From 17258b37e7f22008298d350a2dc954d71d2fd496 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 9 Nov 2020 09:47:33 +0100 Subject: Remove outdated doc page --- docs/dev/reference/saemix.html | 1202 ---------------------------------------- 1 file changed, 1202 deletions(-) delete mode 100644 docs/dev/reference/saemix.html (limited to 'docs/dev/reference') diff --git a/docs/dev/reference/saemix.html b/docs/dev/reference/saemix.html deleted file mode 100644 index 5dacefc9..00000000 --- a/docs/dev/reference/saemix.html +++ /dev/null @@ -1,1202 +0,0 @@ - - - - - - - - -Create saemix models from mmkin row objects — saemix_model • mkin - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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-

Create saemix models from mmkin row objects

- Source: R/saemix.R - -
- -
-

This function sets up a nonlinear mixed effects model for an mmkin row -object for use with the saemix package. 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.

-
- - 
saemix_model(object, cores = 1)
-
-saemix_data(object, ...)
- -

Arguments

- - - - - - - - - - - - - - -
object

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

cores

The number of cores to be used for multicore processing using -parallel::mclapply(). Using more than 1 core is experimental and may -lead to uncontrolled forking, apparently depending on the BLAS version -used.

...

Further parameters passed to saemix::saemixData

- -

Value

- -

An saemix::SaemixModel object.

-

An saemix::SaemixData object.

-

Details

- -

Starting values for the fixed effects (population mean parameters, argument psi0 of -saemix::saemixModel() are the mean values of the parameters found using -mmkin. Starting variances of the random effects (argument omega.init) are the -variances of the deviations of the parameters from these mean values.

- -

Examples

- 
# \dontrun{
-library(saemix)
-
#> Package saemix, version 3.1.9000
-#>   please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr
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, cores = 1,
-  state.ini = c(parent = 100), fixed_initials = "parent", quiet = TRUE)
-m_saemix_p0_fixed <- saemix_model(f_mmkin_parent_p0_fixed["FOMC", ])
-
#> 
-#> 
-#> The following SaemixModel object was successfully created:
-#> 
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     odeini_fixed/(xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 
-#>         1])
-#> }
-#> <bytecode: 0x5555599945b8>
-#> <environment: 0x555559984388>
-#>   Nb of parameters: 2 
-#>       parameter names:  log_alpha log_beta 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] log_alpha normal       Estimated
-#> [2,] log_beta  normal       Estimated
-#>   Variance-covariance matrix:
-#>           log_alpha log_beta
-#> log_alpha         1        0
-#> log_beta          0        1
-#>   Error model: constant , initial values: a.1=2.95893806804889 
-#>     No covariate in the model.
-#>     Initial values
-#>              log_alpha log_beta
-#> Pop.CondInit -0.347996  1.66788
d_saemix_parent <- saemix_data(f_mmkin_parent_p0_fixed)
-
#> 
-#> 
-#> The following SaemixData object was successfully created:
-#> 
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
saemix_options <- list(seed = 123456, displayProgress = FALSE,
-  save = FALSE, save.graphs = FALSE, nbiter.saemix = c(200, 80))
-f_saemix_p0_fixed <- saemix(m_saemix_p0_fixed, d_saemix_parent, saemix_options)
-
#> Running main SAEM algorithm
-#> [1] "Thu Nov  5 23:53:29 2020"
-#> ..
-#>     Minimisation finished
-#> [1] "Thu Nov  5 23:53:30 2020"
#> Nonlinear mixed-effects model fit by the SAEM algorithm
-#> -----------------------------------
-#> ----          Data             ----
-#> -----------------------------------
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
-#> Dataset characteristics:
-#>     number of subjects:     5 
-#>     number of observations: 90 
-#>     average/min/max nb obs: 18.00  /  16  /  20 
-#> First 10 lines of data:
-#>           ds time   name value mdv cens occ ytype
-#> 1  Dataset 6    0 parent  97.2   0    0   1     1
-#> 2  Dataset 6    0 parent  96.4   0    0   1     1
-#> 3  Dataset 6    3 parent  71.1   0    0   1     1
-#> 4  Dataset 6    3 parent  69.2   0    0   1     1
-#> 5  Dataset 6    6 parent  58.1   0    0   1     1
-#> 6  Dataset 6    6 parent  56.6   0    0   1     1
-#> 7  Dataset 6   10 parent  44.4   0    0   1     1
-#> 8  Dataset 6   10 parent  43.4   0    0   1     1
-#> 9  Dataset 6   20 parent  33.3   0    0   1     1
-#> 10 Dataset 6   20 parent  29.2   0    0   1     1
-#> -----------------------------------
-#> ----          Model            ----
-#> -----------------------------------
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     odeini_fixed/(xidep[, "time"]/exp(psi[id, 2]) + 1)^exp(psi[id, 
-#>         1])
-#> }
-#> <bytecode: 0x5555599945b8>
-#> <environment: 0x555559984388>
-#>   Nb of parameters: 2 
-#>       parameter names:  log_alpha log_beta 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] log_alpha normal       Estimated
-#> [2,] log_beta  normal       Estimated
-#>   Variance-covariance matrix:
-#>           log_alpha log_beta
-#> log_alpha         1        0
-#> log_beta          0        1
-#>   Error model: constant , initial values: a.1=2.95893806804889 
-#>     No covariate in the model.
-#>     Initial values
-#>              log_alpha log_beta
-#> Pop.CondInit -0.347996  1.66788
-#> -----------------------------------
-#> ----    Key algorithm options  ----
-#> -----------------------------------
-#>     Estimation of individual parameters (MAP)
-#>     Estimation of standard errors and linearised log-likelihood
-#>     Estimation of log-likelihood by importance sampling
-#>     Number of iterations:  K1=200, K2=80 
-#>     Number of chains:  10 
-#>     Seed:  123456 
-#>     Number of MCMC iterations for IS:  5000 
-#>     Simulations:
-#>         nb of simulated datasets used for npde:  1000 
-#>         nb of simulated datasets used for VPC:  100 
-#>     Input/output
-#>         save the results to a file:  FALSE 
-#>         save the graphs to files:  FALSE 
-#> ----------------------------------------------------
-#> ----                  Results                   ----
-#> ----------------------------------------------------
-#> -----------------  Fixed effects  ------------------
-#> ----------------------------------------------------
-#>   Parameter Estimate SE   CV(%)
-#>   log_alpha -0.33    0.30 91.6 
-#>   log_beta   1.70    0.21 12.4 
-#> a a.1        3.15    0.25  7.9 
-#> ----------------------------------------------------
-#> -----------  Variance of random effects  -----------
-#> ----------------------------------------------------
-#>           Parameter        Estimate SE   CV(%)
-#> log_alpha omega2.log_alpha 0.44     0.28 65   
-#> log_beta  omega2.log_beta  0.18     0.14 79   
-#> ----------------------------------------------------
-#> ------  Correlation matrix of random effects  ------
-#> ----------------------------------------------------
-#>                  omega2.log_alpha omega2.log_beta
-#> omega2.log_alpha 1                0              
-#> omega2.log_beta  0                1              
-#> ----------------------------------------------------
-#> ---------------  Statistical criteria  -------------
-#> ----------------------------------------------------
-#> Likelihood computed by linearisation
-#>       -2LL= 501.6082 
-#>       AIC = 511.6082 
-#>       BIC = 509.6554 
-#> 
-#> Likelihood computed by importance sampling
-#>       -2LL= 501.7 
-#>       AIC = 511.7 
-#>       BIC = 509.7472 
-#> ----------------------------------------------------

-f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE)
-m_saemix_sfo <- saemix_model(f_mmkin_parent["SFO", ])
-
#> 
-#> 
-#> The following SaemixModel object was successfully created:
-#> 
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     psi[id, 1] * exp(-exp(psi[id, 2]) * xidep[, "time"])
-#> }
-#> <bytecode: 0x55555998d588>
-#> <environment: 0x55555c0f4ae8>
-#>   Nb of parameters: 2 
-#>       parameter names:  parent_0 log_k_parent 
-#>       distribution:
-#>      Parameter    Distribution Estimated
-#> [1,] parent_0     normal       Estimated
-#> [2,] log_k_parent normal       Estimated
-#>   Variance-covariance matrix:
-#>              parent_0 log_k_parent
-#> parent_0            1            0
-#> log_k_parent        0            1
-#>   Error model: constant , initial values: a.1=5.76827561471585 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0 log_k_parent
-#> Pop.CondInit 86.39406    -3.215063
m_saemix_fomc <- saemix_model(f_mmkin_parent["FOMC", ])
-
#> 
-#> 
-#> The following SaemixModel object was successfully created:
-#> 
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     psi[id, 1]/(xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 
-#>         2])
-#> }
-#> <bytecode: 0x55555998dc50>
-#> <environment: 0x5555595d7668>
-#>   Nb of parameters: 3 
-#>       parameter names:  parent_0 log_alpha log_beta 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] parent_0  normal       Estimated
-#> [2,] log_alpha normal       Estimated
-#> [3,] log_beta  normal       Estimated
-#>   Variance-covariance matrix:
-#>           parent_0 log_alpha log_beta
-#> parent_0         1         0        0
-#> log_alpha        0         1        0
-#> log_beta         0         0        1
-#>   Error model: constant , initial values: a.1=1.91976382242696 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0  log_alpha log_beta
-#> Pop.CondInit 94.43884 -0.2226095 2.048192
m_saemix_dfop <- saemix_model(f_mmkin_parent["DFOP", ])
-
#> 
-#> 
-#> The following SaemixModel object was successfully created:
-#> 
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     g <- plogis(psi[id, 4])
-#>     t = xidep[, "time"]
-#>     psi[id, 1] * (g * exp(-exp(psi[id, 2]) * t) + (1 - g) * exp(-exp(psi[id, 
-#>         3]) * t))
-#> }
-#> <bytecode: 0x55555998e548>
-#> <environment: 0x555558225bf0>
-#>   Nb of parameters: 4 
-#>       parameter names:  parent_0 log_k1 log_k2 g_qlogis 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] parent_0  normal       Estimated
-#> [2,] log_k1    normal       Estimated
-#> [3,] log_k2    normal       Estimated
-#> [4,] g_qlogis  normal       Estimated
-#>   Variance-covariance matrix:
-#>          parent_0 log_k1 log_k2 g_qlogis
-#> parent_0        1      0      0        0
-#> log_k1          0      1      0        0
-#> log_k2          0      0      1        0
-#> g_qlogis        0      0      0        1
-#>   Error model: constant , initial values: a.1=1.94671278396371 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0    log_k1    log_k2 g_qlogis
-#> Pop.CondInit 94.08322 -1.834163 -4.210797  0.11002
d_saemix_parent <- saemix_data(f_mmkin_parent["SFO", ])
-
#> 
-#> 
-#> The following SaemixData object was successfully created:
-#> 
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
f_saemix_sfo <- saemix(m_saemix_sfo, d_saemix_parent, saemix_options)
-
#> Running main SAEM algorithm
-#> [1] "Thu Nov  5 23:53:31 2020"
-#> ..
-#>     Minimisation finished
-#> [1] "Thu Nov  5 23:53:32 2020"
#> Nonlinear mixed-effects model fit by the SAEM algorithm
-#> -----------------------------------
-#> ----          Data             ----
-#> -----------------------------------
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
-#> Dataset characteristics:
-#>     number of subjects:     5 
-#>     number of observations: 90 
-#>     average/min/max nb obs: 18.00  /  16  /  20 
-#> First 10 lines of data:
-#>           ds time   name value mdv cens occ ytype
-#> 1  Dataset 6    0 parent  97.2   0    0   1     1
-#> 2  Dataset 6    0 parent  96.4   0    0   1     1
-#> 3  Dataset 6    3 parent  71.1   0    0   1     1
-#> 4  Dataset 6    3 parent  69.2   0    0   1     1
-#> 5  Dataset 6    6 parent  58.1   0    0   1     1
-#> 6  Dataset 6    6 parent  56.6   0    0   1     1
-#> 7  Dataset 6   10 parent  44.4   0    0   1     1
-#> 8  Dataset 6   10 parent  43.4   0    0   1     1
-#> 9  Dataset 6   20 parent  33.3   0    0   1     1
-#> 10 Dataset 6   20 parent  29.2   0    0   1     1
-#> -----------------------------------
-#> ----          Model            ----
-#> -----------------------------------
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     psi[id, 1] * exp(-exp(psi[id, 2]) * xidep[, "time"])
-#> }
-#> <bytecode: 0x55555998d588>
-#> <environment: 0x55555c0f4ae8>
-#>   Nb of parameters: 2 
-#>       parameter names:  parent_0 log_k_parent 
-#>       distribution:
-#>      Parameter    Distribution Estimated
-#> [1,] parent_0     normal       Estimated
-#> [2,] log_k_parent normal       Estimated
-#>   Variance-covariance matrix:
-#>              parent_0 log_k_parent
-#> parent_0            1            0
-#> log_k_parent        0            1
-#>   Error model: constant , initial values: a.1=5.76827561471585 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0 log_k_parent
-#> Pop.CondInit 86.39406    -3.215063
-#> -----------------------------------
-#> ----    Key algorithm options  ----
-#> -----------------------------------
-#>     Estimation of individual parameters (MAP)
-#>     Estimation of standard errors and linearised log-likelihood
-#>     Estimation of log-likelihood by importance sampling
-#>     Number of iterations:  K1=200, K2=80 
-#>     Number of chains:  10 
-#>     Seed:  123456 
-#>     Number of MCMC iterations for IS:  5000 
-#>     Simulations:
-#>         nb of simulated datasets used for npde:  1000 
-#>         nb of simulated datasets used for VPC:  100 
-#>     Input/output
-#>         save the results to a file:  FALSE 
-#>         save the graphs to files:  FALSE 
-#> ----------------------------------------------------
-#> ----                  Results                   ----
-#> ----------------------------------------------------
-#> -----------------  Fixed effects  ------------------
-#> ----------------------------------------------------
-#>   Parameter    Estimate SE   CV(%)
-#>   parent_0     85.8     1.85  2.2 
-#>   log_k_parent -3.2     0.59 18.3 
-#> a a.1           6.2     0.49  7.9 
-#> ----------------------------------------------------
-#> -----------  Variance of random effects  -----------
-#> ----------------------------------------------------
-#>              Parameter           Estimate SE   CV(%)
-#> parent_0     omega2.parent_0     7.8      10.7 138  
-#> log_k_parent omega2.log_k_parent 1.7       1.1  64  
-#> ----------------------------------------------------
-#> ------  Correlation matrix of random effects  ------
-#> ----------------------------------------------------
-#>                     omega2.parent_0 omega2.log_k_parent
-#> omega2.parent_0     1               0                  
-#> omega2.log_k_parent 0               1                  
-#> ----------------------------------------------------
-#> ---------------  Statistical criteria  -------------
-#> ----------------------------------------------------
-#> Likelihood computed by linearisation
-#>       -2LL= 615.4074 
-#>       AIC = 625.4074 
-#>       BIC = 623.4546 
-#> 
-#> Likelihood computed by importance sampling
-#>       -2LL= 614.4911 
-#>       AIC = 624.4911 
-#>       BIC = 622.5382 
-#> ----------------------------------------------------
f_saemix_fomc <- saemix(m_saemix_fomc, d_saemix_parent, saemix_options)
-
#> Running main SAEM algorithm
-#> [1] "Thu Nov  5 23:53:33 2020"
-#> ..
-#>     Minimisation finished
-#> [1] "Thu Nov  5 23:53:34 2020"
#> Nonlinear mixed-effects model fit by the SAEM algorithm
-#> -----------------------------------
-#> ----          Data             ----
-#> -----------------------------------
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
-#> Dataset characteristics:
-#>     number of subjects:     5 
-#>     number of observations: 90 
-#>     average/min/max nb obs: 18.00  /  16  /  20 
-#> First 10 lines of data:
-#>           ds time   name value mdv cens occ ytype
-#> 1  Dataset 6    0 parent  97.2   0    0   1     1
-#> 2  Dataset 6    0 parent  96.4   0    0   1     1
-#> 3  Dataset 6    3 parent  71.1   0    0   1     1
-#> 4  Dataset 6    3 parent  69.2   0    0   1     1
-#> 5  Dataset 6    6 parent  58.1   0    0   1     1
-#> 6  Dataset 6    6 parent  56.6   0    0   1     1
-#> 7  Dataset 6   10 parent  44.4   0    0   1     1
-#> 8  Dataset 6   10 parent  43.4   0    0   1     1
-#> 9  Dataset 6   20 parent  33.3   0    0   1     1
-#> 10 Dataset 6   20 parent  29.2   0    0   1     1
-#> -----------------------------------
-#> ----          Model            ----
-#> -----------------------------------
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     psi[id, 1]/(xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 
-#>         2])
-#> }
-#> <bytecode: 0x55555998dc50>
-#> <environment: 0x5555595d7668>
-#>   Nb of parameters: 3 
-#>       parameter names:  parent_0 log_alpha log_beta 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] parent_0  normal       Estimated
-#> [2,] log_alpha normal       Estimated
-#> [3,] log_beta  normal       Estimated
-#>   Variance-covariance matrix:
-#>           parent_0 log_alpha log_beta
-#> parent_0         1         0        0
-#> log_alpha        0         1        0
-#> log_beta         0         0        1
-#>   Error model: constant , initial values: a.1=1.91976382242696 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0  log_alpha log_beta
-#> Pop.CondInit 94.43884 -0.2226095 2.048192
-#> -----------------------------------
-#> ----    Key algorithm options  ----
-#> -----------------------------------
-#>     Estimation of individual parameters (MAP)
-#>     Estimation of standard errors and linearised log-likelihood
-#>     Estimation of log-likelihood by importance sampling
-#>     Number of iterations:  K1=200, K2=80 
-#>     Number of chains:  10 
-#>     Seed:  123456 
-#>     Number of MCMC iterations for IS:  5000 
-#>     Simulations:
-#>         nb of simulated datasets used for npde:  1000 
-#>         nb of simulated datasets used for VPC:  100 
-#>     Input/output
-#>         save the results to a file:  FALSE 
-#>         save the graphs to files:  FALSE 
-#> ----------------------------------------------------
-#> ----                  Results                   ----
-#> ----------------------------------------------------
-#> -----------------  Fixed effects  ------------------
-#> ----------------------------------------------------
-#>   Parameter Estimate SE   CV(%)
-#>   parent_0  94.49    1.18   1.2
-#>   log_alpha -0.21    0.30 142.0
-#>   log_beta   2.06    0.21  10.4
-#> a a.1        2.28    0.19   8.2
-#> ----------------------------------------------------
-#> -----------  Variance of random effects  -----------
-#> ----------------------------------------------------
-#>           Parameter        Estimate SE   CV(%)
-#> parent_0  omega2.parent_0  4.66     4.34 93   
-#> log_alpha omega2.log_alpha 0.45     0.29 65   
-#> log_beta  omega2.log_beta  0.19     0.14 75   
-#> ----------------------------------------------------
-#> ------  Correlation matrix of random effects  ------
-#> ----------------------------------------------------
-#>                  omega2.parent_0 omega2.log_alpha omega2.log_beta
-#> omega2.parent_0  1               0                0              
-#> omega2.log_alpha 0               1                0              
-#> omega2.log_beta  0               0                1              
-#> ----------------------------------------------------
-#> ---------------  Statistical criteria  -------------
-#> ----------------------------------------------------
-#> Likelihood computed by linearisation
-#>       -2LL= 454.0598 
-#>       AIC = 468.0598 
-#>       BIC = 465.3259 
-#> 
-#> Likelihood computed by importance sampling
-#>       -2LL= 453.7499 
-#>       AIC = 467.7499 
-#>       BIC = 465.016 
-#> ----------------------------------------------------
f_saemix_dfop <- saemix(m_saemix_dfop, d_saemix_parent, saemix_options)
-
#> Running main SAEM algorithm
-#> [1] "Thu Nov  5 23:53:35 2020"
-#> ..
-#>     Minimisation finished
-#> [1] "Thu Nov  5 23:53:37 2020"
#> Nonlinear mixed-effects model fit by the SAEM algorithm
-#> -----------------------------------
-#> ----          Data             ----
-#> -----------------------------------
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
-#> Dataset characteristics:
-#>     number of subjects:     5 
-#>     number of observations: 90 
-#>     average/min/max nb obs: 18.00  /  16  /  20 
-#> First 10 lines of data:
-#>           ds time   name value mdv cens occ ytype
-#> 1  Dataset 6    0 parent  97.2   0    0   1     1
-#> 2  Dataset 6    0 parent  96.4   0    0   1     1
-#> 3  Dataset 6    3 parent  71.1   0    0   1     1
-#> 4  Dataset 6    3 parent  69.2   0    0   1     1
-#> 5  Dataset 6    6 parent  58.1   0    0   1     1
-#> 6  Dataset 6    6 parent  56.6   0    0   1     1
-#> 7  Dataset 6   10 parent  44.4   0    0   1     1
-#> 8  Dataset 6   10 parent  43.4   0    0   1     1
-#> 9  Dataset 6   20 parent  33.3   0    0   1     1
-#> 10 Dataset 6   20 parent  29.2   0    0   1     1
-#> -----------------------------------
-#> ----          Model            ----
-#> -----------------------------------
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     g <- plogis(psi[id, 4])
-#>     t = xidep[, "time"]
-#>     psi[id, 1] * (g * exp(-exp(psi[id, 2]) * t) + (1 - g) * exp(-exp(psi[id, 
-#>         3]) * t))
-#> }
-#> <bytecode: 0x55555998e548>
-#> <environment: 0x555558225bf0>
-#>   Nb of parameters: 4 
-#>       parameter names:  parent_0 log_k1 log_k2 g_qlogis 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] parent_0  normal       Estimated
-#> [2,] log_k1    normal       Estimated
-#> [3,] log_k2    normal       Estimated
-#> [4,] g_qlogis  normal       Estimated
-#>   Variance-covariance matrix:
-#>          parent_0 log_k1 log_k2 g_qlogis
-#> parent_0        1      0      0        0
-#> log_k1          0      1      0        0
-#> log_k2          0      0      1        0
-#> g_qlogis        0      0      0        1
-#>   Error model: constant , initial values: a.1=1.94671278396371 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0    log_k1    log_k2 g_qlogis
-#> Pop.CondInit 94.08322 -1.834163 -4.210797  0.11002
-#> -----------------------------------
-#> ----    Key algorithm options  ----
-#> -----------------------------------
-#>     Estimation of individual parameters (MAP)
-#>     Estimation of standard errors and linearised log-likelihood
-#>     Estimation of log-likelihood by importance sampling
-#>     Number of iterations:  K1=200, K2=80 
-#>     Number of chains:  10 
-#>     Seed:  123456 
-#>     Number of MCMC iterations for IS:  5000 
-#>     Simulations:
-#>         nb of simulated datasets used for npde:  1000 
-#>         nb of simulated datasets used for VPC:  100 
-#>     Input/output
-#>         save the results to a file:  FALSE 
-#>         save the graphs to files:  FALSE 
-#> ----------------------------------------------------
-#> ----                  Results                   ----
-#> ----------------------------------------------------
-#> -----------------  Fixed effects  ------------------
-#> ----------------------------------------------------
-#>   Parameter Estimate SE   CV(%)
-#>   parent_0  93.97    1.35   1.4
-#>   log_k1    -2.37    0.58  24.5
-#>   log_k2    -3.63    0.87  24.0
-#>   g_qlogis  -0.14    0.34 246.1
-#> a a.1        2.32    0.19   8.3
-#> ----------------------------------------------------
-#> -----------  Variance of random effects  -----------
-#> ----------------------------------------------------
-#>          Parameter       Estimate SE   CV(%)
-#> parent_0 omega2.parent_0 6.97     5.72  82  
-#> log_k1   omega2.log_k1   1.63     1.06  65  
-#> log_k2   omega2.log_k2   3.73     2.39  64  
-#> g_qlogis omega2.g_qlogis 0.16     0.27 173  
-#> ----------------------------------------------------
-#> ------  Correlation matrix of random effects  ------
-#> ----------------------------------------------------
-#>                 omega2.parent_0 omega2.log_k1 omega2.log_k2 omega2.g_qlogis
-#> omega2.parent_0 1               0             0             0              
-#> omega2.log_k1   0               1             0             0              
-#> omega2.log_k2   0               0             1             0              
-#> omega2.g_qlogis 0               0             0             1              
-#> ----------------------------------------------------
-#> ---------------  Statistical criteria  -------------
-#> ----------------------------------------------------
-#> Likelihood computed by linearisation
-#>       -2LL= 485.4627 
-#>       AIC = 503.4627 
-#>       BIC = 499.9477 
-#> 
-#> Likelihood computed by importance sampling
-#>       -2LL= 473.563 
-#>       AIC = 491.563 
-#>       BIC = 488.048 
-#> ----------------------------------------------------
compare.saemix(list(f_saemix_sfo, f_saemix_fomc, f_saemix_dfop))
-
#> Likelihoods computed by importance sampling 
#>        AIC      BIC
-#> 1 624.4911 622.5382
-#> 2 467.7499 465.0160
-#> 3 491.5630 488.0480
f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc")
-m_saemix_fomc_tc <- saemix_model(f_mmkin_parent_tc["FOMC", ])
-
#> 
-#> 
-#> The following SaemixModel object was successfully created:
-#> 
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     psi[id, 1]/(xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 
-#>         2])
-#> }
-#> <bytecode: 0x55555998dc50>
-#> <environment: 0x555559a957f8>
-#>   Nb of parameters: 3 
-#>       parameter names:  parent_0 log_alpha log_beta 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] parent_0  normal       Estimated
-#> [2,] log_alpha normal       Estimated
-#> [3,] log_beta  normal       Estimated
-#>   Variance-covariance matrix:
-#>           parent_0 log_alpha log_beta
-#> parent_0         1         0        0
-#> log_alpha        0         1        0
-#> log_beta         0         0        1
-#>   Error model: combined , initial values: a.1=1.10728182011691 b.1=0.024889924291374 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0  log_alpha log_beta
-#> Pop.CondInit 93.13042 -0.1215336 2.230815
f_saemix_fomc_tc <- saemix(m_saemix_fomc_tc, d_saemix_parent, saemix_options)
-
#> Running main SAEM algorithm
-#> [1] "Thu Nov  5 23:53:38 2020"
-#> ..
-#>     Minimisation finished
-#> [1] "Thu Nov  5 23:53:42 2020"
#> Nonlinear mixed-effects model fit by the SAEM algorithm
-#> -----------------------------------
-#> ----          Data             ----
-#> -----------------------------------
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
-#> Dataset characteristics:
-#>     number of subjects:     5 
-#>     number of observations: 90 
-#>     average/min/max nb obs: 18.00  /  16  /  20 
-#> First 10 lines of data:
-#>           ds time   name value mdv cens occ ytype
-#> 1  Dataset 6    0 parent  97.2   0    0   1     1
-#> 2  Dataset 6    0 parent  96.4   0    0   1     1
-#> 3  Dataset 6    3 parent  71.1   0    0   1     1
-#> 4  Dataset 6    3 parent  69.2   0    0   1     1
-#> 5  Dataset 6    6 parent  58.1   0    0   1     1
-#> 6  Dataset 6    6 parent  56.6   0    0   1     1
-#> 7  Dataset 6   10 parent  44.4   0    0   1     1
-#> 8  Dataset 6   10 parent  43.4   0    0   1     1
-#> 9  Dataset 6   20 parent  33.3   0    0   1     1
-#> 10 Dataset 6   20 parent  29.2   0    0   1     1
-#> -----------------------------------
-#> ----          Model            ----
-#> -----------------------------------
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     psi[id, 1]/(xidep[, "time"]/exp(psi[id, 3]) + 1)^exp(psi[id, 
-#>         2])
-#> }
-#> <bytecode: 0x55555998dc50>
-#> <environment: 0x555559a957f8>
-#>   Nb of parameters: 3 
-#>       parameter names:  parent_0 log_alpha log_beta 
-#>       distribution:
-#>      Parameter Distribution Estimated
-#> [1,] parent_0  normal       Estimated
-#> [2,] log_alpha normal       Estimated
-#> [3,] log_beta  normal       Estimated
-#>   Variance-covariance matrix:
-#>           parent_0 log_alpha log_beta
-#> parent_0         1         0        0
-#> log_alpha        0         1        0
-#> log_beta         0         0        1
-#>   Error model: combined , initial values: a.1=1.10728182011691 b.1=0.024889924291374 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0  log_alpha log_beta
-#> Pop.CondInit 93.13042 -0.1215336 2.230815
-#> -----------------------------------
-#> ----    Key algorithm options  ----
-#> -----------------------------------
-#>     Estimation of individual parameters (MAP)
-#>     Estimation of standard errors and linearised log-likelihood
-#>     Estimation of log-likelihood by importance sampling
-#>     Number of iterations:  K1=200, K2=80 
-#>     Number of chains:  10 
-#>     Seed:  123456 
-#>     Number of MCMC iterations for IS:  5000 
-#>     Simulations:
-#>         nb of simulated datasets used for npde:  1000 
-#>         nb of simulated datasets used for VPC:  100 
-#>     Input/output
-#>         save the results to a file:  FALSE 
-#>         save the graphs to files:  FALSE 
-#> ----------------------------------------------------
-#> ----                  Results                   ----
-#> ----------------------------------------------------
-#> -----------------  Fixed effects  ------------------
-#> ----------------------------------------------------
-#>   Parameter Estimate SE     CV(%)
-#>   parent_0  94.4481  1.2052   1.3
-#>   log_alpha -0.2088  0.3059 146.5
-#>   log_beta   2.0668  0.2182  10.6
-#> a a.1        2.4273  0.3178  13.1
-#> b b.1       -0.0037  0.0062 168.3
-#> ----------------------------------------------------
-#> -----------  Variance of random effects  -----------
-#> ----------------------------------------------------
-#>           Parameter        Estimate SE   CV(%)
-#> parent_0  omega2.parent_0  5.34     4.58 86   
-#> log_alpha omega2.log_alpha 0.46     0.29 65   
-#> log_beta  omega2.log_beta  0.20     0.15 74   
-#> ----------------------------------------------------
-#> ------  Correlation matrix of random effects  ------
-#> ----------------------------------------------------
-#>                  omega2.parent_0 omega2.log_alpha omega2.log_beta
-#> omega2.parent_0  1               0                0              
-#> omega2.log_alpha 0               1                0              
-#> omega2.log_beta  0               0                1              
-#> ----------------------------------------------------
-#> ---------------  Statistical criteria  -------------
-#> ----------------------------------------------------
-#> Likelihood computed by linearisation
-#>       -2LL= 453.7703 
-#>       AIC = 469.7703 
-#>       BIC = 466.6458 
-#> 
-#> Likelihood computed by importance sampling
-#>       -2LL= 453.6186 
-#>       AIC = 469.6186 
-#>       BIC = 466.4942 
-#> ----------------------------------------------------
compare.saemix(list(f_saemix_fomc, f_saemix_fomc_tc))
-
#> Likelihoods computed by importance sampling 
#>        AIC      BIC
-#> 1 467.7499 465.0160
-#> 2 469.6186 466.4942

-dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-  A1 = mkinsub("SFO"))
-
#> Successfully compiled differential equation model from auto-generated C code.
f_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo), ds, quiet = TRUE)
-m_saemix <- saemix_model(f_mmkin)
-
#> 
-#> 
-#> The following SaemixModel object was successfully created:
-#> 
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     uid <- unique(id)
-#>     res_list <- parallel::mclapply(uid, function(i) {
-#>         transparms_optim <- psi[i, ]
-#>         names(transparms_optim) <- names(degparms_optim)
-#>         odeini_optim <- transparms_optim[odeini_optim_parm_names]
-#>         names(odeini_optim) <- gsub("_0$", "", odeini_optim_parm_names)
-#>         odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
-#>         ode_transparms_optim_names <- setdiff(names(transparms_optim), 
-#>             odeini_optim_parm_names)
-#>         odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], 
-#>             mkin_model, transform_rates = object[[1]]$transform_rates, 
-#>             transform_fractions = object[[1]]$transform_fractions)
-#>         odeparms <- c(odeparms_optim, odeparms_fixed)
-#>         xidep_i <- subset(xidep, id == i)
-#>         if (solution_type == "analytical") {
-#>             out_values <- mkin_model$deg_func(xidep_i, odeini, 
-#>                 odeparms)
-#>         }
-#>         else {
-#>             i_time <- xidep_i$time
-#>             i_name <- xidep_i$name
-#>             out_wide <- mkinpredict(mkin_model, odeparms = odeparms, 
-#>                 odeini = odeini, solution_type = solution_type, 
-#>                 outtimes = sort(unique(i_time)))
-#>             out_index <- cbind(as.character(i_time), as.character(i_name))
-#>             out_values <- out_wide[out_index]
-#>         }
-#>         return(out_values)
-#>     }, mc.cores = cores)
-#>     res <- unlist(res_list)
-#>     return(res)
-#> }
-#> <bytecode: 0x55555998cba0>
-#> <environment: 0x55555bd1fee8>
-#>   Nb of parameters: 6 
-#>       parameter names:  parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis 
-#>       distribution:
-#>      Parameter       Distribution Estimated
-#> [1,] parent_0        normal       Estimated
-#> [2,] log_k_A1        normal       Estimated
-#> [3,] f_parent_qlogis normal       Estimated
-#> [4,] log_k1          normal       Estimated
-#> [5,] log_k2          normal       Estimated
-#> [6,] g_qlogis        normal       Estimated
-#>   Variance-covariance matrix:
-#>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis
-#> parent_0               1        0               0      0      0        0
-#> log_k_A1               0        1               0      0      0        0
-#> f_parent_qlogis        0        0               1      0      0        0
-#> log_k1                 0        0               0      1      0        0
-#> log_k2                 0        0               0      0      1        0
-#> g_qlogis               0        0               0      0      0        1
-#>   Error model: constant , initial values: a.1=1.64723790168612 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0  log_k_A1 f_parent_qlogis    log_k1    log_k2  g_qlogis
-#> Pop.CondInit 93.81015 -9.764746      -0.9711148 -1.879937 -4.270814 0.1356441
d_saemix <- saemix_data(f_mmkin)
-
#> 
-#> 
-#> The following SaemixData object was successfully created:
-#> 
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
f_saemix <- saemix(m_saemix, d_saemix, saemix_options)
-
#> Running main SAEM algorithm
-#> [1] "Thu Nov  5 23:53:43 2020"
-#> ..
-#>     Minimisation finished
-#> [1] "Thu Nov  5 23:56:33 2020"
#> Nonlinear mixed-effects model fit by the SAEM algorithm
-#> -----------------------------------
-#> ----          Data             ----
-#> -----------------------------------
-#> Object of class SaemixData
-#>     longitudinal data for use with the SAEM algorithm
-#> Dataset ds_saemix 
-#>     Structured data: value ~ time + name | ds 
-#>     X variable for graphs: time () 
-#> Dataset characteristics:
-#>     number of subjects:     5 
-#>     number of observations: 170 
-#>     average/min/max nb obs: 34.00  /  30  /  38 
-#> First 10 lines of data:
-#>           ds time   name value mdv cens occ ytype
-#> 1  Dataset 6    0 parent  97.2   0    0   1     1
-#> 2  Dataset 6    0 parent  96.4   0    0   1     1
-#> 3  Dataset 6    3 parent  71.1   0    0   1     1
-#> 4  Dataset 6    3 parent  69.2   0    0   1     1
-#> 5  Dataset 6    6 parent  58.1   0    0   1     1
-#> 6  Dataset 6    6 parent  56.6   0    0   1     1
-#> 7  Dataset 6   10 parent  44.4   0    0   1     1
-#> 8  Dataset 6   10 parent  43.4   0    0   1     1
-#> 9  Dataset 6   20 parent  33.3   0    0   1     1
-#> 10 Dataset 6   20 parent  29.2   0    0   1     1
-#> -----------------------------------
-#> ----          Model            ----
-#> -----------------------------------
-#> Nonlinear mixed-effects model
-#>   Model function:  Mixed model generated from mmkin object  Model type:  structural
-#> function (psi, id, xidep) 
-#> {
-#>     uid <- unique(id)
-#>     res_list <- parallel::mclapply(uid, function(i) {
-#>         transparms_optim <- psi[i, ]
-#>         names(transparms_optim) <- names(degparms_optim)
-#>         odeini_optim <- transparms_optim[odeini_optim_parm_names]
-#>         names(odeini_optim) <- gsub("_0$", "", odeini_optim_parm_names)
-#>         odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
-#>         ode_transparms_optim_names <- setdiff(names(transparms_optim), 
-#>             odeini_optim_parm_names)
-#>         odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], 
-#>             mkin_model, transform_rates = object[[1]]$transform_rates, 
-#>             transform_fractions = object[[1]]$transform_fractions)
-#>         odeparms <- c(odeparms_optim, odeparms_fixed)
-#>         xidep_i <- subset(xidep, id == i)
-#>         if (solution_type == "analytical") {
-#>             out_values <- mkin_model$deg_func(xidep_i, odeini, 
-#>                 odeparms)
-#>         }
-#>         else {
-#>             i_time <- xidep_i$time
-#>             i_name <- xidep_i$name
-#>             out_wide <- mkinpredict(mkin_model, odeparms = odeparms, 
-#>                 odeini = odeini, solution_type = solution_type, 
-#>                 outtimes = sort(unique(i_time)))
-#>             out_index <- cbind(as.character(i_time), as.character(i_name))
-#>             out_values <- out_wide[out_index]
-#>         }
-#>         return(out_values)
-#>     }, mc.cores = cores)
-#>     res <- unlist(res_list)
-#>     return(res)
-#> }
-#> <bytecode: 0x55555998cba0>
-#> <environment: 0x55555bd1fee8>
-#>   Nb of parameters: 6 
-#>       parameter names:  parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis 
-#>       distribution:
-#>      Parameter       Distribution Estimated
-#> [1,] parent_0        normal       Estimated
-#> [2,] log_k_A1        normal       Estimated
-#> [3,] f_parent_qlogis normal       Estimated
-#> [4,] log_k1          normal       Estimated
-#> [5,] log_k2          normal       Estimated
-#> [6,] g_qlogis        normal       Estimated
-#>   Variance-covariance matrix:
-#>                 parent_0 log_k_A1 f_parent_qlogis log_k1 log_k2 g_qlogis
-#> parent_0               1        0               0      0      0        0
-#> log_k_A1               0        1               0      0      0        0
-#> f_parent_qlogis        0        0               1      0      0        0
-#> log_k1                 0        0               0      1      0        0
-#> log_k2                 0        0               0      0      1        0
-#> g_qlogis               0        0               0      0      0        1
-#>   Error model: constant , initial values: a.1=1.64723790168612 
-#>     No covariate in the model.
-#>     Initial values
-#>              parent_0  log_k_A1 f_parent_qlogis    log_k1    log_k2  g_qlogis
-#> Pop.CondInit 93.81015 -9.764746      -0.9711148 -1.879937 -4.270814 0.1356441
-#> -----------------------------------
-#> ----    Key algorithm options  ----
-#> -----------------------------------
-#>     Estimation of individual parameters (MAP)
-#>     Estimation of standard errors and linearised log-likelihood
-#>     Estimation of log-likelihood by importance sampling
-#>     Number of iterations:  K1=200, K2=80 
-#>     Number of chains:  10 
-#>     Seed:  123456 
-#>     Number of MCMC iterations for IS:  5000 
-#>     Simulations:
-#>         nb of simulated datasets used for npde:  1000 
-#>         nb of simulated datasets used for VPC:  100 
-#>     Input/output
-#>         save the results to a file:  FALSE 
-#>         save the graphs to files:  FALSE 
-#> ----------------------------------------------------
-#> ----                  Results                   ----
-#> ----------------------------------------------------
-#> -----------------  Fixed effects  ------------------
-#> ----------------------------------------------------
-#>   Parameter       Estimate SE   CV(%)
-#>   parent_0        93.78    1.35   1.4
-#>   log_k_A1        -6.05    1.12  18.5
-#>   f_parent_qlogis -0.97    0.20  21.1
-#>   log_k1          -2.46    0.51  20.7
-#>   log_k2          -3.63    0.95  26.3
-#>   g_qlogis        -0.08    0.36 447.7
-#> a a.1              1.88    0.11   5.9
-#> ----------------------------------------------------
-#> -----------  Variance of random effects  -----------
-#> ----------------------------------------------------
-#>                 Parameter              Estimate SE   CV(%)
-#> parent_0        omega2.parent_0        7.85     5.76  73  
-#> log_k_A1        omega2.log_k_A1        4.27     3.44  80  
-#> f_parent_qlogis omega2.f_parent_qlogis 0.20     0.13  65  
-#> log_k1          omega2.log_k1          1.08     0.77  72  
-#> log_k2          omega2.log_k2          4.24     2.83  67  
-#> g_qlogis        omega2.g_qlogis        0.21     0.26 123  
-#> ----------------------------------------------------
-#> ------  Correlation matrix of random effects  ------
-#> ----------------------------------------------------
-#>                        omega2.parent_0 omega2.log_k_A1 omega2.f_parent_qlogis
-#> omega2.parent_0        1               0               0                     
-#> omega2.log_k_A1        0               1               0                     
-#> omega2.f_parent_qlogis 0               0               1                     
-#> omega2.log_k1          0               0               0                     
-#> omega2.log_k2          0               0               0                     
-#> omega2.g_qlogis        0               0               0                     
-#>                        omega2.log_k1 omega2.log_k2 omega2.g_qlogis
-#> omega2.parent_0        0             0             0              
-#> omega2.log_k_A1        0             0             0              
-#> omega2.f_parent_qlogis 0             0             0              
-#> omega2.log_k1          1             0             0              
-#> omega2.log_k2          0             1             0              
-#> omega2.g_qlogis        0             0             1              
-#> ----------------------------------------------------
-#> ---------------  Statistical criteria  -------------
-#> ----------------------------------------------------
-#> Likelihood computed by linearisation
-#>       -2LL= 879.7721 
-#>       AIC = 905.7721 
-#>       BIC = 900.6948 
-#> 
-#> Likelihood computed by importance sampling
-#>       -2LL= 816.8276 
-#>       AIC = 842.8276 
-#>       BIC = 837.7503 
-#> ----------------------------------------------------

-# }
-
-
- -
- - - -
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