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<h1>Create saemix models from mmkin row objects</h1>
<small class="dont-index">Source: <a href='http://github.com/jranke/mkin/blob/master/R/saemix.R'><code>R/saemix.R</code></a></small>
<div class="hidden name"><code>saemix.Rd</code></div>
</div>
<div class="ref-description">
<p>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.</p>
</div>
<pre class="usage"><span class='fu'>saemix_model</span>(<span class='no'>object</span>)
<span class='fu'>saemix_data</span>(<span class='no'>object</span>, <span class='no'>...</span>)</pre>
<h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
<table class="ref-arguments">
<colgroup><col class="name" /><col class="desc" /></colgroup>
<tr>
<th>object</th>
<td><p>An mmkin row object containing several fits of the same model to different datasets</p></td>
</tr>
<tr>
<th>...</th>
<td><p>Further parameters passed to <a href='https://rdrr.io/pkg/saemix/man/saemixData.html'>saemix::saemixData</a></p></td>
</tr>
</table>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>An <a href='https://rdrr.io/pkg/saemix/man/SaemixModel-class.html'>saemix::SaemixModel</a> object.</p>
<p>An <a href='https://rdrr.io/pkg/saemix/man/SaemixData-class.html'>saemix::SaemixData</a> object.</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><div class='input'><span class='no'>ds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>],
<span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)]))
<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>ds</span>) <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>6</span>:<span class='fl'>10</span>)
<span class='no'>sfo_sfo</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"A1"</span>),
<span class='kw'>A1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f_mmkin</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='st'>"SFO-SFO"</span> <span class='kw'>=</span> <span class='no'>sfo_sfo</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>5</span>)
<span class='co'># \dontrun{</span>
<span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>saemix</span>)) {
<span class='no'>m_saemix</span> <span class='kw'><-</span> <span class='fu'>saemix_model</span>(<span class='no'>f_mmkin</span>)
<span class='no'>d_saemix</span> <span class='kw'><-</span> <span class='fu'>saemix_data</span>(<span class='no'>f_mmkin</span>)
<span class='no'>saemix_options</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>seed</span> <span class='kw'>=</span> <span class='fl'>123456</span>, <span class='kw'>save</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>save.graphs</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)
<span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span>(<span class='no'>m_saemix</span>, <span class='no'>d_saemix</span>, <span class='no'>saemix_options</span>)
}</div><div class='output co'>#> <span class='message'>Loading required package: saemix</span></div><div class='output co'>#> <span class='message'>Package saemix, version 3.1.9000</span>
#> <span class='message'> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span></div><div class='output co'>#>
#>
#> 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]]$transform_fractions)
#> odeparms <- c(odeparms_optim, odeparms_fixed)
#> xidep_i <- subset(xidep, id == i)
#> if (analytical) {
#> out_values <- mkin_model$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 = object[[1]]$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 = 15)
#> res <- unlist(res_list)
#> return(res)
#> }
#> <bytecode: 0x555559875398>
#> <environment: 0x55555973a248>
#> Nb of parameters: 4
#> parameter names: parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> distribution:
#> Parameter Distribution Estimated
#> [1,] parent_0 normal Estimated
#> [2,] log_k_parent normal Estimated
#> [3,] log_k_A1 normal Estimated
#> [4,] f_parent_ilr_1 normal Estimated
#> Variance-covariance matrix:
#> parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> parent_0 1 0 0 0
#> log_k_parent 0 1 0 0
#> log_k_A1 0 0 1 0
#> f_parent_ilr_1 0 0 0 1
#> Error model: constant , initial values: a.1=1
#> No covariate in the model.
#> Initial values
#> parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> Pop.CondInit 86.53449 -3.207005 -3.060308 -1.920449
#>
#>
#> 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 ()
#> Running main SAEM algorithm
#> [1] "Mon May 25 12:48:51 2020"
#> .</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></div><div class='output co'>#> .</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='output co'>#> .</div><div class='img'><img src='saemix-3.png' alt='' width='700' height='433' /></div><div class='output co'>#> .</div><div class='img'><img src='saemix-4.png' alt='' width='700' height='433' /></div><div class='output co'>#>
#> Minimisation finished
#> [1] "Mon May 25 12:56:39 2020"</div><div class='output co'>#> 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]]$transform_fractions)
#> odeparms <- c(odeparms_optim, odeparms_fixed)
#> xidep_i <- subset(xidep, id == i)
#> if (analytical) {
#> out_values <- mkin_model$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 = object[[1]]$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 = 15)
#> res <- unlist(res_list)
#> return(res)
#> }
#> <bytecode: 0x555559875398>
#> <environment: 0x55555973a248>
#> Nb of parameters: 4
#> parameter names: parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> distribution:
#> Parameter Distribution Estimated
#> [1,] parent_0 normal Estimated
#> [2,] log_k_parent normal Estimated
#> [3,] log_k_A1 normal Estimated
#> [4,] f_parent_ilr_1 normal Estimated
#> Variance-covariance matrix:
#> parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> parent_0 1 0 0 0
#> log_k_parent 0 1 0 0
#> log_k_A1 0 0 1 0
#> f_parent_ilr_1 0 0 0 1
#> Error model: constant , initial values: a.1=1
#> No covariate in the model.
#> Initial values
#> parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> Pop.CondInit 86.53449 -3.207005 -3.060308 -1.920449
#> -----------------------------------
#> ---- 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=300, K2=100
#> 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(%)
#> [1,] parent_0 86.21 1.51 1.7
#> [2,] log_k_parent -3.21 0.59 18.5
#> [3,] log_k_A1 -4.64 0.29 6.3
#> [4,] f_parent_ilr_1 -0.32 0.30 93.2
#> [5,] a.1 4.69 0.27 5.8
#> ----------------------------------------------------
#> ----------- Variance of random effects -----------
#> ----------------------------------------------------
#> Parameter Estimate SE CV(%)
#> parent_0 omega2.parent_0 6.07 7.08 117
#> log_k_parent omega2.log_k_parent 1.75 1.11 63
#> log_k_A1 omega2.log_k_A1 0.26 0.26 101
#> f_parent_ilr_1 omega2.f_parent_ilr_1 0.38 0.27 71
#> ----------------------------------------------------
#> ------ Correlation matrix of random effects ------
#> ----------------------------------------------------
#> omega2.parent_0 omega2.log_k_parent omega2.log_k_A1
#> omega2.parent_0 1 0 0
#> omega2.log_k_parent 0 1 0
#> omega2.log_k_A1 0 0 1
#> omega2.f_parent_ilr_1 0 0 0
#> omega2.f_parent_ilr_1
#> omega2.parent_0 0
#> omega2.log_k_parent 0
#> omega2.log_k_A1 0
#> omega2.f_parent_ilr_1 1
#> ----------------------------------------------------
#> --------------- Statistical criteria -------------
#> ----------------------------------------------------
#> Likelihood computed by linearisation
#> -2LL= 1064.397
#> AIC = 1082.397
#> BIC = 1078.882
#>
#> Likelihood computed by importance sampling
#> -2LL= 1063.161
#> AIC = 1081.161
#> BIC = 1077.646
#> ----------------------------------------------------</div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm
#> -----------------------------------------
#> ---- Data and Model ----
#> -----------------------------------------
#> Data
#> Dataset ds_saemix
#> Longitudinal data: value ~ time + name | ds
#>
#> Model:
#> Mixed model generated from mmkin object
#> 4 parameters: parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#> error model: constant
#> No covariate
#>
#> Key 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=300, K2=100
#> Number of chains: 10
#> Seed: 123456
#> Number of MCMC iterations for IS: 5000
#> Input/output
#> results not saved
#> no graphs
#> ----------------------------------------------------
#> ---- Results ----
#> Fixed effects
#> Parameter Estimate SE CV(%)
#> parent_0 86.214 1.506 1.75
#> log_k_parent -3.210 0.593 18.47
#> log_k_A1 -4.643 0.294 6.34
#> f_parent_ilr_1 -0.322 0.300 93.24
#> a.1 4.689 0.270 5.76
#>
#> Variance of random effects
#> Parameter Estimate SE CV(%)
#> omega2.parent_0 6.068 7.078 116.7
#> omega2.log_k_parent 1.752 1.111 63.4
#> omega2.log_k_A1 0.256 0.257 100.5
#> omega2.f_parent_ilr_1 0.385 0.273 70.8
#>
#> Statistical criteria
#> Likelihood computed by linearisation
#> -2LL= 1064.397
#> AIC= 1082.397
#> BIC= 1078.882
#> Likelihood computed by importance sampling
#> -2LL= 1063.161
#> AIC= 1081.161
#> BIC= 1077.646 </div><div class='input'># }
</div><div class='img'><img src='saemix-5.png' alt='' width='700' height='433' /></div></pre>
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