---
title: "RxODE syntax"
output: 
  learnr::tutorial:
    progressive: true
runtime: shiny_prerendered
description: >
  This covers the basic syntax supported for `RxODE` models and how to compile the models using `RxODE`
---

```{r setup, include=FALSE}
library(learnr)
library(RxODE)
library(ggplot2)
knitr::opts_chunk$set(echo = FALSE)
```

From Rstudio 1.3+ tutorial pane you can use the expand button
(![](images/expandTutorial.png)) to navigate tutorial sections.

## Creating Compiled models

`RxODE` currently supports differential equations using Leibniz
notation, that is `d/dt(x)`. These differential equations can be in
one of the following locations:

1. In a separate model definition file
2. Inline in a string
3. In an R expression passed to `RxODE` in brackets `{}`

Each of these can be directly passed into the `RxODE` function.  This
translates the input into compiled C code that can be used to quickly
run ODE solutions.

### Creating an RxODE model in 3 ways

*This is a simple exercise to create a RxODE model from more than one model definition*

Change the below R code to:

- Move the model definition to an inline string `ode`
- Once the inline string method is complete, use the `RxODE({})`
  method to specify the ODE inline.

```{r RxODE_model, exercise=TRUE}
## This example creates an ODE with a file tmp.rxode
## The learnr won't create the file, so change this to be an inline
## string
sink("tmp.rxode")
cat("d/dt(center)=-kel*center\n")
sink()
model <- RxODE("tmp.rxode")
print(model)
```


<div id="RxODE_model-hint">
You may wish to take out the `sink` statements and change the `cat` to `ode=`
</div>

### Create an RxODE model inline 

Now change the string-based model to an inline `RxODE` model:

```{r RxODE_inline, exercise=TRUE}
## This is an ode model defined by a string; 
## Change it to be defined inline, that is RxODE({})
out = "d/dt(center)=-kel*center"
model <- RxODE(out)
print(model)
```


```{r RxODE_inline-solution}
model <- RxODE({
  d/dt(center) <- -kel*center
})
print(model)
```

## Pre-Quiz - ODE syntax

We briefly touched on the ODE syntax in `RxODE`.  Based on what you
know so far, What is the way you specify ODE equations in `RxODE`?

```{r odeQuiz}
quiz(
  question("What is the syntax for ODEs in RxODE",
    answer("`DXDT_state = -k*state`"),
    answer("`ddt_state = -k*state`"),
    answer("`DADT(1)=-K*A(1)`"),
    answer("`d/dt(state)=-k*state`", correct = TRUE),
    answer("`d/dy(state)=-m*state`")
  ),
  question("Do you have to use `=` for RxODE ODE assignments?",
    answer("Yes, RxODE requires `=` for ODE assignment"),
    answer("No, You may use `=` or `<-` to assign ODE equations", correct=TRUE)
  )
)
```

## Simple and time derivative statements

The most fundamental part of an `RxODE` model are simple assignments
and time-derivative assignments:

- **simple** assignments, where the left hand is an identifier (i.e.,
  variable)
- special **time-derivative** assignments, where the left hand
  specifies the change of the amount in the corresponding state
  variable (compartment) with respect to time e.g., `d/dt(depot)`. 
  
Once these variables are assigned in the RxODE model, RxODE parses the
model and separates the following types of variables:

- `state` - This gives a list of names the ODE state variables. These
  are in order of how they appear in the model; The first compartment
  is the first compartment listed.
- `params` - This gives a list of names of parameters that need to be
  specified (as a defined parameter, supplied parameter, or
  covariate).  If you use `summary(rxodeModel)` it will show you what
  parameters still need to be defined.
- `lhs` - This gives a parameter that is calculated instead of
  supplied or used as a ODE `state`.

Each of these properties can be accessed with the `$` R operator. For
example if you have an `RxODE` compiled model `mod`, you can access
the state names by `mod$state`

### Exercise - Fix RxODE model and solve

With the knowledge of how simple statements are defined, you can
figure out what is going wrong with a simple `RxODE` model.

In this exercise, fix the model by adding a parameter value to the
following code assign the missing parameter value to 1

```{r rxParams, exercise=TRUE}
## 
mod1 <-RxODE({
    KA <- .294
    CL=18.6
    V2=40.2 # central 
    Q =10.5
    V3=297.# peripheral
    Kin=1
    EC50=200
    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;
})
# You can see what parameters are needed by the summary function:
# summary(mod1)

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% et(0,24,length.out=24)) %>%
  plot(centr, eff)
```

```{r rxParams-solution}
## 
mod1 <-RxODE({
    KA <- .294
    CL=18.6
    V2=40.2 # central 
    Q =10.5
    V3=297.# peripheral
    Kin=1
    Kout = 1 # Missing parameter
    EC50=200
    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;
})
# You can see what parameters are needed by the summary function:
# summary(mod1)

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% et(0,24,length.out=24)) %>%
  plot(centr, eff)
```


## Special state properties

In addition to the simple statements, and the time-deriviate
statements defining states, there are a few special state properties
that can be added to each state:

- **initial-condition** assignments where the left hand
  specifies the compartment of the initial condition being specified,
  e.g. `depot(0) = 0`
  
- **bioavailability** is a modeled parameter or constant that changes
  the magnitude of an event/dose by multiplying the dose/event by this
  amount. `f(depot)=`

- **absorption lag time** is a modeled parameter that shifts the time
  of the dose/event by lag time specified; It is specified by `alag`
  on the compartment, like `alag(depot)=`

- **modeled rate** when the event data specify the rate should be
  modeled (`rate=-1`) `rate` for a compartment can specify/model the
  rate; This is specified by `rate(depot) = `

- **modeled duration** when the event data specify the duration should
  be modeled (`rate=-2`), `dur` for a compartment can specify the
  duration of the infusion: `dur(depot) = `

More information about the event specification for the duration/rate
  items can be found in the [RxODE events
  vignette](https://nlmixrdevelopment.github.io/RxODE/articles/RxODE-event-types.html).
  
**Important:** By default, these special properties can only be defined after a state has been defined by `d/dt(state)=`.  

### Exercise - change bioavailibilty and effect initial condition

With the old model:

- Change the bioavailability of the `depot` compartment to `0.5`
- Set the initial `eff` compartment value to `1`.

```{r rxF, exercise=TRUE}
mod1 <-RxODE({
    KA <- .294
    CL=18.6
    V2=40.2 # central 
    Q =10.5
    V3=297.# peripheral
    Kin=1
    Kout = 1
    EC50=200
    C2 = centr/V2;
    C3 = peri/V3;
    f(depot)=0.5
    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;
})
# You can see what parameters are needed by the summary function:
# summary(mod1)

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% et(0,24,length.out=24)) %>%
  plot(centr, eff)
```

```{r rxF-solution}
## 
mod1 <-RxODE({
    KA <- .294
    CL=18.6
    V2=40.2 # central 
    Q =10.5
    V3=297.# peripheral
    Kin=1
    Kout = 1 # Missing parameter
    EC50=200
    C2 = centr/V2;
    C3 = peri/V3;
    d/dt(depot) =-KA*depot;
    f(depot) = 0.5
    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
})
# You can see what parameters are needed by the summary function:
# summary(mod1)

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% et(0,24,length.out=24)) %>%
  plot(centr, eff)
```

## Model Times and Compartment order

We discussed simple assigments, time-derivative statements
`d/dt(state)=...` and special properties you can add to the state
`alag(state)=...`.  There are a few additional convience statements
you can add to your model to change the compartment behavior

- **Compartment declaration statements** `cmt(compartmentName)`. This
  can change the compartment order.  The order of states in the
  `RxODE` model is determined by the order you see `cmt()` statements
  and `d/dt()=` statements. Therefore, to adjust the default dose (ie
  `cmt=1`) you simply need to specify right `cmt()`at the top of the
  `RxODE` model. This is useful for matching event table compartments
  to what you want them to be.

- **modeled times**.  These model times are specified by
  `mtime(var)=time`.  This adds a modeled time to the output dataset.
  This can be set to a numeric value or a model variable.  It is
  useful to add an observation point at the end of an infusion or the
  end of a entero-hepatic recycling model.
  
### Compartment declaration exercise

The following code explicitly states that `depot` is the first
compartment.

Consider a situation where you want to change the `cmt=1` event to the
`central` compartment to deliver a bolus dose.  Adjust the code below
to change to bolus dose to an iv infusion by switching the default or
first compartment to `centr`


```{r cmt,exercise=TRUE}
mod1 <-RxODE({
  cmt(depot) # the first compartment/default dose is depot
  KA <- .294
  CL=18.6
  V2=40.2 # central 
  Q =10.5
  V3=297.# peripheral
  Kin=1
  Kout = 1
  EC50=200
  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
})
# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% et(0,24,length.out=24)) %>%
  plot(centr, eff)
```


```{r cmt-solution}
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA <- .294
  CL=18.6
  V2=40.2 # central 
  Q =10.5
  V3=297.# peripheral
  Kin=1
  Kout = 1
  EC50=200
  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
})
# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% et(0,24,length.out=24)) %>%
  plot(centr, eff)
```


### Add modeled time

If you notice the output plot above, it seems that the central compartment received 2 different bolus doses.  This is a consequence of the sampling time used.

In this exercise add a modeling time to observe the `12.001`
time-point to see if the plot changes.  Recall to assign a variable to
an observation you wrap it in the `mtime()` function, ie
`mtime(var)=X`

```{r mtime, exercise=TRUE}
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA <- .294
  CL=18.6
  V2=40.2 # central 
  Q =10.5
  V3=297.# peripheral
  Kin=1
  Kout = 1
  EC50=200
  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
})
# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot(centr, eff)
```



```{r mtime-solution}
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA <- .294
  CL=18.6
  V2=40.2 # central 
  Q =10.5
  V3=297.0 # peripheral
  Kin=1
  Kout = 1
  EC50=200
  C2 = centr/V2;
  C3 = peri/V3;
  mtime(mt1)=12.001
  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
})

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot(centr, eff)
```

### Mtime fixes the plots 

You can now see that the doses seem the same at time zero and the 12
hour time-points.

## If/else clauses

`RxODE` also supports the standard `if`/`else` clauses; A more common
use of `mtime()=` variables is to make sure that a time-point is
captured when a key variable changes. The `RxODE` `if` and `else`
allow time-changes to occur in a model like you would use in a
standard `RxODE` block.  Using the internal variable `t` for time you
can adjust parameters and make sure they show up in the model.

### Exercise 2 -- Make sure that the change point is observed

Change the model below to make sure the 2.5 hour point is observed as
a modeling time-point.

```{r if-else, exercise=TRUE}
library(ggplot2)
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA <- .294
  V2=40.2 # central 
  Q =10.5
  V3=297.0 # peripheral
  if (t < 2.5){
    CL = 18.6
  } else {
    CL=40
  }
  Kin=1
  Kout = 1
  EC50=200
  C2 = centr/V2;
  C3 = peri/V3;
  mtime(mt1)=12.001
  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
})

# This is a simple code that plots the ODE system.
# D2on't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot(centr) + scale_y_log10()
```

```{r if-else-solution}
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA <- .294
  V2=40.2 # central 
  Q =10.5
  V3=297.0 # peripheral
  mtime(ttrans) = 2.5
  if (t < ttrans){
    CL = 18.6
  } else {
    CL=40
  }
  Kin=1
  Kout = 1
  EC50=200
  C2 = centr/V2;
  C3 = peri/V3;
  mtime(mt1)=12.001
  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
})

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot(centr) + scale_y_log10()
```

## Suppressing output

One last note about `RxODE`, you can suppress columns in the solved
output (by default `RxODE` outputs all `$state` values and `$lhs`
values).  This is done by modifing either the simple assigment or
time-derivative assignment `d/dt(state)=...` to use `~` instead of the
standard assignment operators `<-` or `=`

### Exercise: Suppressing all output exept C2

Instead of creating a large matrix of output, change the output to
include only `C2`

```{r suppress, exercise=TRUE}
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA <- .294
  V2=40.2 # central 
  Q =10.5
  V3=297.0 # peripheral
  mtime(ttrans) = 2.5
  if (t < ttrans){
    CL = 18.6
  } else {
    CL=40
  }
  Kin=1
  Kout = 1
  EC50=200
  C2 = centr/V2;
  C3 = peri/V3;
  mtime(mt1)=12.001
  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
})

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot()
```



```{r suppress-solution}
mod1 <-RxODE({
  cmt(centr) # the first compartment/default dose is depot
  KA ~ .294
  V2~40.2 # central 
  Q ~10.5
  V3~297.0 # peripheral
  mtime(ttrans) ~ 2.5
  if (t < ttrans){
    CL ~ 18.6
  } else {
    CL~40
  }
  Kin~1
  Kout ~ 1
  EC50~200
  C2 <- centr/V2;
  C3 ~ peri/V3;
  mtime(mt1)~12.001
  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
})

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot()
```

## PK solved systems

PK Solved models are also simple to create. You simply place the
`linCmt()` psuedo-function into your code.  The `linCmt()` function
figures out the type of model to use based on the parameter names
specified.

Most often, pharmacometric models are parameterized in terms of volume
and clearances. Clearances are specified by NONMEM-style names of
`CL`, `Q`, `Q1`, `Q2`, etc. or distributional clearances `CLD`,
`CLD2`. Volumes are specified by Central (`VC` or `V`),
Peripheral/Tissue (`VP`, `VT`). 

Another popular parameterization is in terms of micro-constants. RxODE
assumes compartment `1` is the central compartment.  The elimination
constant would be specified by `K`, `Ke` or `Kel`. 

The last parameterization possible is using `alpha` and `V` and/or
`A`/`B`/`C`

To see some tables of tested parameters for `linCmt()` you can go to
the [`linCmt()`
vignette](https://nlmixrdevelopment.github.io/RxODE/articles/RxODE-model-types.html#solved-compartment-models)

Once the `linCmt()` sleuthing is complete, the `1`, `2` or `3`
compartment model solution is used as the value of `linCmt()`.

The compartments where you can dose in a linear solved system are
`depot` (for oral absorption models) and `central`.  Without any
additional ODEs, these compartments are numbered `depot=1` and
`central=2`.  You can add some properties to these compartments like
bioavailibilty changes `f(depot)=0.5` changes the bioavailability to
0.5.

When the absorption constant `ka` is missing, you may only dose to the
`central` compartment.  Without any additional ODEs the compartment
number is `central=1`.


### Exercise 1: Change a one-compartment bolus model

The model below is a 2 compartment IV model; Change the model to be a 2-compartment
oral absorption model by adding a `Ka` parameter.

```{r lincmt, exercise=TRUE}
mod1 <-RxODE({
  V2~40.2 # central 
  Q ~10.5
  V3~297.0 # peripheral
  CL ~ 18.6
  C2 = linCmt()
})

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24) %>% 
          et(0,24,length.out=24)) %>%
  plot()
```


<div id="lincmt-hint">
Put in the `KA = .294` parameter to change the model
</div>


### Exercise 2: Mixing ODE and PK solved systems

One of the nice features of `RxODE` is it can also mix `linCmt()`
solutions and ODE solutions.

As an exercise change the below model to drop the `depot` and
`central` ODE definitions:

```{r linCmt-mix, exercise=TRUE}
mod1 <-RxODE({
  KA ~ .294
  V2~40.2 # central 
  Q ~10.5
  V3~297.0 # peripheral
  CL ~ 18.6
  Kin~1
  Kout ~ 1
  EC50~200
  C2 <- central/V2;
  C3 ~ peri/V3;
  mtime(mt1)~12.001
  d/dt(depot) ~ -KA*depot;
  d/dt(central) ~ 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
})

# This is a simple code that plots the ODE system.
# Don't worry about `et` if you don't understand it now
# It will be covered in another tutorial
rxSolve(mod1,et(amt=10000, ii=12, until=24, cmt='depot') %>% 
          et(0,24,length.out=24)) %>%
  plot()
```

<div id="linCmt-mix-hint">
Simply drop the `d/dt(central)`, `d/dt(depot)` and `C3` lines; Change `C2=linCmt()`
</div>

### Exercise 3: Compartment number with mixed `linCmt`/`ODE` models

You may have noticed that the `et` dosed to `cmt='depot'`.  This is so
that the solutions are the same between the ODE model and the solved system.

The compartment numbers have changed when mixing `ODE`s and `linCmt()` models.

In this exercise use `print(mod1)` to get an idea of what the
compartment numbers are for the model.

```{r linCmt-cmt, exercise=TRUE}
mod1 <-RxODE({
  KA ~ .294
  V2~40.2 # central 
  Q ~10.5
  V3~297.0 # peripheral
  CL ~ 18.6
  Kin~1
  Kout ~ 1
  EC50~200
  C2 <- linCmt()
  d/dt(eff) = Kin - Kout*(1-C2/(EC50+C2))*eff;
  eff(0) = 1
})
```

### Mixing ODE models and `linCmt()` models add solved systems at the end of the model

You can see the `linCmt()` model has a variable `$stateExtra` which
lists the extra states.  In this case the `depot` compartment is the
`2`nd compartment since it is after the state `eff` compartment.  The
extra states always are numbered after the ODE states have been
specified.

**Note:** `RxODE` cannot currently use `cmt()` on the `depot` and
`central` compartments for solved systems to change their compartment
number.


## Jacobian-derivatives

There is one more type of syntax that you may see in `RxODE` models, the **Jacobain** syntax; ie:

- special **Jacobian-derivative** assignments, where the left hand
  specifies the change in the compartment ode with respect to a
  variable.  For example, if `d/dt(y) = dy`, then a Jacobian for this
  compartment can be specified as `df(y)/dy(dy) = 1`. 
  
### Seeing Jacobian derivatives

The Following code is an example of Jacobain syntax:

```{r jac, exercise=TRUE}
van <- RxODE({
  d/dt(dy)=y
  d/dt(y)=mu*(1-dy^2)*y-dy
})

van <- RxODE({
    d/dt(dy) = y
    d/dt(y) = -dy + y * mu * (1 - dy^2)
    df(dy)/dy(dy) = 0
    df(y)/dy(dy) = -1 - 2 * y * mu * dy
    df(dy)/dy(y) = 1
    df(y)/dy(y) = mu * (1 - dy^2)
    df(dy)/dy(mu) = 0
    df(y)/dy(mu) = y * (1 - dy^2)
})

## The lsoda (not liblsoda) method is the only solving method that
## uses the Jacobian specification currently
```

Note if `RxODE` is setup completely, you can automatically calculate
the Jacoabian by `RxODE({}, calcJac=TRUE)`

## Summary

`RxODE` model syntax comprises of:

- **simple** assignments, where the left hand is an identifier (i.e.,
  variable)
- special **time-derivative** assignments, where the left hand
  specifies the change of the amount in the corresponding state
  variable (compartment) with respect to time e.g., `d/dt(depot)`:
- special **initial-condition** assignments where the left hand
  specifies the compartment of the initial condition being specified,
  e.g. `depot(0) = 0`
- special model event changes including **bioavailability**
  (`f(depot)=1`), **lag time** (`lag(depot)=0`), **modeled rate**
  (`rate(depot)=2`) and **modeled duration** (`dur(depot)=2`). An
  example of these model features and the event specification for the
  modeled infusions the RxODE data specification is found in [RxODE
  events vignette](https://nlmixrdevelopment.github.io/RxODE/articles/RxODE-event-types.html).
- **Compartment declaration statements**, which can change the default
  dosing compartment and the assumed compartment number(s) as well as
  add extra compartment names at the end (useful for multiple-endpoint
  nlmixr models); These are specified by `cmt(compartmentName)`
- special **change point syntax, or model times**.  These model times are
  specified by `mtime(var)=time`
- special **Jacobian-derivative** assignments, where the left hand
  specifies the change in the compartment ode with respect to a
  variable.  For example, if `d/dt(y) = dy`, then a Jacobian for this
  compartment can be specified as `df(y)/dy(dy) = 1`.  There may be
  some advantage to obtaining the solution or specifying the Jacobian
  for very stiff ODE systems.  However, for the few stiff systems we
  tried with LSODA, this actually slightly slowed down the solving.
  
Note that assignment can be done by `=` or `<-`.

Additionally, assignment can be done with the `~` operator, which
causes RxODE to use the variable/expression while solving but suppress
output to either the matrix or data-frame returned in R.

Also note `linCmt()` can be used to specify linear compartmental models.
