Using Prior Data for ODE solving
2021-09-01
Source:vignettes/RxODE-prior-data.Rmd
RxODE-prior-data.RmdUsing prior data for solving
RxODE can use a single subject or multiple subjects with a single event table to solve ODEs. Additionally, RxODE can use an arbitrary data frame with individualized events. For example when using nlmixr, you could use the theo_sd data frame
library(RxODE)
#> RxODE 1.1.1 using 1 threads (see ?getRxThreads)
#> no cache: create with `rxCreateCache()`
## Load data from nlmixr
d <- qs::qread("theo_sd.qs")
## Create RxODE model
theo <- RxODE({
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
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)
plot(solveData, cp)
print(solveData)
#> ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂ Solved RxODE object ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
#> ── Parameters ($params): ───────────────────────────────────────────────────────
#> # A tibble: 12 × 1
#> id
#> <fct>
#> 1 1
#> 2 2
#> 3 3
#> 4 4
#> 5 5
#> 6 6
#> 7 7
#> 8 8
#> 9 9
#> 10 10
#> 11 11
#> 12 12
#> ── Initial Conditions ($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 2.86 3.67 34.8 0 320. 0
#> 2 1 0.25 2.86 3.67 34.8 4.62 157. 161.
#> 3 1 0.57 2.86 3.67 34.8 7.12 62.8 248.
#> 4 1 1.12 2.86 3.67 34.8 8.09 13.0 282.
#> 5 1 2.02 2.86 3.67 34.8 7.68 0.996 267.
#> 6 1 3.82 2.86 3.67 34.8 6.38 0.00581 222.
#> # … with 126 more rows
#> ▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂▂
## Of course the fasest way to solve if you don't care about the RxODE extra parameters is
solveData <- rxSolve(theo, parsDf, d, returnType="data.frame")
## solved data
dplyr::as.tbl(solveData)
#> Warning: `as.tbl()` was deprecated in dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> # A tibble: 132 × 8
#> id time ka cl v cp depot center
#> <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 0 2.86 3.67 34.8 0 3.20e+2 0
#> 2 1 0.25 2.86 3.67 34.8 4.62 1.57e+2 161.
#> 3 1 0.57 2.86 3.67 34.8 7.12 6.28e+1 248.
#> 4 1 1.12 2.86 3.67 34.8 8.09 1.30e+1 282.
#> 5 1 2.02 2.86 3.67 34.8 7.68 9.96e-1 267.
#> 6 1 3.82 2.86 3.67 34.8 6.38 5.81e-3 222.
#> 7 1 5.1 2.86 3.67 34.8 5.58 1.50e-4 194.
#> 8 1 7.03 2.86 3.67 34.8 4.55 6.02e-7 158.
#> 9 1 9.05 2.86 3.67 34.8 3.68 1.77e-9 128.
#> 10 1 12.1 2.86 3.67 34.8 2.66 9.43e-9 92.6
#> # … with 122 more rows
data.table::data.table(solveData)
#> id time ka cl v cp depot center
#> 1: 1 0.00 2.857651 3.669297 34.81332 0.0000000 3.199920e+02 0.00000
#> 2: 1 0.25 2.857651 3.669297 34.81332 4.6240421 1.566295e+02 160.97825
#> 3: 1 0.57 2.857651 3.669297 34.81332 7.1151647 6.276731e+01 247.70249
#> 4: 1 1.12 2.857651 3.669297 34.81332 8.0922106 1.303613e+01 281.71670
#> 5: 1 2.02 2.857651 3.669297 34.81332 7.6837844 9.958446e-01 267.49803
#> ---
#> 128: 12 5.07 2.857651 3.669297 34.81332 5.6044213 1.636210e-04 195.10850
#> 129: 12 7.07 2.857651 3.669297 34.81332 4.5392337 5.385697e-07 158.02579
#> 130: 12 9.03 2.857651 3.669297 34.81332 3.6920276 1.882087e-09 128.53173
#> 131: 12 12.05 2.857651 3.669297 34.81332 2.6855080 8.461424e-09 93.49144
#> 132: 12 24.15 2.857651 3.669297 34.81332 0.7501667 -4.775222e-10 26.11579