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The goal of nlmixr2extra is to provide the tools to help with common pharmacometric tasks with nlmixr2 models like bootstrapping, covariate selection etc.

Installation

You can install the development version of nlmixr2extra from GitHub with:

# 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")

Example of a bootstrapFit()

This is a basic example of bootstrapping provided by this package

library(nlmixr2est)
library(nlmixr2extra)
## basic example code
## The basic model consiss of an ini block that has initial estimates
one.compartment <- function() {
  ini({
    tka <- 0.45 # Log Ka
    tcl <- 1 # Log Cl
    tv <- 3.45    # 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 sppecification 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))
#>  
#>  
#> 
#> ℹ parameter labels from comments will be replaced by 'label()'
#> 
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of saem model...
#> ✔ done
#> → finding duplicate expressions in saem model...
#> → optimizing duplicate expressions in saem model...
#> ✔ done
#> 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 9560
#> → compress saem0 in nlmixr2 object, save 24584

fit2 <- suppressMessages(bootstrapFit(fit))
fit2
#> ── nlmixr SAEM OBJF by FOCEi approximation ──
#> 
#>  Gaussian/Laplacian Likelihoods: AIC(fit) or fit$objf etc. 
#>  FOCEi CWRES & Likelihoods: addCwres(fit) 
#> 
#> ── Time (sec fit$time): ──
#> 
#>            setup table compress    other covariance
#> elapsed 0.001151 0.024    0.017 3.042849      0.074
#> 
#> ── Population Parameters (fit$parFixed or fit$parFixedDf): ──
#> 
#>        Parameter  Est.     SE %RSE Back-transformed(95%CI) BSV(CV%) Shrink(SD)%
#> tka       Log Ka 0.454  0.207 45.7       1.57 (1.05, 2.36)     71.5   -0.0203% 
#> tcl       Log Cl  1.02 0.0771 7.59       2.76 (2.37, 3.21)     27.6      3.46% 
#> tv         Log V  3.45 0.0453 1.31       31.5 (28.8, 34.4)     13.4      9.89% 
#> add.sd           0.693                               0.693                     
#>  
#>   Covariance Type (fit$covMethod): boot200
#>     other calculated covs (setCov()): linFim
#>   No correlations in between subject variability (BSV) matrix
#>   Full BSV covariance (fit$omega) or correlation (fit$omegaR; diagonals=SDs) 
#>   Distribution stats (mean/skewness/kurtosis/p-value) available in fit$shrink 
#> 
#> ── Fit Data (object fit is a modified tibble): ──
#> # A tibble: 132 × 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>