ggPMX with nlmixr

library(ggPMX)
library(nlmixr)

It is simple to create a ggPMX controller for a nlmixr object.

Using the theophylline example with a nlmixr model we have:

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
    })
    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)
    })
}
nlmixr(one.compartment)
#> ▂▂ RxODE-based ODE model ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ 
#> ── Initialization: ──────────────────────────────────────────────────────────────────────────────────────────────────── 
#> Fixed Effects ($theta): 
#>  tka  tcl   tv 
#> 0.45 1.00 3.45 
#> 
#> Omega ($omega): 
#>        eta.ka eta.cl eta.v
#> eta.ka    0.6    0.0   0.0
#> eta.cl    0.0    0.3   0.0
#> eta.v     0.0    0.0   0.1
#> ── μ-referencing ($muRefTable): ─────────────────────────────────────────────────────────────────────────────────────── 
#> ┌─────────┬─────────┐
#> │ theta   │ eta     │
#> ├─────────┼─────────┤
#> │ tka     │ eta.ka  │
#> ├─────────┼─────────┤
#> │ tcl     │ eta.cl  │
#> ├─────────┼─────────┤
#> │ tv      │ eta.v   │
#> └─────────┴─────────┘
#> ── 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) 
#> ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂

At the time of this writing, nlmixr requires python; Since cran does not have python installed, we run this model locally by:

fit <- nlmixr(one.compartment, theo_sd, est="saem", control=list(print=0))
saveRDS(fit,"fit.rds")

Then we can read this into the system

fit <- readRDS("fit.rds")
print(fit)
#> ── nlmixr SAEM(ODE); OBJF not calculated fit ────────────────────────────────────────────────────────────────────────── 
#>  Gaussian/Laplacian Likelihoods: AIC() or $objf etc. 
#>  FOCEi CWRES & Likelihoods: addCwres() 
#> 
#> ── Time (sec; $time): ───────────────────────────────────────────────────────────────────────────────────────────────── 
#>           saem    setup table covariance    other
#> elapsed 20.803 3.921085 0.009      0.007 0.150915
#> 
#> ── Population Parameters ($parFixed or $parFixedDf): ──────────────────────────────────────────────────────────────────
#> Registered S3 method overwritten by 'R.oo':
#>   method        from       
#>   throw.default R.methodsS3
#>        Parameter  Est.     SE %RSE Back-transformed(95%CI) BSV(CV%)
#> tka       Log Ka 0.451  0.196 43.5       1.57 (1.07, 2.31)     71.9
#> tcl       Log Cl  1.02 0.0836 8.22       2.77 (2.35, 3.26)     27.0
#> tv         Log V  3.45 0.0469 1.36       31.5 (28.7, 34.5)     14.0
#> add.sd           0.692                               0.692         
#>        Shrink(SD)%
#> tka        0.411% 
#> tcl         3.36% 
#> tv          10.0% 
#> add.sd             
#> 
#>   Covariance Type ($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 
#> 
#> ── Fit Data (object is a modified tibble): ──────────────────────────────────────────────────────────────────────────── 
#> # A tibble: 132 x 18
#>   ID     TIME    DV  EVID  PRED    RES IPRED   IRES  IWRES eta.ka eta.cl
#>   <fct> <dbl> <dbl> <int> <dbl>  <dbl> <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1 1     0      0.74     0  0     0.74   0     0.74   1.07   0.105 -0.487
#> 2 1     0.25   2.84     0  3.26 -0.423  3.86 -1.02  -1.48   0.105 -0.487
#> 3 1     0.570  6.57     0  5.84  0.725  6.81 -0.235 -0.340  0.105 -0.487
#> # … with 129 more rows, and 7 more variables: eta.v <dbl>, ka <dbl>,
#> #   cl <dbl>, v <dbl>, cp <dbl>, depot <dbl>, center <dbl>

The fit object is a nlmixr fit; You can read it into the nlmixr controller by:

fit %>%
    pmx_nlmixr(vpc = FALSE) -> ## VPC is turned on by default, can turn off.
    ctr ## Assigned to controller 
#> Compiling NPDE model...done
#> Compiling model...done

Once the controller is created, you can of course have the same types of diagnostic plots as the standard ggPMX package; Using the same examples as the user manual:

ctr %>% pmx_plot_dv_pred

ctr %>% pmx_plot_npde_time

ctr %>% pmx_plot_vpc
#> NULL
ctr %>% pmx_plot_eta_box

ctr %>% pmx_plot_eta_matrix(
  shrink=list(size=3,hjust=1.5))
#> Warning: Removed 2 rows containing non-finite values (stat_smooth).
#> Warning: Removed 2 rows containing missing values (geom_point).
#> Warning: Removed 2 rows containing non-finite values (stat_smooth).
#> Warning: Removed 2 rows containing missing values (geom_point).
#> Warning: Removed 1 rows containing missing values (geom_smooth).

This shows the standard plots for theophylline