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Using RxODE data frames

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

library(RxODE)
## Setup example model
mod1 <-RxODE({
    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;
});

## Seup parameters and initial conditions

theta <- 
    c(KA=2.94E-01, CL=1.86E+01, V2=4.02E+01, # central 
      Q=1.05E+01,  V3=2.97E+02,              # peripheral
      Kin=1, Kout=1, EC50=200)               # effects

inits <- c(eff=1);

## Setup dosing event information
ev <- eventTable(amount.units="mg", time.units="hours") %>%
    add.dosing(dose=10000, nbr.doses=10, dosing.interval=12) %>%
    add.dosing(dose=20000, nbr.doses=5, start.time=120,dosing.interval=24) %>%
    add.sampling(0:240);


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

x <- solve(mod1,theta, ev, inits)
rxHtml(x)

Or with mattigr

x <- mod1 %>% solve(theta, ev, inits)
rxHtml(x)

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)

## 
## 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

x <- mod1 %>% solve(theta,ev,inits) %>%  filter(time <=3)
x

##   time       C2        C3     depot    centr      peri      eff
## 1    0  0.00000 0.0000000 10000.000    0.000    0.0000 1.000000
## 2    1 44.37555 0.9198298  7452.765 1783.897  273.1895 1.084664
## 3    2 54.88296 2.6729825  5554.370 2206.295  793.8758 1.180825
## 4    3 51.90343 4.4564927  4139.542 2086.518 1323.5783 1.228914

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

First we need to recreate the original solved system:

x <- mod1 %>% solve(theta,ev,inits);

To examine or change initial conditions, you can use the syntax cmt.0, cmt0, or cmt_0. In the case of the eff compartment defined by the model, this is:

x$eff0

## [1] 1

which shows the initial condition of the effect compartment. If you wished to change this initial condition to 2, this can be done easily by:

x$eff0 <- 2
rxHtml(x)

Notice that the inital 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)
rxHtml(x)

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;
rxHtml(x)

You can access/change all the parametrs, initilizations 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.