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


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<meta property="og:title" content="Create saemix models from mmkin row objects — saemix_model" />
<meta property="og:description" content="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
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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'>&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='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='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'>&lt;-</span> <span class='fu'>saemix_model</span>(<span class='no'>f_mmkin</span>)
  <span class='no'>d_saemix</span> <span class='kw'>&lt;-</span> <span class='fu'>saemix_data</span>(<span class='no'>f_mmkin</span>)
  <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='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; <span class='message'>Loading required package: 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='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 = 15)
#&gt;     res &lt;- unlist(res_list)
#&gt;     return(res)
#&gt; }
#&gt; &lt;bytecode: 0x555559875398&gt;
#&gt; &lt;environment: 0x55555973a248&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; 
#&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 () 
#&gt; Running main SAEM algorithm
#&gt; [1] "Mon May 25 12:48:51 2020"
#&gt; .</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></div><div class='output co'>#&gt; .</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='output co'>#&gt; .</div><div class='img'><img src='saemix-3.png' alt='' width='700' height='433' /></div><div class='output co'>#&gt; .</div><div class='img'><img src='saemix-4.png' alt='' width='700' height='433' /></div><div class='output co'>#&gt; 
#&gt;     Minimisation finished
#&gt; [1] "Mon May 25 12:56:39 2020"</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 = 15)
#&gt;     res &lt;- unlist(res_list)
#&gt;     return(res)
#&gt; }
#&gt; &lt;bytecode: 0x555559875398&gt;
#&gt; &lt;environment: 0x55555973a248&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=300, K2=100 
#&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       86.21    1.51  1.7 
#&gt; [2,] log_k_parent   -3.21    0.59 18.5 
#&gt; [3,] log_k_A1       -4.64    0.29  6.3 
#&gt; [4,] f_parent_ilr_1 -0.32    0.30 93.2 
#&gt; [5,] a.1             4.69    0.27  5.8 
#&gt; ----------------------------------------------------
#&gt; -----------  Variance of random effects  -----------
#&gt; ----------------------------------------------------
#&gt;                Parameter             Estimate SE   CV(%)
#&gt; parent_0       omega2.parent_0       6.07     7.08 117  
#&gt; log_k_parent   omega2.log_k_parent   1.75     1.11  63  
#&gt; log_k_A1       omega2.log_k_A1       0.26     0.26 101  
#&gt; f_parent_ilr_1 omega2.f_parent_ilr_1 0.38     0.27  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= 1064.397 
#&gt;       AIC = 1082.397 
#&gt;       BIC = 1078.882 
#&gt; 
#&gt; Likelihood computed by importance sampling
#&gt;       -2LL= 1063.161 
#&gt;       AIC = 1081.161 
#&gt;       BIC = 1077.646 
#&gt; ----------------------------------------------------</div><div class='output co'>#&gt; Nonlinear mixed-effects model fit by the SAEM algorithm
#&gt; -----------------------------------------
#&gt; ----         Data and Model          ----
#&gt; -----------------------------------------
#&gt; Data
#&gt;     Dataset ds_saemix 
#&gt;     Longitudinal data: value ~ time + name | ds 
#&gt; 
#&gt; Model:
#&gt;     Mixed model generated from mmkin object 
#&gt;      4 parameters: parent_0 log_k_parent log_k_A1 f_parent_ilr_1 
#&gt;      error model: constant 
#&gt;      No covariate
#&gt; 
#&gt; Key options
#&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=300, K2=100 
#&gt;     Number of chains:  10 
#&gt;     Seed:  123456 
#&gt;     Number of MCMC iterations for IS:  5000 
#&gt;     Input/output
#&gt;         results not saved
#&gt;         no graphs
#&gt; ----------------------------------------------------
#&gt; ----                  Results                   ----
#&gt; Fixed effects
#&gt;  Parameter      Estimate   SE   CV(%)
#&gt;  parent_0       86.214   1.506  1.75 
#&gt;  log_k_parent   -3.210   0.593 18.47 
#&gt;  log_k_A1       -4.643   0.294  6.34 
#&gt;  f_parent_ilr_1 -0.322   0.300 93.24 
#&gt;  a.1             4.689   0.270  5.76 
#&gt; 
#&gt; Variance of random effects
#&gt;  Parameter             Estimate   SE   CV(%)
#&gt;  omega2.parent_0       6.068    7.078 116.7 
#&gt;  omega2.log_k_parent   1.752    1.111  63.4 
#&gt;  omega2.log_k_A1       0.256    0.257 100.5 
#&gt;  omega2.f_parent_ilr_1 0.385    0.273  70.8 
#&gt; 
#&gt; Statistical criteria
#&gt; Likelihood computed by linearisation
#&gt;       -2LL= 1064.397 
#&gt;        AIC= 1082.397 
#&gt;        BIC= 1078.882 
#&gt; Likelihood computed by importance sampling
#&gt;       -2LL= 1063.161 
#&gt;        AIC= 1081.161 
#&gt;        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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