Dataset 7     A1  180     25.8  26.80282 -1.00282 1.886    -0.531818
+#>   Dataset 7     A1  180     25.3  26.80282 -1.50282 1.886    -0.796981
+#>   Dataset 8 parent    0     91.9  91.08733  0.81267 1.886     0.430980
+#>   Dataset 8 parent    0     90.8  91.08733 -0.28733 1.886    -0.152377
+#>   Dataset 8 parent    1     64.9  67.55332 -2.65332 1.886    -1.407123
+#>   Dataset 8 parent    1     66.2  67.55332 -1.35332 1.886    -0.717701
+#>   Dataset 8 parent    3     43.5  41.65811  1.84189 1.886     0.976800
+#>   Dataset 8 parent    3     44.1  41.65811  2.44189 1.886     1.294994
+#>   Dataset 8 parent    8     18.3  19.65773 -1.35773 1.886    -0.720038
+#>   Dataset 8 parent    8     18.1  19.65773 -1.55773 1.886    -0.826103
+#>   Dataset 8 parent   14     10.2  10.65118 -0.45118 1.886    -0.239269
+#>   Dataset 8 parent   14     10.8  10.65118  0.14882 1.886     0.078925
+#>   Dataset 8 parent   27      4.9   3.11694  1.78306 1.886     0.945601
+#>   Dataset 8 parent   27      3.3   3.11694  0.18306 1.886     0.097082
+#>   Dataset 8 parent   48      1.6   0.43165  1.16835 1.886     0.619603
+#>   Dataset 8 parent   48      1.5   0.43165  1.06835 1.886     0.566570
+#>   Dataset 8 parent   70      1.1   0.05441  1.04559 1.886     0.554503
+#>   Dataset 8 parent   70      0.9   0.05441  0.84559 1.886     0.448438
+#>   Dataset 8     A1    1      9.6   7.66431  1.93569 1.886     1.026546
+#>   Dataset 8     A1    1      7.7   7.66431  0.03569 1.886     0.018930
+#>   Dataset 8     A1    3     15.0  15.57948 -0.57948 1.886    -0.307311
+#>   Dataset 8     A1    3     15.1  15.57948 -0.47948 1.886    -0.254279
+#>   Dataset 8     A1    8     21.2  20.38988  0.81012 1.886     0.429625
+#>   Dataset 8     A1    8     21.1  20.38988  0.71012 1.886     0.376593
+#>   Dataset 8     A1   14     19.7  20.16439 -0.46439 1.886    -0.246276
+#>   Dataset 8     A1   14     18.9  20.16439 -1.26439 1.886    -0.670535
+#>   Dataset 8     A1   27     17.5  16.40918  1.09082 1.886     0.578489
+#>   Dataset 8     A1   27     15.9  16.40918 -0.50918 1.886    -0.270030
+#>   Dataset 8     A1   48      9.5  10.12011 -0.62011 1.886    -0.328861
+#>   Dataset 8     A1   48      9.8  10.12011 -0.32011 1.886    -0.169764
+#>   Dataset 8     A1   70      6.2   5.79080  0.40920 1.886     0.217011
+#>   Dataset 8     A1   70      6.1   5.79080  0.30920 1.886     0.163979
+#>   Dataset 9 parent    0     99.8  97.38786  2.41214 1.886     1.279218
+#>   Dataset 9 parent    0     98.3  97.38786  0.91214 1.886     0.483731
+#>   Dataset 9 parent    1     77.1  79.25431 -2.15431 1.886    -1.142481
+#>   Dataset 9 parent    1     77.2  79.25431 -2.05431 1.886    -1.089449
+#>   Dataset 9 parent    3     59.0  55.69866  3.30134 1.886     1.750781
+#>   Dataset 9 parent    3     58.1  55.69866  2.40134 1.886     1.273489
+#>   Dataset 9 parent    8     27.4  31.64893 -4.24893 1.886    -2.253314
+#>   Dataset 9 parent    8     29.2  31.64893 -2.44893 1.886    -1.298729
+#>   Dataset 9 parent   14     19.1  22.57316 -3.47316 1.886    -1.841901
+#>   Dataset 9 parent   14     29.6  22.57316  7.02684 1.886     3.726507
+#>   Dataset 9 parent   27     10.1  14.11345 -4.01345 1.886    -2.128430
+#>   Dataset 9 parent   27     18.2  14.11345  4.08655 1.886     2.167199
+#>   Dataset 9 parent   48      4.5   6.95586 -2.45586 1.886    -1.302400
+#>   Dataset 9 parent   48      9.1   6.95586  2.14414 1.886     1.137093
+#>   Dataset 9 parent   70      2.3   3.31753 -1.01753 1.886    -0.539619
+#>   Dataset 9 parent   70      2.9   3.31753 -0.41753 1.886    -0.221424
+#>   Dataset 9 parent   91      2.0   1.63642  0.36358 1.886     0.192816
+#>   Dataset 9 parent   91      1.8   1.63642  0.16358 1.886     0.086751
+#>   Dataset 9 parent  120      2.0   0.61667  1.38333 1.886     0.733614
+#>   Dataset 9 parent  120      2.2   0.61667  1.58333 1.886     0.839679
+#>   Dataset 9     A1    1      4.2   3.67247  0.52753 1.886     0.279763
+#>   Dataset 9     A1    1      3.9   3.67247  0.22753 1.886     0.120666
+#>   Dataset 9     A1    3      7.4   8.36240 -0.96240 1.886    -0.510385
+#>   Dataset 9     A1    3      7.9   8.36240 -0.46240 1.886    -0.245223
+#>   Dataset 9     A1    8     14.5  12.80590  1.69410 1.886     0.898422
+#>   Dataset 9     A1    8     13.7  12.80590  0.89410 1.886     0.474162
+#>   Dataset 9     A1   14     14.2  13.99625  0.20375 1.886     0.108053
+#>   Dataset 9     A1   14     12.2  13.99625 -1.79625 1.886    -0.952596
+#>   Dataset 9     A1   27     13.7  14.22730 -0.52730 1.886    -0.279641
+#>   Dataset 9     A1   27     13.2  14.22730 -1.02730 1.886    -0.544803
+#>   Dataset 9     A1   48     13.6  13.33713  0.26287 1.886     0.139406
+#>   Dataset 9     A1   48     15.4  13.33713  2.06287 1.886     1.093991
+#>   Dataset 9     A1   70     10.4  11.84008 -1.44008 1.886    -0.763708
+#>   Dataset 9     A1   70     11.6  11.84008 -0.24008 1.886    -0.127318
+#>   Dataset 9     A1   91     10.0  10.30732 -0.30732 1.886    -0.162980
+#>   Dataset 9     A1   91      9.5  10.30732 -0.80732 1.886    -0.428142
+#>   Dataset 9     A1  120      9.1   8.33981  0.76019 1.886     0.403149
+#>   Dataset 9     A1  120      9.0   8.33981  0.66019 1.886     0.350117
+#>  Dataset 10 parent    0     96.1  93.70349  2.39651 1.886     1.270926
+#>  Dataset 10 parent    0     94.3  93.70349  0.59651 1.886     0.316342
+#>  Dataset 10 parent    8     73.9  77.86253 -3.96253 1.886    -2.101429
+#>  Dataset 10 parent    8     73.9  77.86253 -3.96253 1.886    -2.101429
+#>  Dataset 10 parent   14     69.4  70.18665 -0.78665 1.886    -0.417182
+#>  Dataset 10 parent   14     73.1  70.18665  2.91335 1.886     1.545019
+#>  Dataset 10 parent   21     65.6  64.03245  1.56755 1.886     0.831308
+#>  Dataset 10 parent   21     65.3  64.03245  1.26755 1.886     0.672210
+#>  Dataset 10 parent   41     55.9  54.71491  1.18509 1.886     0.628480
+#>  Dataset 10 parent   41     54.4  54.71491 -0.31491 1.886    -0.167007
+#>  Dataset 10 parent   63     47.0  49.63436 -2.63436 1.886    -1.397065
+#>  Dataset 10 parent   63     49.3  49.63436 -0.33436 1.886    -0.177319
+#>  Dataset 10 parent   91     44.7  45.08853 -0.38853 1.886    -0.206049
+#>  Dataset 10 parent   91     46.7  45.08853  1.61147 1.886     0.854600
+#>  Dataset 10 parent  120     42.1  41.07653  1.02347 1.886     0.542772
+#>  Dataset 10 parent  120     41.3  41.07653  0.22347 1.886     0.118513
+#>  Dataset 10     A1    8      3.3   4.08295 -0.78295 1.886    -0.415218
+#>  Dataset 10     A1    8      3.4   4.08295 -0.68295 1.886    -0.362186
+#>  Dataset 10     A1   14      3.9   6.04367 -2.14367 1.886    -1.136841
+#>  Dataset 10     A1   14      2.9   6.04367 -3.14367 1.886    -1.667165
+#>  Dataset 10     A1   21      6.4   7.59693 -1.19693 1.886    -0.634761
+#>  Dataset 10     A1   21      7.2   7.59693 -0.39693 1.886    -0.210502
+#>  Dataset 10     A1   41      9.1   9.86436 -0.76436 1.886    -0.405361
+#>  Dataset 10     A1   41      8.5   9.86436 -1.36436 1.886    -0.723555
+#>  Dataset 10     A1   63     11.7  10.99397  0.70603 1.886     0.374425
+#>  Dataset 10     A1   63     12.0  10.99397  1.00603 1.886     0.533522
+#>  Dataset 10     A1   91     13.3  11.91274  1.38726 1.886     0.735696
+#>  Dataset 10     A1   91     13.2  11.91274  1.28726 1.886     0.682663
+#>  Dataset 10     A1  120     14.3  12.66519  1.63481 1.886     0.866981
+#>  Dataset 10     A1  120     12.1  12.66519 -0.56519 1.886    -0.299733
+
+# The following takes about 6 minutes
+#f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
+#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
+
+#saemix::compare.saemix(list(
+#  f_saem_dfop_sfo$so,
+#  f_saem_dfop_sfo_deSolve$so))
+
+# If the model supports it, we can also use eigenvalue based solutions, which
+# take a similar amount of time
+#f_saem_sfo_sfo_eigen <- saem(f_mmkin["SFO-SFO", ], solution_type = "eigen",
+#  control = list(nbiter.saemix = c(200, 80), nbdisplay = 10))
+# }
    @@ -309,7 +650,7 @@ using mmkin.

    -

    Site built with pkgdown 2.0.2.

    +

    Site built with pkgdown 2.0.6.

    -- cgit v1.2.1