From 6476f5f49b373cd4cf05f2e73389df83e437d597 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 13 Feb 2025 16:30:31 +0100 Subject: Axis legend formatting, update vignettes --- docs/dev/reference/saem.html | 779 ------------------------------------------- 1 file changed, 779 deletions(-) delete mode 100644 docs/dev/reference/saem.html (limited to 'docs/dev/reference/saem.html') diff --git a/docs/dev/reference/saem.html b/docs/dev/reference/saem.html deleted file mode 100644 index 9b9a911d..00000000 --- a/docs/dev/reference/saem.html +++ /dev/null @@ -1,779 +0,0 @@ - -Fit nonlinear mixed models with SAEM — saem • mkin - - -
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Fit nonlinear mixed models with SAEM

- Source: R/saem.R - -
- -
-

This function uses saemix::saemix() as a backend for fitting nonlinear mixed -effects models created from mmkin row objects using the Stochastic Approximation -Expectation Maximisation algorithm (SAEM).

-
- -
- 
saem(object, ...)
-
-# S3 method for mmkin
-saem(
-  object,
-  transformations = c("mkin", "saemix"),
-  error_model = "auto",
-  degparms_start = numeric(),
-  test_log_parms = TRUE,
-  conf.level = 0.6,
-  solution_type = "auto",
-  covariance.model = "auto",
-  omega.init = "auto",
-  covariates = NULL,
-  covariate_models = NULL,
-  no_random_effect = NULL,
-  error.init = c(1, 1),
-  nbiter.saemix = c(300, 100),
-  control = list(displayProgress = FALSE, print = FALSE, nbiter.saemix = nbiter.saemix,
-    save = FALSE, save.graphs = FALSE),
-  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"),
-  error_model = "auto",
-  degparms_start = numeric(),
-  covariance.model = "auto",
-  no_random_effect = NULL,
-  omega.init = "auto",
-  covariates = NULL,
-  covariate_models = NULL,
-  error.init = numeric(),
-  test_log_parms = FALSE,
-  conf.level = 0.6,
-  verbose = FALSE,
-  ...
-)
-
-saemix_data(object, covariates = NULL, verbose = FALSE, ...)
-
- -
-

Arguments

-
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, 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.

- - -
error_model
-

Possibility to override the error model used in the mmkin object

- - -
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

- - -
covariance.model
-

Will be passed to saemix::saemixModel(). Per -default, uncorrelated random effects are specified for all degradation -parameters.

- - -
omega.init
-

Will be passed to saemix::saemixModel(). If using -mkin transformations and the default covariance model with optionally -excluded random effects, the variances of the degradation parameters -are estimated using mean_degparms, with testing of untransformed -log parameters for significant difference from zero. If not using -mkin transformations or a custom covariance model, the default -initialisation of saemix::saemixModel is used for omega.init.

- - -
covariates
-

A data frame with covariate data for use in -'covariate_models', with dataset names as row names.

- - -
covariate_models
-

A list containing linear model formulas with one explanatory -variable, i.e. of the type 'parameter ~ covariate'. Covariates must be available -in the 'covariates' data frame.

- - -
no_random_effect
-

Character vector of degradation parameters for -which there should be no variability over the groups. Only used -if the covariance model is not explicitly specified.

- - -
error.init
-

Will be passed to saemix::saemixModel().

- - -
nbiter.saemix
-

Convenience option to increase the number of -iterations

- - -
control
-

Passed to saemix::saemix.

- - -
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 -saemix::SaemixObject as a list component named 'so'. The -object also inherits from 'mixed.mmkin'.

- - -

An saemix::SaemixModel object.

- - -

An saemix::SaemixData object.

-
-
-

Details

-

An mmkin row object is essentially a list of mkinfit objects that have been -obtained by fitting the same model to a list of datasets using mkinfit.

-

Starting values for the fixed effects (population mean parameters, argument -psi0 of saemix::saemixModel() are the mean values of the parameters found -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)
-
-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", ])
-anova(f_saem_sfo, f_saem_fomc, f_saem_dfop)
-#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
-#> 
-#>             npar    AIC    BIC     Lik
-#> f_saem_sfo     5 624.33 622.38 -307.17
-#> f_saem_fomc    7 467.85 465.11 -226.92
-#> f_saem_dfop    9 493.76 490.24 -237.88
-anova(f_saem_sfo, f_saem_dfop, test = TRUE)
-#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
-#> 
-#>             npar    AIC    BIC     Lik  Chisq Df Pr(>Chisq)    
-#> f_saem_sfo     5 624.33 622.38 -307.17                         
-#> f_saem_dfop    9 493.76 490.24 -237.88 138.57  4  < 2.2e-16 ***
-#> ---
-#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-illparms(f_saem_dfop)
-#> [1] "sd(g_qlogis)"
-f_saem_dfop_red <- update(f_saem_dfop, no_random_effect = "g_qlogis")
-anova(f_saem_dfop, f_saem_dfop_red, test = TRUE)
-#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
-#> 
-#>                 npar    AIC    BIC     Lik Chisq Df Pr(>Chisq)
-#> f_saem_dfop_red    8 488.68 485.55 -236.34                    
-#> f_saem_dfop        9 493.76 490.24 -237.88     0  1          1
-
-anova(f_saem_sfo, f_saem_fomc, f_saem_dfop)
-#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
-#> 
-#>             npar    AIC    BIC     Lik
-#> f_saem_sfo     5 624.33 622.38 -307.17
-#> f_saem_fomc    7 467.85 465.11 -226.92
-#> f_saem_dfop    9 493.76 490.24 -237.88
-# The returned saem.mmkin object contains an SaemixObject, therefore we can use
-# functions from saemix
-library(saemix)
-#> Loading required package: npde
-#> Package saemix, version 3.2
-#>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
-#> 
-#> Attaching package: ‘saemix’
-#> The following objects are masked from ‘package:npde’:
-#> 
-#>     kurtosis, skewness
-compare.saemix(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)
-#> Likelihoods calculated by importance sampling
-#>        AIC      BIC
-#> 1 624.3316 622.3788
-#> 2 467.8472 465.1132
-#> 3 493.7592 490.2441
-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", ])
-anova(f_saem_fomc, f_saem_fomc_tc, test = TRUE)
-#> Data: 90 observations of 1 variable(s) grouped in 5 datasets
-#> 
-#>                npar    AIC    BIC     Lik Chisq Df Pr(>Chisq)
-#> f_saem_fomc       7 467.85 465.11 -226.92                    
-#> f_saem_fomc_tc    8 469.83 466.71 -226.92 0.015  1     0.9027
-
-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"))
-#> Temporary DLL for differentials generated and loaded
-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", ])
-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
-#>   839.2 834.1 -406.6
-#> 
-#> Fitted parameters:
-#>                    estimate    lower   upper
-#> parent_0           93.70402 91.04104 96.3670
-#> log_k_A1           -5.83760 -7.66452 -4.0107
-#> f_parent_qlogis    -0.95718 -1.35955 -0.5548
-#> log_k1             -2.35514 -3.39402 -1.3163
-#> log_k2             -3.79634 -5.64009 -1.9526
-#> g_qlogis           -0.02108 -0.66463  0.6225
-#> a.1                 1.88191  1.66491  2.0989
-#> SD.parent_0         2.81628  0.78922  4.8433
-#> SD.log_k_A1         1.78751  0.42105  3.1540
-#> SD.f_parent_qlogis  0.45016  0.16116  0.7391
-#> SD.log_k1           1.06923  0.31676  1.8217
-#> SD.log_k2           2.03768  0.70938  3.3660
-#> SD.g_qlogis         0.44024 -0.09262  0.9731
-plot(f_saem_dfop_sfo)
-
-summary(f_saem_dfop_sfo, data = TRUE)
-#> saemix version used for fitting:      3.2 
-#> mkin version used for pre-fitting:  1.2.3 
-#> R version used for fitting:         4.2.3 
-#> Date of fit:     Sun Apr 16 08:32:32 2023 
-#> Date of summary: Sun Apr 16 08:32:32 2023 
-#> 
-#> 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 4.145 s
-#> Using 300, 100 iterations and 10 chains
-#> 
-#> Variance model: Constant variance 
-#> 
-#> Starting values for degradation 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
-#> 
-#> Starting values for random effects (square root of initial entries in omega):
-#>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis
-#> parent_0           4.941    0.000          0.0000  0.000  0.000   0.0000
-#> log_k_A1           0.000    2.551          0.0000  0.000  0.000   0.0000
-#> f_parent_qlogis    0.000    0.000          0.7251  0.000  0.000   0.0000
-#> log_k1             0.000    0.000          0.0000  1.449  0.000   0.0000
-#> log_k2             0.000    0.000          0.0000  0.000  2.228   0.0000
-#> g_qlogis           0.000    0.000          0.0000  0.000  0.000   0.7814
-#> 
-#> Starting values for error model parameters:
-#> a.1 
-#>   1 
-#> 
-#> Results:
-#> 
-#> Likelihood computed by importance sampling
-#>     AIC   BIC logLik
-#>   839.2 834.1 -406.6
-#> 
-#> Optimised parameters:
-#>                        est.    lower   upper
-#> parent_0           93.70402 91.04104 96.3670
-#> log_k_A1           -5.83760 -7.66452 -4.0107
-#> f_parent_qlogis    -0.95718 -1.35955 -0.5548
-#> log_k1             -2.35514 -3.39402 -1.3163
-#> log_k2             -3.79634 -5.64009 -1.9526
-#> g_qlogis           -0.02108 -0.66463  0.6225
-#> a.1                 1.88191  1.66491  2.0989
-#> SD.parent_0         2.81628  0.78922  4.8433
-#> SD.log_k_A1         1.78751  0.42105  3.1540
-#> SD.f_parent_qlogis  0.45016  0.16116  0.7391
-#> SD.log_k1           1.06923  0.31676  1.8217
-#> SD.log_k2           2.03768  0.70938  3.3660
-#> SD.g_qlogis         0.44024 -0.09262  0.9731
-#> 
-#> Correlation: 
-#>                 parnt_0 lg_k_A1 f_prnt_ log_k1  log_k2 
-#> log_k_A1        -0.0147                                
-#> f_parent_qlogis -0.0269  0.0573                        
-#> log_k1           0.0263 -0.0011 -0.0040                
-#> log_k2           0.0020  0.0065 -0.0002 -0.0776        
-#> g_qlogis        -0.0248 -0.0180 -0.0004 -0.0903 -0.0603
-#> 
-#> Random effects:
-#>                      est.    lower  upper
-#> SD.parent_0        2.8163  0.78922 4.8433
-#> SD.log_k_A1        1.7875  0.42105 3.1540
-#> SD.f_parent_qlogis 0.4502  0.16116 0.7391
-#> SD.log_k1          1.0692  0.31676 1.8217
-#> SD.log_k2          2.0377  0.70938 3.3660
-#> SD.g_qlogis        0.4402 -0.09262 0.9731
-#> 
-#> Variance model:
-#>      est. lower upper
-#> a.1 1.882 1.665 2.099
-#> 
-#> Backtransformed parameters:
-#>                     est.     lower    upper
-#> parent_0       93.704015 9.104e+01 96.36699
-#> k_A1            0.002916 4.692e-04  0.01812
-#> f_parent_to_A1  0.277443 2.043e-01  0.36475
-#> k1              0.094880 3.357e-02  0.26813
-#> k2              0.022453 3.553e-03  0.14191
-#> g               0.494731 3.397e-01  0.65078
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_A1   0.2774
-#> parent_sink 0.7226
-#> 
-#> Estimated disappearance times:
-#>         DT50   DT90 DT50back DT50_k1 DT50_k2
-#> parent  14.0  72.38    21.79   7.306   30.87
-#> A1     237.7 789.68       NA      NA      NA
-#> 
-#> Data:
-#>          ds   name time observed predicted residual   std standardized
-#>   Dataset 6 parent    0     97.2  95.70025  1.49975 1.882      0.79693
-#>   Dataset 6 parent    0     96.4  95.70025  0.69975 1.882      0.37183
-#>   Dataset 6 parent    3     71.1  71.44670 -0.34670 1.882     -0.18423
-#>   Dataset 6 parent    3     69.2  71.44670 -2.24670 1.882     -1.19384
-#>   Dataset 6 parent    6     58.1  56.59283  1.50717 1.882      0.80087
-#>   Dataset 6 parent    6     56.6  56.59283  0.00717 1.882      0.00381
-#>   Dataset 6 parent   10     44.4  44.56648 -0.16648 1.882     -0.08847
-#>   Dataset 6 parent   10     43.4  44.56648 -1.16648 1.882     -0.61984
-#>   Dataset 6 parent   20     33.3  29.76020  3.53980 1.882      1.88096
-#>   Dataset 6 parent   20     29.2  29.76020 -0.56020 1.882     -0.29767
-#>   Dataset 6 parent   34     17.6  19.39208 -1.79208 1.882     -0.95226
-#>   Dataset 6 parent   34     18.0  19.39208 -1.39208 1.882     -0.73971
-#>   Dataset 6 parent   55     10.5  10.55761 -0.05761 1.882     -0.03061
-#>   Dataset 6 parent   55      9.3  10.55761 -1.25761 1.882     -0.66826
-#>   Dataset 6 parent   90      4.5   3.84742  0.65258 1.882      0.34676
-#>   Dataset 6 parent   90      4.7   3.84742  0.85258 1.882      0.45304
-#>   Dataset 6 parent  112      3.0   2.03997  0.96003 1.882      0.51013
-#>   Dataset 6 parent  112      3.4   2.03997  1.36003 1.882      0.72268
-#>   Dataset 6 parent  132      2.3   1.14585  1.15415 1.882      0.61328
-#>   Dataset 6 parent  132      2.7   1.14585  1.55415 1.882      0.82583
-#>   Dataset 6     A1    3      4.3   4.86054 -0.56054 1.882     -0.29786
-#>   Dataset 6     A1    3      4.6   4.86054 -0.26054 1.882     -0.13844
-#>   Dataset 6     A1    6      7.0   7.74179 -0.74179 1.882     -0.39417
-#>   Dataset 6     A1    6      7.2   7.74179 -0.54179 1.882     -0.28789
-#>   Dataset 6     A1   10      8.2   9.94048 -1.74048 1.882     -0.92485
-#>   Dataset 6     A1   10      8.0   9.94048 -1.94048 1.882     -1.03112
-#>   Dataset 6     A1   20     11.0  12.19109 -1.19109 1.882     -0.63291
-#>   Dataset 6     A1   20     13.7  12.19109  1.50891 1.882      0.80180
-#>   Dataset 6     A1   34     11.5  13.10706 -1.60706 1.882     -0.85395
-#>   Dataset 6     A1   34     12.7  13.10706 -0.40706 1.882     -0.21630
-#>   Dataset 6     A1   55     14.9  13.06131  1.83869 1.882      0.97703
-#>   Dataset 6     A1   55     14.5  13.06131  1.43869 1.882      0.76448
-#>   Dataset 6     A1   90     12.1  11.54495  0.55505 1.882      0.29494
-#>   Dataset 6     A1   90     12.3  11.54495  0.75505 1.882      0.40122
-#>   Dataset 6     A1  112      9.9  10.31533 -0.41533 1.882     -0.22070
-#>   Dataset 6     A1  112     10.2  10.31533 -0.11533 1.882     -0.06128
-#>   Dataset 6     A1  132      8.8   9.20222 -0.40222 1.882     -0.21373
-#>   Dataset 6     A1  132      7.8   9.20222 -1.40222 1.882     -0.74510
-#>   Dataset 7 parent    0     93.6  90.82357  2.77643 1.882      1.47532
-#>   Dataset 7 parent    0     92.3  90.82357  1.47643 1.882      0.78453
-#>   Dataset 7 parent    3     87.0  84.73448  2.26552 1.882      1.20384
-#>   Dataset 7 parent    3     82.2  84.73448 -2.53448 1.882     -1.34675
-#>   Dataset 7 parent    7     74.0  77.65013 -3.65013 1.882     -1.93958
-#>   Dataset 7 parent    7     73.9  77.65013 -3.75013 1.882     -1.99272
-#>   Dataset 7 parent   14     64.2  67.60639 -3.40639 1.882     -1.81007
-#>   Dataset 7 parent   14     69.5  67.60639  1.89361 1.882      1.00621
-#>   Dataset 7 parent   30     54.0  52.53663  1.46337 1.882      0.77760
-#>   Dataset 7 parent   30     54.6  52.53663  2.06337 1.882      1.09642
-#>   Dataset 7 parent   60     41.1  39.42728  1.67272 1.882      0.88884
-#>   Dataset 7 parent   60     38.4  39.42728 -1.02728 1.882     -0.54587
-#>   Dataset 7 parent   90     32.5  33.76360 -1.26360 1.882     -0.67144
-#>   Dataset 7 parent   90     35.5  33.76360  1.73640 1.882      0.92268
-#>   Dataset 7 parent  120     28.1  30.39975 -2.29975 1.882     -1.22203
-#>   Dataset 7 parent  120     29.0  30.39975 -1.39975 1.882     -0.74379
-#>   Dataset 7 parent  180     26.5  25.62379  0.87621 1.882      0.46559
-#>   Dataset 7 parent  180     27.6  25.62379  1.97621 1.882      1.05010
-#>   Dataset 7     A1    3      3.9   2.70005  1.19995 1.882      0.63762
-#>   Dataset 7     A1    3      3.1   2.70005  0.39995 1.882      0.21252
-#>   Dataset 7     A1    7      6.9   5.83475  1.06525 1.882      0.56605
-#>   Dataset 7     A1    7      6.6   5.83475  0.76525 1.882      0.40663
-#>   Dataset 7     A1   14     10.4  10.26142  0.13858 1.882      0.07364
-#>   Dataset 7     A1   14      8.3  10.26142 -1.96142 1.882     -1.04225
-#>   Dataset 7     A1   30     14.4  16.82999 -2.42999 1.882     -1.29123
-#>   Dataset 7     A1   30     13.7  16.82999 -3.12999 1.882     -1.66319
-#>   Dataset 7     A1   60     22.1  22.32486 -0.22486 1.882     -0.11949
-#>   Dataset 7     A1   60     22.3  22.32486 -0.02486 1.882     -0.01321
-#>   Dataset 7     A1   90     27.5  24.45927  3.04073 1.882      1.61576
-#>   Dataset 7     A1   90     25.4  24.45927  0.94073 1.882      0.49988
-#>   Dataset 7     A1  120     28.0  25.54862  2.45138 1.882      1.30260
-#>   Dataset 7     A1  120     26.6  25.54862  1.05138 1.882      0.55868
-#>   Dataset 7     A1  180     25.8  26.82277 -1.02277 1.882     -0.54347
-#>   Dataset 7     A1  180     25.3  26.82277 -1.52277 1.882     -0.80916
-#>   Dataset 8 parent    0     91.9  91.16791  0.73209 1.882      0.38901
-#>   Dataset 8 parent    0     90.8  91.16791 -0.36791 1.882     -0.19550
-#>   Dataset 8 parent    1     64.9  67.58358 -2.68358 1.882     -1.42598
-#>   Dataset 8 parent    1     66.2  67.58358 -1.38358 1.882     -0.73520
-#>   Dataset 8 parent    3     43.5  41.62086  1.87914 1.882      0.99853
-#>   Dataset 8 parent    3     44.1  41.62086  2.47914 1.882      1.31735
-#>   Dataset 8 parent    8     18.3  19.60116 -1.30116 1.882     -0.69140
-#>   Dataset 8 parent    8     18.1  19.60116 -1.50116 1.882     -0.79768
-#>   Dataset 8 parent   14     10.2  10.63101 -0.43101 1.882     -0.22903
-#>   Dataset 8 parent   14     10.8  10.63101  0.16899 1.882      0.08980
-#>   Dataset 8 parent   27      4.9   3.12435  1.77565 1.882      0.94354
-#>   Dataset 8 parent   27      3.3   3.12435  0.17565 1.882      0.09334
-#>   Dataset 8 parent   48      1.6   0.43578  1.16422 1.882      0.61864
-#>   Dataset 8 parent   48      1.5   0.43578  1.06422 1.882      0.56550
-#>   Dataset 8 parent   70      1.1   0.05534  1.04466 1.882      0.55510
-#>   Dataset 8 parent   70      0.9   0.05534  0.84466 1.882      0.44883
-#>   Dataset 8     A1    1      9.6   7.63450  1.96550 1.882      1.04442
-#>   Dataset 8     A1    1      7.7   7.63450  0.06550 1.882      0.03481
-#>   Dataset 8     A1    3     15.0  15.52593 -0.52593 1.882     -0.27947
-#>   Dataset 8     A1    3     15.1  15.52593 -0.42593 1.882     -0.22633
-#>   Dataset 8     A1    8     21.2  20.32192  0.87808 1.882      0.46659
-#>   Dataset 8     A1    8     21.1  20.32192  0.77808 1.882      0.41345
-#>   Dataset 8     A1   14     19.7  20.09721 -0.39721 1.882     -0.21107
-#>   Dataset 8     A1   14     18.9  20.09721 -1.19721 1.882     -0.63617
-#>   Dataset 8     A1   27     17.5  16.37477  1.12523 1.882      0.59792
-#>   Dataset 8     A1   27     15.9  16.37477 -0.47477 1.882     -0.25228
-#>   Dataset 8     A1   48      9.5  10.13141 -0.63141 1.882     -0.33551
-#>   Dataset 8     A1   48      9.8  10.13141 -0.33141 1.882     -0.17610
-#>   Dataset 8     A1   70      6.2   5.81827  0.38173 1.882      0.20284
-#>   Dataset 8     A1   70      6.1   5.81827  0.28173 1.882      0.14970
-#>   Dataset 9 parent    0     99.8  97.48728  2.31272 1.882      1.22892
-#>   Dataset 9 parent    0     98.3  97.48728  0.81272 1.882      0.43186
-#>   Dataset 9 parent    1     77.1  79.29476 -2.19476 1.882     -1.16624
-#>   Dataset 9 parent    1     77.2  79.29476 -2.09476 1.882     -1.11310
-#>   Dataset 9 parent    3     59.0  55.67060  3.32940 1.882      1.76915
-#>   Dataset 9 parent    3     58.1  55.67060  2.42940 1.882      1.29092
-#>   Dataset 9 parent    8     27.4  31.57871 -4.17871 1.882     -2.22046
-#>   Dataset 9 parent    8     29.2  31.57871 -2.37871 1.882     -1.26398
-#>   Dataset 9 parent   14     19.1  22.51546 -3.41546 1.882     -1.81489
-#>   Dataset 9 parent   14     29.6  22.51546  7.08454 1.882      3.76454
-#>   Dataset 9 parent   27     10.1  14.09074 -3.99074 1.882     -2.12057
-#>   Dataset 9 parent   27     18.2  14.09074  4.10926 1.882      2.18355
-#>   Dataset 9 parent   48      4.5   6.95747 -2.45747 1.882     -1.30584
-#>   Dataset 9 parent   48      9.1   6.95747  2.14253 1.882      1.13848
-#>   Dataset 9 parent   70      2.3   3.32472 -1.02472 1.882     -0.54451
-#>   Dataset 9 parent   70      2.9   3.32472 -0.42472 1.882     -0.22569
-#>   Dataset 9 parent   91      2.0   1.64300  0.35700 1.882      0.18970
-#>   Dataset 9 parent   91      1.8   1.64300  0.15700 1.882      0.08343
-#>   Dataset 9 parent  120      2.0   0.62073  1.37927 1.882      0.73291
-#>   Dataset 9 parent  120      2.2   0.62073  1.57927 1.882      0.83918
-#>   Dataset 9     A1    1      4.2   3.64568  0.55432 1.882      0.29455
-#>   Dataset 9     A1    1      3.9   3.64568  0.25432 1.882      0.13514
-#>   Dataset 9     A1    3      7.4   8.30173 -0.90173 1.882     -0.47916
-#>   Dataset 9     A1    3      7.9   8.30173 -0.40173 1.882     -0.21347
-#>   Dataset 9     A1    8     14.5  12.71589  1.78411 1.882      0.94803
-#>   Dataset 9     A1    8     13.7  12.71589  0.98411 1.882      0.52293
-#>   Dataset 9     A1   14     14.2  13.90452  0.29548 1.882      0.15701
-#>   Dataset 9     A1   14     12.2  13.90452 -1.70452 1.882     -0.90574
-#>   Dataset 9     A1   27     13.7  14.15523 -0.45523 1.882     -0.24190
-#>   Dataset 9     A1   27     13.2  14.15523 -0.95523 1.882     -0.50759
-#>   Dataset 9     A1   48     13.6  13.31038  0.28962 1.882      0.15389
-#>   Dataset 9     A1   48     15.4  13.31038  2.08962 1.882      1.11037
-#>   Dataset 9     A1   70     10.4  11.85965 -1.45965 1.882     -0.77562
-#>   Dataset 9     A1   70     11.6  11.85965 -0.25965 1.882     -0.13797
-#>   Dataset 9     A1   91     10.0  10.36294 -0.36294 1.882     -0.19286
-#>   Dataset 9     A1   91      9.5  10.36294 -0.86294 1.882     -0.45855
-#>   Dataset 9     A1  120      9.1   8.43003  0.66997 1.882      0.35601
-#>   Dataset 9     A1  120      9.0   8.43003  0.56997 1.882      0.30287
-#>  Dataset 10 parent    0     96.1  93.95603  2.14397 1.882      1.13925
-#>  Dataset 10 parent    0     94.3  93.95603  0.34397 1.882      0.18278
-#>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237
-#>  Dataset 10 parent    8     73.9  77.70592 -3.80592 1.882     -2.02237
-#>  Dataset 10 parent   14     69.4  70.04570 -0.64570 1.882     -0.34311
-#>  Dataset 10 parent   14     73.1  70.04570  3.05430 1.882      1.62298
-#>  Dataset 10 parent   21     65.6  64.01710  1.58290 1.882      0.84111
-#>  Dataset 10 parent   21     65.3  64.01710  1.28290 1.882      0.68170
-#>  Dataset 10 parent   41     55.9  54.98434  0.91566 1.882      0.48656
-#>  Dataset 10 parent   41     54.4  54.98434 -0.58434 1.882     -0.31050
-#>  Dataset 10 parent   63     47.0  49.87137 -2.87137 1.882     -1.52577
-#>  Dataset 10 parent   63     49.3  49.87137 -0.57137 1.882     -0.30361
-#>  Dataset 10 parent   91     44.7  45.06727 -0.36727 1.882     -0.19516
-#>  Dataset 10 parent   91     46.7  45.06727  1.63273 1.882      0.86759
-#>  Dataset 10 parent  120     42.1  40.76402  1.33598 1.882      0.70991
-#>  Dataset 10 parent  120     41.3  40.76402  0.53598 1.882      0.28481
-#>  Dataset 10     A1    8      3.3   4.14599 -0.84599 1.882     -0.44954
-#>  Dataset 10     A1    8      3.4   4.14599 -0.74599 1.882     -0.39640
-#>  Dataset 10     A1   14      3.9   6.08478 -2.18478 1.882     -1.16093
-#>  Dataset 10     A1   14      2.9   6.08478 -3.18478 1.882     -1.69231
-#>  Dataset 10     A1   21      6.4   7.59411 -1.19411 1.882     -0.63452
-#>  Dataset 10     A1   21      7.2   7.59411 -0.39411 1.882     -0.20942
-#>  Dataset 10     A1   41      9.1   9.78292 -0.68292 1.882     -0.36289
-#>  Dataset 10     A1   41      8.5   9.78292 -1.28292 1.882     -0.68171
-#>  Dataset 10     A1   63     11.7  10.93274  0.76726 1.882      0.40770
-#>  Dataset 10     A1   63     12.0  10.93274  1.06726 1.882      0.56711
-#>  Dataset 10     A1   91     13.3  11.93986  1.36014 1.882      0.72274
-#>  Dataset 10     A1   91     13.2  11.93986  1.26014 1.882      0.66961
-#>  Dataset 10     A1  120     14.3  12.79238  1.50762 1.882      0.80111
-#>  Dataset 10     A1  120     12.1  12.79238 -0.69238 1.882     -0.36791
-
-# The following takes about 6 minutes
-f_saem_dfop_sfo_deSolve <- saem(f_mmkin["DFOP-SFO", ], solution_type = "deSolve",
-  nbiter.saemix = c(200, 80))
-#> DINTDY-  T (=R1) illegal      
-#> In above message, R1 = 70
-#>  
-#>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      
-#> In above message, R1 = 53.1122, R2 = 56.6407
-#>  
-#> DINTDY-  T (=R1) illegal      
-#> In above message, R1 = 91
-#>  
-#>       T not in interval TCUR - HU (= R1) to TCUR (=R2)      
-#> In above message, R1 = 53.1122, R2 = 56.6407
-#>  
-#> DLSODA-  Trouble in DINTDY.  ITASK = I1, TOUT = R1
-#> In above message, I1 = 1
-#>  
-#> In above message, R1 = 91
-#>  
-#> Error in deSolve::lsoda(y = odeini, times = outtimes, func = lsoda_func,  : 
-#>   illegal input detected before taking any integration steps - see written message
-
-#anova(
-#  f_saem_dfop_sfo,
-#  f_saem_dfop_sfo_deSolve))
-
-# 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))
-# }
-
-
-
- -
- - -
- - - - - - - - -- cgit v1.2.1