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Using rxode2 data frames

Creating an interactive data frame

rxode2 supports returning a solved object that is a modified data-frame. This is done by the predict(), solve(), or rxSolve() methods.

library(rxode2)
library(units)

## Setup example model
mod1 <- function() {
  ini({
    # central 
    KA <- 2.94E-01
    CL <- 1.86E+01
    # peripheral
    V2 <- 4.02E+01 
    Q  <- 1.05E+01
    V3 <- 2.97E+02
    # effects
    Kin  <- 1
    Kout <- 1
    EC50 <- 200
  })
  model({
    C2 <- centr/V2
    C3 <- peri/V3
    d/dt(depot) <- -KA*depot
    d/dt(centr) <-  KA*depot - CL*C2 - Q*C2 + Q*C3
    d/dt(peri)  <-                     Q*C2 - Q*C3
    d/dt(eff)   <- Kin - Kout*(1-C2/(EC50+C2))*eff
    eff(0) <- 1
  })
}

## Seup parameters and initial conditions


## Setup dosing event information
ev <- et(amountUnits="mg", timeUnits = "hours") %>%
  et(amt=10000, addl=9, ii=12) %>%
  et(amt=20000, addl=4, time=120, ii=24) %>%
  et(0:240)


## Now solve
x <- predict(mod1, ev)

x
#> -- Solved rxode2 object --
#> -- Parameters (x$params): --
#>      KA      CL      V2       Q      V3     Kin    Kout    EC50 
#>   0.294  18.600  40.200  10.500 297.000   1.000   1.000 200.000 
#> -- Initial Conditions (x$inits): --
#> depot centr  peri   eff 
#>     0     0     0     1 
#> -- First part of data (object): --
#> # A tibble: 241 x 7
#>   time    C2    C3  depot centr  peri   eff
#>    [h] <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1    0   0   0     10000     0     0   1   
#> 2    1  44.4 0.920  7453. 1784.  273.  1.08
#> 3    2  54.9 2.67   5554. 2206.  794.  1.18
#> 4    3  51.9 4.46   4140. 2087. 1324.  1.23
#> 5    4  44.5 5.98   3085. 1789. 1776.  1.23
#> 6    5  36.5 7.18   2299. 1467. 2132.  1.21
#> # i 235 more rows

rxode2 solved object properties

Using the solved object as a simple data frame

The solved object acts as a data.frame or tbl that can be filtered by dpylr. For example you could filter it easily.


library(dplyr)
## You can  drop units for comparisons and filtering
x <- mod1 %>% solve(ev) %>%
  drop_units() %>% filter(time <= 3) %>% as_tibble()

## or keep them and compare with the proper units.
x <- mod1 %>% solve(ev) %>%
    filter(time <= set_units(3, hr)) %>% as_tibble()
x
#> # A tibble: 4 x 7
#>   time    C2    C3  depot centr  peri   eff
#>    [h] <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1    0   0   0     10000     0     0   1   
#> 2    1  44.4 0.920  7453. 1784.  273.  1.08
#> 3    2  54.9 2.67   5554. 2206.  794.  1.18
#> 4    3  51.9 4.46   4140. 2087. 1324.  1.23

Updating the data-set interactively

However it isn’t just a simple data object. You can use the solved object to update parameters on the fly, or even change the sampling time.

First we need to recreate the original solved system:

x <- mod1 %>% solve(ev)
print(x)
#> -- Solved rxode2 object --
#> -- Parameters ($params): --
#>      KA      CL      V2       Q      V3     Kin    Kout    EC50 
#>   0.294  18.600  40.200  10.500 297.000   1.000   1.000 200.000 
#> -- Initial Conditions ($inits): --
#> depot centr  peri   eff 
#>     0     0     0     1 
#> -- First part of data (object): --
#> # A tibble: 241 x 7
#>   time    C2    C3  depot centr  peri   eff
#>    [h] <dbl> <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1    0   0   0     10000     0     0   1   
#> 2    1  44.4 0.920  7453. 1784.  273.  1.08
#> 3    2  54.9 2.67   5554. 2206.  794.  1.18
#> 4    3  51.9 4.46   4140. 2087. 1324.  1.23
#> 5    4  44.5 5.98   3085. 1789. 1776.  1.23
#> 6    5  36.5 7.18   2299. 1467. 2132.  1.21
#> # i 235 more rows

Modifying observation times for rxode2

Notice that the initial effect is now 2.

You can also change the sampling times easily by this method by changing t or time. For example:

x$t <- seq(0,5,length.out=20)
print(x)
#> -- Solved rxode2 object --
#> -- Parameters ($params): --
#>      KA      CL      V2       Q      V3     Kin    Kout    EC50 
#>   0.294  18.600  40.200  10.500 297.000   1.000   1.000 200.000 
#> -- Initial Conditions ($inits): --
#> depot centr  peri   eff 
#>     0     0     0     1 
#> -- First part of data (object): --
#> # A tibble: 20 x 7
#>    time    C2     C3  depot centr  peri   eff
#>     [h] <dbl>  <dbl>  <dbl> <dbl> <dbl> <dbl>
#> 1 0       0   0      10000     0    0    1   
#> 2 0.263  16.8 0.0817  9255.  677.  24.3  1.01
#> 3 0.526  29.5 0.299   8566. 1187.  88.7  1.03
#> 4 0.789  38.9 0.615   7929. 1562. 183.   1.06
#> 5 1.05   45.5 1.00    7338. 1830. 298.   1.09
#> 6 1.32   50.1 1.44    6792. 2013. 427.   1.12
#> # i 14 more rows
plot(x)

Modifying simulation parameters

You can also access or change parameters by the $ operator. For example, accessing KA can be done by:

x$KA
#> [1] 0.294

And you may change it by assigning it to a new value.

x$KA <- 1
print(x)
#> -- Solved rxode2 object --
#> -- Parameters ($params): --
#>    KA    CL    V2     Q    V3   Kin  Kout  EC50 
#>   1.0  18.6  40.2  10.5 297.0   1.0   1.0 200.0 
#> -- Initial Conditions ($inits): --
#> depot centr  peri   eff 
#>     0     0     0     1 
#> -- First part of data (object): --
#> # A tibble: 20 x 7
#>    time    C2    C3  depot centr   peri   eff
#>     [h] <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl>
#> 1 0       0   0     10000     0     0    1   
#> 2 0.263  52.2 0.261  7686. 2098.   77.6  1.03
#> 3 0.526  83.3 0.900  5908. 3348.  267.   1.09
#> 4 0.789  99.8 1.75   4541. 4010.  519.   1.15
#> 5 1.05  106.  2.69   3490. 4273.  800.   1.21
#> 6 1.32  106.  3.66   2683. 4272. 1086.   1.26
#> # i 14 more rows
plot(x)

You can access/change all the parameters, initialization(s) or events with the $params, $inits, $events accessor syntax, similar to what is used above.

This syntax makes it easy to update and explore the effect of various parameters on the solved object.