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<title>Create saemix models from mmkin row objects — saemix_model • mkin</title>


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<meta property="og:description" content="This function sets up a nonlinear mixed effects model for an mmkin row
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list of mkinfit objects that have been obtained by fitting the same model to
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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='kw'>cores</span> <span class='kw'>=</span> <span class='kw pkg'>parallel</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></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>cores</th>
      <td><p>The number of cores to be used for multicore processing.
On Windows machines, cores &gt; 1 is currently not supported.</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="details"><a class="anchor" href="#details"></a>Details</h2>

    <p>Starting values for the fixed effects (population mean parameters, argument psi0 of
<code><a href='https://rdrr.io/pkg/saemix/man/saemixModel.html'>saemix::saemixModel()</a></code> 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.</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'>&lt;-</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'>&lt;-</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'>&lt;-</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'>#&gt; <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># \dontrun{</span>
<span class='no'>f_mmkin</span> <span class='kw'>&lt;-</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='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>saemix</span>)</div><div class='output co'>#&gt; <span class='message'>Package saemix, version 3.1.9000</span>
#&gt; <span class='message'>  please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span></div><div class='input'><span class='no'>m_saemix</span> <span class='kw'>&lt;-</span> <span class='fu'>saemix_model</span>(<span class='no'>f_mmkin</span>)</div><div class='output co'>#&gt; 
#&gt; 
#&gt; The following SaemixModel object was successfully created:
#&gt; 
#&gt; Nonlinear mixed-effects model
#&gt;   Model function:  Mixed model generated from mmkin object  Model type:  structural
#&gt; function (psi, id, xidep) 
#&gt; {
#&gt;     uid &lt;- unique(id)
#&gt;     res_list &lt;- parallel::mclapply(uid, function(i) {
#&gt;         transparms_optim &lt;- psi[i, ]
#&gt;         names(transparms_optim) &lt;- names(degparms_optim)
#&gt;         odeini_optim &lt;- transparms_optim[odeini_optim_parm_names]
#&gt;         names(odeini_optim) &lt;- gsub("_0$", "", odeini_optim_parm_names)
#&gt;         odeini &lt;- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
#&gt;         ode_transparms_optim_names &lt;- setdiff(names(transparms_optim), 
#&gt;             odeini_optim_parm_names)
#&gt;         odeparms_optim &lt;- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], 
#&gt;             mkin_model, transform_rates = object[[1]]$transform_rates, 
#&gt;             transform_fractions = object[[1]]$transform_fractions)
#&gt;         odeparms &lt;- c(odeparms_optim, odeparms_fixed)
#&gt;         xidep_i &lt;- subset(xidep, id == i)
#&gt;         if (analytical) {
#&gt;             out_values &lt;- mkin_model$deg_func(xidep_i, odeini, 
#&gt;                 odeparms)
#&gt;         }
#&gt;         else {
#&gt;             i_time &lt;- xidep_i$time
#&gt;             i_name &lt;- xidep_i$name
#&gt;             out_wide &lt;- mkinpredict(mkin_model, odeparms = odeparms, 
#&gt;                 odeini = odeini, solution_type = object[[1]]$solution_type, 
#&gt;                 outtimes = sort(unique(i_time)))
#&gt;             out_index &lt;- cbind(as.character(i_time), as.character(i_name))
#&gt;             out_values &lt;- out_wide[out_index]
#&gt;         }
#&gt;         return(out_values)
#&gt;     }, mc.cores = cores)
#&gt;     res &lt;- unlist(res_list)
#&gt;     return(res)
#&gt; }
#&gt; &lt;bytecode: 0x55555bbf17c0&gt;
#&gt; &lt;environment: 0x55555bbec4e0&gt;
#&gt;   Nb of parameters: 4 
#&gt;       parameter names:  parent_0 log_k_parent log_k_A1 f_parent_ilr_1 
#&gt;       distribution:
#&gt;      Parameter      Distribution Estimated
#&gt; [1,] parent_0       normal       Estimated
#&gt; [2,] log_k_parent   normal       Estimated
#&gt; [3,] log_k_A1       normal       Estimated
#&gt; [4,] f_parent_ilr_1 normal       Estimated
#&gt;   Variance-covariance matrix:
#&gt;                parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#&gt; parent_0              1            0        0              0
#&gt; log_k_parent          0            1        0              0
#&gt; log_k_A1              0            0        1              0
#&gt; f_parent_ilr_1        0            0        0              1
#&gt;   Error model: constant , initial values: a.1=1 
#&gt;     No covariate in the model.
#&gt;     Initial values
#&gt;              parent_0 log_k_parent  log_k_A1 f_parent_ilr_1
#&gt; Pop.CondInit 86.53449    -3.207005 -3.060308      -1.920449</div><div class='input'><span class='no'>d_saemix</span> <span class='kw'>&lt;-</span> <span class='fu'>saemix_data</span>(<span class='no'>f_mmkin</span>)</div><div class='output co'>#&gt; 
#&gt; 
#&gt; The following SaemixData object was successfully created:
#&gt; 
#&gt; Object of class SaemixData
#&gt;     longitudinal data for use with the SAEM algorithm
#&gt; Dataset ds_saemix 
#&gt;     Structured data: value ~ time + name | ds 
#&gt;     X variable for graphs: time () </div><div class='input'><span class='no'>saemix_options</span> <span class='kw'>&lt;-</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='kw'>displayProgress</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
  <span class='kw'>nbiter.saemix</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>200</span>, <span class='fl'>80</span>))
<span class='no'>f_saemix</span> <span class='kw'>&lt;-</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'>#&gt; Running main SAEM algorithm
#&gt; [1] "Wed May 27 07:45:07 2020"
#&gt; ..
#&gt;     Minimisation finished
#&gt; [1] "Wed May 27 07:51:24 2020"</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></div><div class='output co'>#&gt; Nonlinear mixed-effects model fit by the SAEM algorithm
#&gt; -----------------------------------
#&gt; ----          Data             ----
#&gt; -----------------------------------
#&gt; Object of class SaemixData
#&gt;     longitudinal data for use with the SAEM algorithm
#&gt; Dataset ds_saemix 
#&gt;     Structured data: value ~ time + name | ds 
#&gt;     X variable for graphs: time () 
#&gt; Dataset characteristics:
#&gt;     number of subjects:     5 
#&gt;     number of observations: 170 
#&gt;     average/min/max nb obs: 34.00  /  30  /  38 
#&gt; First 10 lines of data:
#&gt;           ds time   name value mdv cens occ ytype
#&gt; 1  Dataset 6    0 parent  97.2   0    0   1     1
#&gt; 2  Dataset 6    0 parent  96.4   0    0   1     1
#&gt; 3  Dataset 6    3 parent  71.1   0    0   1     1
#&gt; 4  Dataset 6    3 parent  69.2   0    0   1     1
#&gt; 5  Dataset 6    6 parent  58.1   0    0   1     1
#&gt; 6  Dataset 6    6 parent  56.6   0    0   1     1
#&gt; 7  Dataset 6   10 parent  44.4   0    0   1     1
#&gt; 8  Dataset 6   10 parent  43.4   0    0   1     1
#&gt; 9  Dataset 6   20 parent  33.3   0    0   1     1
#&gt; 10 Dataset 6   20 parent  29.2   0    0   1     1
#&gt; -----------------------------------
#&gt; ----          Model            ----
#&gt; -----------------------------------
#&gt; Nonlinear mixed-effects model
#&gt;   Model function:  Mixed model generated from mmkin object  Model type:  structural
#&gt; function (psi, id, xidep) 
#&gt; {
#&gt;     uid &lt;- unique(id)
#&gt;     res_list &lt;- parallel::mclapply(uid, function(i) {
#&gt;         transparms_optim &lt;- psi[i, ]
#&gt;         names(transparms_optim) &lt;- names(degparms_optim)
#&gt;         odeini_optim &lt;- transparms_optim[odeini_optim_parm_names]
#&gt;         names(odeini_optim) &lt;- gsub("_0$", "", odeini_optim_parm_names)
#&gt;         odeini &lt;- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
#&gt;         ode_transparms_optim_names &lt;- setdiff(names(transparms_optim), 
#&gt;             odeini_optim_parm_names)
#&gt;         odeparms_optim &lt;- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], 
#&gt;             mkin_model, transform_rates = object[[1]]$transform_rates, 
#&gt;             transform_fractions = object[[1]]$transform_fractions)
#&gt;         odeparms &lt;- c(odeparms_optim, odeparms_fixed)
#&gt;         xidep_i &lt;- subset(xidep, id == i)
#&gt;         if (analytical) {
#&gt;             out_values &lt;- mkin_model$deg_func(xidep_i, odeini, 
#&gt;                 odeparms)
#&gt;         }
#&gt;         else {
#&gt;             i_time &lt;- xidep_i$time
#&gt;             i_name &lt;- xidep_i$name
#&gt;             out_wide &lt;- mkinpredict(mkin_model, odeparms = odeparms, 
#&gt;                 odeini = odeini, solution_type = object[[1]]$solution_type, 
#&gt;                 outtimes = sort(unique(i_time)))
#&gt;             out_index &lt;- cbind(as.character(i_time), as.character(i_name))
#&gt;             out_values &lt;- out_wide[out_index]
#&gt;         }
#&gt;         return(out_values)
#&gt;     }, mc.cores = cores)
#&gt;     res &lt;- unlist(res_list)
#&gt;     return(res)
#&gt; }
#&gt; &lt;bytecode: 0x55555bbf17c0&gt;
#&gt; &lt;environment: 0x55555bbec4e0&gt;
#&gt;   Nb of parameters: 4 
#&gt;       parameter names:  parent_0 log_k_parent log_k_A1 f_parent_ilr_1 
#&gt;       distribution:
#&gt;      Parameter      Distribution Estimated
#&gt; [1,] parent_0       normal       Estimated
#&gt; [2,] log_k_parent   normal       Estimated
#&gt; [3,] log_k_A1       normal       Estimated
#&gt; [4,] f_parent_ilr_1 normal       Estimated
#&gt;   Variance-covariance matrix:
#&gt;                parent_0 log_k_parent log_k_A1 f_parent_ilr_1
#&gt; parent_0              1            0        0              0
#&gt; log_k_parent          0            1        0              0
#&gt; log_k_A1              0            0        1              0
#&gt; f_parent_ilr_1        0            0        0              1
#&gt;   Error model: constant , initial values: a.1=1 
#&gt;     No covariate in the model.
#&gt;     Initial values
#&gt;              parent_0 log_k_parent  log_k_A1 f_parent_ilr_1
#&gt; Pop.CondInit 86.53449    -3.207005 -3.060308      -1.920449
#&gt; -----------------------------------
#&gt; ----    Key algorithm options  ----
#&gt; -----------------------------------
#&gt;     Estimation of individual parameters (MAP)
#&gt;     Estimation of standard errors and linearised log-likelihood
#&gt;     Estimation of log-likelihood by importance sampling
#&gt;     Number of iterations:  K1=200, K2=80 
#&gt;     Number of chains:  10 
#&gt;     Seed:  123456 
#&gt;     Number of MCMC iterations for IS:  5000 
#&gt;     Simulations:
#&gt;         nb of simulated datasets used for npde:  1000 
#&gt;         nb of simulated datasets used for VPC:  100 
#&gt;     Input/output
#&gt;         save the results to a file:  FALSE 
#&gt;         save the graphs to files:  FALSE 
#&gt; ----------------------------------------------------
#&gt; ----                  Results                   ----
#&gt; ----------------------------------------------------
#&gt; -----------------  Fixed effects  ------------------
#&gt; ----------------------------------------------------
#&gt;      Parameter      Estimate SE   CV(%)
#&gt; [1,] parent_0       86.14    1.61  1.9 
#&gt; [2,] log_k_parent   -3.21    0.59 18.5 
#&gt; [3,] log_k_A1       -4.66    0.30  6.4 
#&gt; [4,] f_parent_ilr_1 -0.33    0.30 91.7 
#&gt; [5,] a.1             4.68    0.27  5.8 
#&gt; ----------------------------------------------------
#&gt; -----------  Variance of random effects  -----------
#&gt; ----------------------------------------------------
#&gt;                Parameter             Estimate SE   CV(%)
#&gt; parent_0       omega2.parent_0       7.71     8.14 106  
#&gt; log_k_parent   omega2.log_k_parent   1.76     1.12  63  
#&gt; log_k_A1       omega2.log_k_A1       0.26     0.26 101  
#&gt; f_parent_ilr_1 omega2.f_parent_ilr_1 0.39     0.28  71  
#&gt; ----------------------------------------------------
#&gt; ------  Correlation matrix of random effects  ------
#&gt; ----------------------------------------------------
#&gt;                       omega2.parent_0 omega2.log_k_parent omega2.log_k_A1
#&gt; omega2.parent_0       1               0                   0              
#&gt; omega2.log_k_parent   0               1                   0              
#&gt; omega2.log_k_A1       0               0                   1              
#&gt; omega2.f_parent_ilr_1 0               0                   0              
#&gt;                       omega2.f_parent_ilr_1
#&gt; omega2.parent_0       0                    
#&gt; omega2.log_k_parent   0                    
#&gt; omega2.log_k_A1       0                    
#&gt; omega2.f_parent_ilr_1 1                    
#&gt; ----------------------------------------------------
#&gt; ---------------  Statistical criteria  -------------
#&gt; ----------------------------------------------------
#&gt; Likelihood computed by linearisation
#&gt;       -2LL= 1064.364 
#&gt;       AIC = 1082.364 
#&gt;       BIC = 1078.848 
#&gt; 
#&gt; Likelihood computed by importance sampling
#&gt;       -2LL= 1063.462 
#&gt;       AIC = 1081.462 
#&gt;       BIC = 1077.947 
#&gt; ----------------------------------------------------</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span>(<span class='no'>f_saemix</span>, <span class='kw'>plot.type</span> <span class='kw'>=</span> <span class='st'>"convergence"</span>)</div><div class='output co'>#&gt; Plotting convergence plots</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='input'># }
</div></pre>
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