diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2020-11-06 00:03:29 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2020-11-06 00:03:29 +0100 |
| commit | b5b446b718b15ccaae5b197e147fc1358f0f564e (patch) | |
| tree | a36f32ee664c6925b5afdb812daca41075968152 /docs/dev/reference/saemix.html | |
| parent | 2f24fe0ce70d040e491619d7f2413fc902e433f1 (diff) | |
Fast analytical solutions for saemix, update.mmkin
Also, use logit transformation for g and for solitary formation
fractions, addressing #10.
Diffstat (limited to 'docs/dev/reference/saemix.html')
| -rw-r--r-- | docs/dev/reference/saemix.html | 974 |
1 files changed, 748 insertions, 226 deletions
diff --git a/docs/dev/reference/saemix.html b/docs/dev/reference/saemix.html index c8cf9fab..5dacefc9 100644 --- a/docs/dev/reference/saemix.html +++ b/docs/dev/reference/saemix.html @@ -190,16 +190,15 @@ 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='va'>ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>, - <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span><span class='op'>)</span><span class='op'>]</span><span class='op'>)</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Dataset"</span>, <span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>)</span> -<span class='va'>sfo_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, - A1 <span class='op'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</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='co'># \dontrun{</span> -<span class='va'>f_mmkin</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"SFO-SFO"</span> <span class='op'>=</span> <span class='va'>sfo_sfo</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> + <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> <span class='kw'><a href='https://rdrr.io/r/base/library.html'>library</a></span><span class='op'>(</span><span class='va'>saemix</span><span class='op'>)</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='input'><span class='va'>m_saemix</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> +#> <span class='message'> please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr</span></div><div class='input'><span class='va'>ds</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>experimental_data_for_UBA_2019</span><span class='op'>[</span><span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>]</span>, + <span class='kw'>function</span><span class='op'>(</span><span class='va'>x</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>x</span><span class='op'>$</span><span class='va'>data</span><span class='op'>[</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span><span class='op'>)</span><span class='op'>]</span><span class='op'>)</span><span class='op'>)</span> +<span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span><span class='op'>(</span><span class='st'>"Dataset"</span>, <span class='fl'>6</span><span class='op'>:</span><span class='fl'>10</span><span class='op'>)</span> +<span class='va'>f_mmkin_parent_p0_fixed</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='st'>"FOMC"</span>, <span class='va'>ds</span>, cores <span class='op'>=</span> <span class='fl'>1</span>, + state.ini <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span><span class='op'>)</span>, fixed_initials <span class='op'>=</span> <span class='st'>"parent"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='va'>m_saemix_p0_fixed</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin_parent_p0_fixed</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> #> #> The following SaemixModel object was successfully created: @@ -208,59 +207,236 @@ variances of the deviations of the parameters from these mean values.</p> #> 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 = cores) -#> res <- unlist(res_list) -#> return(res) +#> 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</div><div class='input'><span class='va'>d_saemix_parent</span> <span class='op'><-</span> <span class='fu'>saemix_data</span><span class='op'>(</span><span class='va'>f_mmkin_parent_p0_fixed</span><span class='op'>)</span> +</div><div class='output co'>#> +#> +#> 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 () </div><div class='input'><span class='va'>saemix_options</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>seed <span class='op'>=</span> <span class='fl'>123456</span>, displayProgress <span class='op'>=</span> <span class='cn'>FALSE</span>, + save <span class='op'>=</span> <span class='cn'>FALSE</span>, save.graphs <span class='op'>=</span> <span class='cn'>FALSE</span>, nbiter.saemix <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>200</span>, <span class='fl'>80</span><span class='op'>)</span><span class='op'>)</span> +<span class='va'>f_saemix_p0_fixed</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix_p0_fixed</span>, <span class='va'>d_saemix_parent</span>, <span class='va'>saemix_options</span><span class='op'>)</span> +</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Thu Nov 5 23:53:29 2020" +#> .. +#> Minimisation finished +#> [1] "Thu Nov 5 23:53:30 2020"</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></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: 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 +#> ----------------------------------------------------</div><div class='input'> +<span class='va'>f_mmkin_parent</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='va'>m_saemix_sfo</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='op'>]</span><span class='op'>)</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) +#> { +#> psi[id, 1] * exp(-exp(psi[id, 2]) * xidep[, "time"]) #> } -#> <bytecode: 0x55555d62aeb8> -#> <environment: 0x55555e35c170> +#> <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</div><div class='input'><span class='va'>m_saemix_fomc</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</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) +#> { +#> 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</div><div class='input'><span class='va'>m_saemix_dfop</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"DFOP"</span>, <span class='op'>]</span><span class='op'>)</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) +#> { +#> 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_k_parent log_k_A1 f_parent_ilr_1 +#> parameter names: parent_0 log_k1 log_k2 g_qlogis #> 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 +#> 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_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=4.97259024646577 +#> 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_k_parent log_k_A1 f_parent_ilr_1 -#> Pop.CondInit 86.53449 -3.207005 -3.060308 -1.920449</div><div class='input'><span class='va'>d_saemix</span> <span class='op'><-</span> <span class='fu'>saemix_data</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span> +#> parent_0 log_k1 log_k2 g_qlogis +#> Pop.CondInit 94.08322 -1.834163 -4.210797 0.11002</div><div class='input'><span class='va'>d_saemix_parent</span> <span class='op'><-</span> <span class='fu'>saemix_data</span><span class='op'>(</span><span class='va'>f_mmkin_parent</span><span class='op'>[</span><span class='st'>"SFO"</span>, <span class='op'>]</span><span class='op'>)</span> </div><div class='output co'>#> #> #> The following SaemixData object was successfully created: @@ -269,15 +445,12 @@ variances of the deviations of the parameters from these mean values.</p> #> longitudinal data for use with the SAEM algorithm #> Dataset ds_saemix #> Structured data: value ~ time + name | ds -#> X variable for graphs: time () </div><div class='input'><span class='va'>saemix_options</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>seed <span class='op'>=</span> <span class='fl'>123456</span>, - save <span class='op'>=</span> <span class='cn'>FALSE</span>, save.graphs <span class='op'>=</span> <span class='cn'>FALSE</span>, displayProgress <span class='op'>=</span> <span class='cn'>FALSE</span>, - nbiter.saemix <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>200</span>, <span class='fl'>80</span><span class='op'>)</span><span class='op'>)</span> -<span class='va'>f_saemix</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix</span>, <span class='va'>d_saemix</span>, <span class='va'>saemix_options</span><span class='op'>)</span> +#> X variable for graphs: time () </div><div class='input'><span class='va'>f_saemix_sfo</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix_sfo</span>, <span class='va'>d_saemix_parent</span>, <span class='va'>saemix_options</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Thu Nov 5 08:26:39 2020" +#> [1] "Thu Nov 5 23:53:31 2020" #> .. #> Minimisation finished -#> [1] "Thu Nov 5 08:28:33 2020"</div><div class='img'><img src='saemix-1.png' alt='' width='700' height='433' /></div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm +#> [1] "Thu Nov 5 23:53:32 2020"</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm #> ----------------------------------- #> ---- Data ---- #> ----------------------------------- @@ -288,8 +461,8 @@ variances of the deviations of the parameters from these mean values.</p> #> X variable for graphs: time () #> Dataset characteristics: #> number of subjects: 5 -#> number of observations: 170 -#> average/min/max nb obs: 34.00 / 30 / 38 +#> 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 @@ -309,59 +482,248 @@ variances of the deviations of the parameters from these mean values.</p> #> 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 = cores) -#> res <- unlist(res_list) -#> return(res) +#> psi[id, 1] * exp(-exp(psi[id, 2]) * xidep[, "time"]) #> } -#> <bytecode: 0x55555d62aeb8> -#> <environment: 0x55555e35c170> +#> <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 +#> ----------------------------------------------------</div><div class='input'><span class='va'>f_saemix_fomc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix_fomc</span>, <span class='va'>d_saemix_parent</span>, <span class='va'>saemix_options</span><span class='op'>)</span> +</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Thu Nov 5 23:53:33 2020" +#> .. +#> Minimisation finished +#> [1] "Thu Nov 5 23:53:34 2020"</div><div class='img'><img src='saemix-3.png' alt='' width='700' height='433' /></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: 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 +#> ----------------------------------------------------</div><div class='input'><span class='va'>f_saemix_dfop</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix_dfop</span>, <span class='va'>d_saemix_parent</span>, <span class='va'>saemix_options</span><span class='op'>)</span> +</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Thu Nov 5 23:53:35 2020" +#> .. +#> Minimisation finished +#> [1] "Thu Nov 5 23:53:37 2020"</div><div class='img'><img src='saemix-4.png' alt='' width='700' height='433' /></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: 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_k_parent log_k_A1 f_parent_ilr_1 +#> parameter names: parent_0 log_k1 log_k2 g_qlogis #> 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 +#> 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_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=4.97259024646577 +#> 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_k_parent log_k_A1 f_parent_ilr_1 -#> Pop.CondInit 86.53449 -3.207005 -3.060308 -1.920449 +#> parent_0 log_k1 log_k2 g_qlogis +#> Pop.CondInit 94.08322 -1.834163 -4.210797 0.11002 #> ----------------------------------- #> ---- Key algorithm options ---- #> ----------------------------------- @@ -383,69 +745,195 @@ variances of the deviations of the parameters from these mean values.</p> #> ---------------------------------------------------- #> ----------------- Fixed effects ------------------ #> ---------------------------------------------------- -#> Parameter Estimate SE CV(%) -#> parent_0 86.09 1.57 1.8 -#> log_k_parent -3.21 0.59 18.5 -#> log_k_A1 -4.69 0.31 6.6 -#> f_parent_ilr_1 -0.34 0.30 89.2 -#> a a.1 4.69 0.27 5.8 +#> 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 7.07 7.72 109 -#> log_k_parent omega2.log_k_parent 1.75 1.11 63 -#> log_k_A1 omega2.log_k_A1 0.28 0.28 99 -#> f_parent_ilr_1 omega2.f_parent_ilr_1 0.39 0.27 71 +#> 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_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 +#> 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= 1064.35 -#> AIC = 1082.35 -#> BIC = 1078.835 +#> -2LL= 485.4627 +#> AIC = 503.4627 +#> BIC = 499.9477 #> #> Likelihood computed by importance sampling -#> -2LL= 1063.475 -#> AIC = 1081.475 -#> BIC = 1077.96 -#> ----------------------------------------------------</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saemix</span>, plot.type <span class='op'>=</span> <span class='st'>"convergence"</span><span class='op'>)</span> -</div><div class='output co'>#> Plotting convergence plots</div><div class='img'><img src='saemix-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> -<span class='co'># Synthetic data with two-component error</span> -<span class='va'>sampling_times</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>7</span>, <span class='fl'>14</span>, <span class='fl'>28</span>, <span class='fl'>60</span>, <span class='fl'>90</span>, <span class='fl'>120</span><span class='op'>)</span> -<span class='va'>dt50_sfo_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='fl'>80</span>, <span class='fl'>90</span>, <span class='fl'>100</span>, <span class='fl'>111.111</span>, <span class='fl'>125</span><span class='op'>)</span> -<span class='va'>k_in</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span><span class='op'>(</span><span class='fl'>2</span><span class='op'>)</span> <span class='op'>/</span> <span class='va'>dt50_sfo_in</span> - -<span class='va'>SFO</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</span> - -<span class='va'>pred_sfo</span> <span class='op'><-</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>k</span><span class='op'>)</span> <span class='op'>{</span> - <span class='fu'><a href='mkinpredict.html'>mkinpredict</a></span><span class='op'>(</span><span class='va'>SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>k_parent <span class='op'>=</span> <span class='va'>k</span><span class='op'>)</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fl'>100</span><span class='op'>)</span>, <span class='va'>sampling_times</span><span class='op'>)</span> -<span class='op'>}</span> - -<span class='va'>ds_sfo_mean</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>k_in</span>, <span class='va'>pred_sfo</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/Random.html'>set.seed</a></span><span class='op'>(</span><span class='fl'>123456L</span><span class='op'>)</span> -<span class='va'>ds_sfo_syn</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span><span class='op'>(</span><span class='va'>ds_sfo_mean</span>, <span class='kw'>function</span><span class='op'>(</span><span class='va'>ds</span><span class='op'>)</span> <span class='op'>{</span> - <span class='fu'><a href='add_err.html'>add_err</a></span><span class='op'>(</span><span class='va'>ds</span>, sdfunc <span class='op'>=</span> <span class='kw'>function</span><span class='op'>(</span><span class='va'>value</span><span class='op'>)</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>sqrt</a></span><span class='op'>(</span><span class='fl'>1</span><span class='op'>^</span><span class='fl'>2</span> <span class='op'>+</span> <span class='va'>value</span><span class='op'>^</span><span class='fl'>2</span> <span class='op'>*</span> <span class='fl'>0.07</span><span class='op'>^</span><span class='fl'>2</span><span class='op'>)</span>, - n <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span><span class='op'>[[</span><span class='fl'>1</span><span class='op'>]</span><span class='op'>]</span> - <span class='op'>}</span><span class='op'>)</span> -<span class='co'># \dontrun{</span> -<span class='va'>f_mmkin_syn</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>ds_sfo_syn</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='co'># plot(f_mmkin_syn)</span> -<span class='va'>m_saemix_tc</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin_syn</span>, cores <span class='op'>=</span> <span class='fl'>1</span><span class='op'>)</span> +#> -2LL= 473.563 +#> AIC = 491.563 +#> BIC = 488.048 +#> ----------------------------------------------------</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>f_saemix_sfo</span>, <span class='va'>f_saemix_fomc</span>, <span class='va'>f_saemix_dfop</span><span class='op'>)</span><span class='op'>)</span> +</div><div class='output co'>#> Likelihoods computed by importance sampling </div><div class='output co'>#> AIC BIC +#> 1 624.4911 622.5382 +#> 2 467.7499 465.0160 +#> 3 491.5630 488.0480</div><div class='input'><span class='va'>f_mmkin_parent_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span><span class='op'>(</span><span class='va'>f_mmkin_parent</span>, error_model <span class='op'>=</span> <span class='st'>"tc"</span><span class='op'>)</span> +<span class='va'>m_saemix_fomc_tc</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin_parent_tc</span><span class='op'>[</span><span class='st'>"FOMC"</span>, <span class='op'>]</span><span class='op'>)</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) +#> { +#> 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</div><div class='input'><span class='va'>f_saemix_fomc_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix_fomc_tc</span>, <span class='va'>d_saemix_parent</span>, <span class='va'>saemix_options</span><span class='op'>)</span> +</div><div class='output co'>#> Running main SAEM algorithm +#> [1] "Thu Nov 5 23:53:38 2020" +#> .. +#> Minimisation finished +#> [1] "Thu Nov 5 23:53:42 2020"</div><div class='img'><img src='saemix-5.png' alt='' width='700' height='433' /></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: 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 +#> ----------------------------------------------------</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/compare.saemix.html'>compare.saemix</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='va'>f_saemix_fomc</span>, <span class='va'>f_saemix_fomc_tc</span><span class='op'>)</span><span class='op'>)</span> +</div><div class='output co'>#> Likelihoods computed by importance sampling </div><div class='output co'>#> AIC BIC +#> 1 467.7499 465.0160 +#> 2 469.6186 466.4942</div><div class='input'> +<span class='va'>dfop_sfo</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='st'>"A1"</span><span class='op'>)</span>, + A1 <span class='op'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span><span class='op'>(</span><span class='st'>"SFO"</span><span class='op'>)</span><span class='op'>)</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='va'>f_mmkin</span> <span class='op'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span><span class='op'>(</span><span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span><span class='st'>"DFOP-SFO"</span> <span class='op'>=</span> <span class='va'>dfop_sfo</span><span class='op'>)</span>, <span class='va'>ds</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> +<span class='va'>m_saemix</span> <span class='op'><-</span> <span class='fu'>saemix_model</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span> </div><div class='output co'>#> #> #> The following SaemixModel object was successfully created: @@ -468,7 +956,7 @@ variances of the deviations of the parameters from these mean values.</p> #> transform_fractions = object[[1]]$transform_fractions) #> odeparms <- c(odeparms_optim, odeparms_fixed) #> xidep_i <- subset(xidep, id == i) -#> if (analytical) { +#> if (solution_type == "analytical") { #> out_values <- mkin_model$deg_func(xidep_i, odeini, #> odeparms) #> } @@ -476,7 +964,7 @@ variances of the deviations of the parameters from these mean values.</p> #> 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, +#> 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] @@ -486,23 +974,31 @@ variances of the deviations of the parameters from these mean values.</p> #> res <- unlist(res_list) #> return(res) #> } -#> <bytecode: 0x55555d62aeb8> -#> <environment: 0x55555cd8e028> -#> Nb of parameters: 2 -#> parameter names: parent_0 log_k_parent +#> <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_parent normal Estimated +#> 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_parent -#> parent_0 1 0 -#> log_k_parent 0 1 -#> Error model: combined , initial values: a.1=1.05209877924905 b.1=0.0586479225303944 +#> 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_parent -#> Pop.CondInit 100.315 -4.962075</div><div class='input'><span class='va'>d_saemix_tc</span> <span class='op'><-</span> <span class='fu'>saemix_data</span><span class='op'>(</span><span class='va'>f_mmkin_syn</span><span class='op'>)</span> +#> 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</div><div class='input'><span class='va'>d_saemix</span> <span class='op'><-</span> <span class='fu'>saemix_data</span><span class='op'>(</span><span class='va'>f_mmkin</span><span class='op'>)</span> </div><div class='output co'>#> #> #> The following SaemixData object was successfully created: @@ -511,12 +1007,12 @@ variances of the deviations of the parameters from these mean values.</p> #> longitudinal data for use with the SAEM algorithm #> Dataset ds_saemix #> Structured data: value ~ time + name | ds -#> X variable for graphs: time () </div><div class='input'><span class='va'>f_saemix_tc</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix_tc</span>, <span class='va'>d_saemix_tc</span>, <span class='va'>saemix_options</span><span class='op'>)</span> +#> X variable for graphs: time () </div><div class='input'><span class='va'>f_saemix</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/saemix/man/saemix.html'>saemix</a></span><span class='op'>(</span><span class='va'>m_saemix</span>, <span class='va'>d_saemix</span>, <span class='va'>saemix_options</span><span class='op'>)</span> </div><div class='output co'>#> Running main SAEM algorithm -#> [1] "Thu Nov 5 08:28:50 2020" +#> [1] "Thu Nov 5 23:53:43 2020" #> .. #> Minimisation finished -#> [1] "Thu Nov 5 08:29:41 2020"</div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm +#> [1] "Thu Nov 5 23:56:33 2020"</div><div class='img'><img src='saemix-6.png' alt='' width='700' height='433' /></div><div class='output co'>#> Nonlinear mixed-effects model fit by the SAEM algorithm #> ----------------------------------- #> ---- Data ---- #> ----------------------------------- @@ -527,20 +1023,20 @@ variances of the deviations of the parameters from these mean values.</p> #> X variable for graphs: time () #> Dataset characteristics: #> number of subjects: 5 -#> number of observations: 90 -#> average/min/max nb obs: 18.00 / 18 / 18 +#> 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 1 0 parent 105.9 0 0 1 1 -#> 2 1 0 parent 98.0 0 0 1 1 -#> 3 1 1 parent 96.6 0 0 1 1 -#> 4 1 1 parent 99.8 0 0 1 1 -#> 5 1 3 parent 113.0 0 0 1 1 -#> 6 1 3 parent 103.2 0 0 1 1 -#> 7 1 7 parent 102.9 0 0 1 1 -#> 8 1 7 parent 110.8 0 0 1 1 -#> 9 1 14 parent 95.9 0 0 1 1 -#> 10 1 14 parent 85.9 0 0 1 1 +#> 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 ---- #> ----------------------------------- @@ -562,7 +1058,7 @@ variances of the deviations of the parameters from these mean values.</p> #> transform_fractions = object[[1]]$transform_fractions) #> odeparms <- c(odeparms_optim, odeparms_fixed) #> xidep_i <- subset(xidep, id == i) -#> if (analytical) { +#> if (solution_type == "analytical") { #> out_values <- mkin_model$deg_func(xidep_i, odeini, #> odeparms) #> } @@ -570,7 +1066,7 @@ variances of the deviations of the parameters from these mean values.</p> #> i_time <- xidep_i$time #> i_name <- xidep_i$name #> out_wide <- mkinpredict(mkin_model, odeparms = odeparms, -#> odeini = odeini, solution_type = object[[1]]$solution_type, +#> 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] @@ -580,23 +1076,31 @@ variances of the deviations of the parameters from these mean values.</p> #> res <- unlist(res_list) #> return(res) #> } -#> <bytecode: 0x55555d62aeb8> -#> <environment: 0x55555cd8e028> -#> Nb of parameters: 2 -#> parameter names: parent_0 log_k_parent +#> <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_parent normal Estimated +#> 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_parent -#> parent_0 1 0 -#> log_k_parent 0 1 -#> Error model: combined , initial values: a.1=1.05209877924905 b.1=0.0586479225303944 +#> 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_parent -#> Pop.CondInit 100.315 -4.962075 +#> 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 ---- #> ----------------------------------- @@ -618,37 +1122,55 @@ variances of the deviations of the parameters from these mean values.</p> #> ---------------------------------------------------- #> ----------------- Fixed effects ------------------ #> ---------------------------------------------------- -#> Parameter Estimate SE CV(%) -#> parent_0 100.232 1.266 1.3 -#> log_k_parent -4.961 0.089 1.8 -#> a a.1 -0.106 1.211 1142.0 -#> b b.1 0.071 0.017 24.2 +#> 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 3.334 5.024 151 -#> log_k_parent omega2.log_k_parent 0.036 0.024 68 +#> 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_parent -#> omega2.parent_0 1 0 -#> omega2.log_k_parent 0 1 +#> 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= 575.5586 -#> AIC = 587.5586 -#> BIC = 585.2153 +#> -2LL= 879.7721 +#> AIC = 905.7721 +#> BIC = 900.6948 #> #> Likelihood computed by importance sampling -#> -2LL= 575.7797 -#> AIC = 587.7797 -#> BIC = 585.4364 -#> ----------------------------------------------------</div><div class='input'><span class='fu'><a href='https://rdrr.io/pkg/saemix/man/plot-SaemixObject-method.html'>plot</a></span><span class='op'>(</span><span class='va'>f_saemix_tc</span>, plot.type <span class='op'>=</span> <span class='st'>"convergence"</span><span class='op'>)</span> -</div><div class='img'><img src='saemix-3.png' alt='' width='700' height='433' /></div><div class='output co'>#> Plotting convergence plots</div><div class='img'><img src='saemix-4.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># }</span> +#> -2LL= 816.8276 +#> AIC = 842.8276 +#> BIC = 837.7503 +#> ----------------------------------------------------</div><div class='input'> +<span class='co'># }</span> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> |