Using Prior Data for ODE solving

Using prior data for solving

rxode2 can use a single subject or multiple subjects with a single event table to solve ODEs. Additionally, rxode2 can use an arbitrary data frame with individualized events. For example when using nlmixr, you could use the theo_sd data frame

library(rxode2)
#> rxode2 3.0.2.9000 using 2 threads (see ?getRxThreads)
#>   no cache: create with `rxCreateCache()`
library(nlmixr2data)

## Load data from nlmixr
d <- theo_sd

## Create rxode2 model
theo <- 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
  })
  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
  }) 
}

## Create parameter dataset
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
parsDf <- tribble(
  ~ eta.ka, ~ eta.cl, ~ eta.v, 
  0.105, -0.487, -0.080,
  0.221, 0.144, 0.021,
  0.368, 0.031, 0.058,
 -0.277, -0.015, -0.007,
 -0.046, -0.155, -0.142,
 -0.382, 0.367, 0.203,
 -0.791, 0.160, 0.047,
 -0.181, 0.168, 0.096,
  1.420, 0.042, 0.012,
 -0.738, -0.391, -0.170,
  0.790, 0.281, 0.146,
 -0.527, -0.126, -0.198) %>%
    mutate(tka = 0.451, tcl = 1.017, tv = 3.449)

## Now solve the dataset
solveData <- rxSolve(theo, parsDf, d)
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’

plot(solveData, cp)


print(solveData)
#> ── Solved rxode2 object ──
#> ── Parameters ($params): ──
#> # A tibble: 12 × 7
#>    id      tka   tcl    tv eta.ka eta.cl  eta.v
#>    <fct> <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl>
#>  1 1     0.451  1.02  3.45  0.105 -0.487 -0.08 
#>  2 2     0.451  1.02  3.45  0.221  0.144  0.021
#>  3 3     0.451  1.02  3.45  0.368  0.031  0.058
#>  4 4     0.451  1.02  3.45 -0.277 -0.015 -0.007
#>  5 5     0.451  1.02  3.45 -0.046 -0.155 -0.142
#>  6 6     0.451  1.02  3.45 -0.382  0.367  0.203
#>  7 7     0.451  1.02  3.45 -0.791  0.16   0.047
#>  8 8     0.451  1.02  3.45 -0.181  0.168  0.096
#>  9 9     0.451  1.02  3.45  1.42   0.042  0.012
#> 10 10    0.451  1.02  3.45 -0.738 -0.391 -0.17 
#> 11 11    0.451  1.02  3.45  0.79   0.281  0.146
#> 12 12    0.451  1.02  3.45 -0.527 -0.126 -0.198
#> ── Initial Conditions ($inits): ──
#>  depot center 
#>      0      0 
#> ── First part of data (object): ──
#> # A tibble: 132 × 8
#>      id  time    ka    cl     v    cp   depot center
#>   <int> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl>  <dbl>
#> 1     1  0     1.74  1.70  29.0  0    320.        0 
#> 2     1  0.25  1.74  1.70  29.0  3.86 207.      112.
#> 3     1  0.57  1.74  1.70  29.0  6.81 118.      198.
#> 4     1  1.12  1.74  1.70  29.0  9.06  45.4     263.
#> 5     1  2.02  1.74  1.70  29.0  9.79   9.45    284.
#> 6     1  3.82  1.74  1.70  29.0  9.10   0.410   264.
#> # ℹ 126 more rows

## Of course the fasest way to solve if you don't care about the rxode2 extra parameters is

solveData <- rxSolve(theo, parsDf, d, returnType="data.frame")
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
#> using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’

## solved data
dplyr::as_tibble(solveData)
#> # A tibble: 132 × 8
#>       id  time    ka    cl     v    cp         depot center
#>    <int> <dbl> <dbl> <dbl> <dbl> <dbl>         <dbl>  <dbl>
#>  1     1  0     1.74  1.70  29.0  0    320.              0 
#>  2     1  0.25  1.74  1.70  29.0  3.86 207.            112.
#>  3     1  0.57  1.74  1.70  29.0  6.81 118.            198.
#>  4     1  1.12  1.74  1.70  29.0  9.06  45.4           263.
#>  5     1  2.02  1.74  1.70  29.0  9.79   9.45          284.
#>  6     1  3.82  1.74  1.70  29.0  9.10   0.410         264.
#>  7     1  5.1   1.74  1.70  29.0  8.46   0.0440        246.
#>  8     1  7.03  1.74  1.70  29.0  7.56   0.00152       219.
#>  9     1  9.05  1.74  1.70  29.0  6.71   0.0000449     195.
#> 10     1 12.1   1.74  1.70  29.0  5.61   0.000000212   163.
#> # ℹ 122 more rows

data.table::data.table(solveData)
#>         id  time        ka       cl        v       cp        depot    center
#>      <int> <num>     <num>    <num>    <num>    <num>        <num>     <num>
#>   1:     1  0.00 1.7436838 1.698932 29.04946 0.000000 3.199920e+02   0.00000
#>   2:     1  0.25 1.7436838 1.698932 29.04946 3.861730 2.069289e+02 112.18117
#>   3:     1  0.57 1.7436838 1.698932 29.04946 6.805372 1.184389e+02 197.69240
#>   4:     1  1.12 1.7436838 1.698932 29.04946 9.058196 4.539354e+01 263.13572
#>   5:     1  2.02 1.7436838 1.698932 29.04946 9.791088 9.450361e+00 284.42585
#>  ---                                                                        
#> 128:    12  5.07 0.9268162 2.437566 25.81614 8.442535 2.919432e+00 217.95370
#> 129:    12  7.07 0.9268162 2.437566 25.81614 7.074251 4.573778e-01 182.62989
#> 130:    12  9.03 0.9268162 2.437566 25.81614 5.892253 7.436222e-02 152.11524
#> 131:    12 12.05 0.9268162 2.437566 25.81614 4.432614 4.526550e-03 114.43300
#> 132:    12 24.15 0.9268162 2.437566 25.81614 1.414179 6.154773e-08  36.50865

2024-11-22

Using prior data for solving