## 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

### 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.