nlmixr2plot: The core estimation routines for nlmixr2

 # install.packages("remotes")
remotes::install_github("nlmixr2/nlmixr2data")
remotes::install_github("nlmixr2/lotri")
remotes::install_github("nlmixr2/rxode2")
remotes::install_github("nlmixr2/nlmixr2est")
remotes::install_github("nlmixr2/nlmixr2extra")
remotes::install_github("nlmixr2/nlmixr2plot")

 library(nlmixr2est)
 #> Loading required package: nlmixr2data
 library(nlmixr2plot)
 
 ## The basic model consists of an ini block that has initial estimates
one.compartment <- function() {
 ini({
 tka <- 0.45 ; label("Log Ka")
 tcl <- 1 ; label("Log Cl")
 tv <- 3.45 ; label("Log V")
 eta.ka ~ 0.6
 eta.cl ~ 0.3
 eta.v ~ 0.1
 add.sd <- 0.7
 })
 # and a model block with the error specification and model specification
 model({
 ka <- exp(tka + eta.ka)
 cl <- exp(tcl + eta.cl)
 v <- exp(tv + eta.v)
 d/dt(depot) = -ka * depot
 d/dt(center) = ka * depot - cl / v * center
 cp = center / v
 cp ~ add(add.sd)
 })
}
 
 ## The fit is performed by the function nlmixr/nlmix2 specifying the model, data and estimate
fit <- nlmixr2(one.compartment, theo_sd, est="saem", saemControl(print=0))
 #> → loading into symengine environment...
 #> → pruning branches (`if`/`else`) of saem model...
 #> ✔ done
 #> → finding duplicate expressions in saem model...
 #> [====|====|====|====|====|====|====|====|====|====] 0:00:00
 #> → optimizing duplicate expressions in saem model...
 #> [====|====|====|====|====|====|====|====|====|====] 0:00:00
 #> ✔ done
 #> rxode2 2.0.11.9000 using 8 threads (see ?getRxThreads)
 #> no cache: create with `rxCreateCache()`
 #> Calculating covariance matrix
 #> → loading into symengine environment...
 #> → pruning branches (`if`/`else`) of saem model...
 #> ✔ done
 #> → finding duplicate expressions in saem predOnly model 0...
 #> → finding duplicate expressions in saem predOnly model 1...
 #> → optimizing duplicate expressions in saem predOnly model 1...
 #> → finding duplicate expressions in saem predOnly model 2...
 #> ✔ done
 #> → Calculating residuals/tables
 #> ✔ done
 #> → compress origData in nlmixr2 object, save 5952
 #> → compress phiM in nlmixr2 object, save 62360
 #> → compress parHist in nlmixr2 object, save 9592
 #> → compress saem0 in nlmixr2 object, save 28760
 
 print(fit)
 #> ── nlmixr2 SAEM OBJF by FOCEi approximation ──
 #> 
 #> Gaussian/Laplacian Likelihoods: AIC() or $objf etc. 
 #> FOCEi CWRES & Likelihoods: addCwres() 
 #> 
 #> ── Time (sec $time): ──
 #> 
 #> setup covariance saem table compress other
 #> elapsed 0.002 0.01 5.24 0.1 0.09 2.498
 #> 
 #> ── Population Parameters ($parFixed or $parFixedDf): ──
 #> 
 #> Parameter Est. SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
 #> tka Log Ka 0.454 0.196 43.1 1.57 (1.07, 2.31) 71.5 -0.0203% 
 #> tcl Log Cl 1.02 0.0853 8.4 2.76 (2.34, 3.26) 27.6 3.46% 
 #> tv Log V 3.45 0.0454 1.32 31.5 (28.8, 34.4) 13.4 9.89% 
 #> add.sd 0.693 0.693 
 #> 
 #> Covariance Type ($covMethod): linFim
 #> No correlations in between subject variability (BSV) matrix
 #> Full BSV covariance ($omega) or correlation ($omegaR; diagonals=SDs) 
 #> Distribution stats (mean/skewness/kurtosis/p-value) available in $shrink 
 #> Censoring ($censInformation): No censoring
 #> 
 #> ── Fit Data (object is a modified tibble): ──
 #> # A tibble: 132 ×ばつ 19
 #> ID TIME DV PRED RES IPRED IRES IWRES eta.ka eta.cl eta.v cp
 #> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
 #> 1 1 0 0.74 0 0.74 0 0.74 1.07 0.103 -0.491 -0.0820 0 
 #> 2 1 0.25 2.84 3.27 -0.426 3.87 -1.03 -1.48 0.103 -0.491 -0.0820 3.87
 #> 3 1 0.57 6.57 5.85 0.723 6.82 -0.246 -0.356 0.103 -0.491 -0.0820 6.82
 #> # ... with 129 more rows, and 7 more variables: depot <dbl>, center <dbl>,
 #> # ka <dbl>, cl <dbl>, v <dbl>, tad <dbl>, dosenum <dbl>
 
 # this now gives the goodness of fit plots
 plot(fit)