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authorJohannes Ranke <jranke@uni-bremen.de>2020-05-27 07:12:51 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2020-05-27 07:12:51 +0200
commitb5ee48a86e4b1d4c05aaadb80b44954e2e994ebc (patch)
treeaeeeaf623e4a6102b9ca2440cf0e32ff7a7a1d25 /docs/reference/saemix.html
parenta77a10ea6c607346778ba0700b3b66ac393101a2 (diff)
Add docs generated using released version 0.9.52
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-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." />
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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: 0x555559668108&gt;
-#&gt; &lt;environment: 0x555559677c08&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] "Tue May 26 18:25:16 2020"
-#&gt; ..
-#&gt; Minimisation finished
-#&gt; [1] "Tue May 26 18:31:09 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: 0x555559668108&gt;
-#&gt; &lt;environment: 0x555559677c08&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'># }
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