rxode2 {rxode2}R Documentation

Create an ODE-based model specification

Description

Create a dynamic ODE-based model object suitably for translation into fast C code

Usage

rxode2(
  model,
  modName = basename(wd),
  wd = getwd(),
  filename = NULL,
  extraC = NULL,
  debug = FALSE,
  calcJac = NULL,
  calcSens = NULL,
  collapseModel = FALSE,
  package = NULL,
  ...,
  linCmtSens = c("linCmtA", "linCmtB", "linCmtC"),
  indLin = FALSE,
  verbose = FALSE,
  fullPrint = getOption("rxode2.fullPrint", FALSE),
  envir = parent.frame()
)

RxODE(
  model,
  modName = basename(wd),
  wd = getwd(),
  filename = NULL,
  extraC = NULL,
  debug = FALSE,
  calcJac = NULL,
  calcSens = NULL,
  collapseModel = FALSE,
  package = NULL,
  ...,
  linCmtSens = c("linCmtA", "linCmtB", "linCmtC"),
  indLin = FALSE,
  verbose = FALSE,
  fullPrint = getOption("rxode2.fullPrint", FALSE),
  envir = parent.frame()
)

rxode(
  model,
  modName = basename(wd),
  wd = getwd(),
  filename = NULL,
  extraC = NULL,
  debug = FALSE,
  calcJac = NULL,
  calcSens = NULL,
  collapseModel = FALSE,
  package = NULL,
  ...,
  linCmtSens = c("linCmtA", "linCmtB", "linCmtC"),
  indLin = FALSE,
  verbose = FALSE,
  fullPrint = getOption("rxode2.fullPrint", FALSE),
  envir = parent.frame()
)

Arguments

model

This is the ODE model specification. It can be:

  • a string containing the set of ordinary differential equations (ODE) and other expressions defining the changes in the dynamic system.

  • a file name where the ODE system equation is contained

An ODE expression enclosed in ⁠\{\}⁠

(see also the filename argument). For details, see the sections “Details” and ⁠rxode2 Syntax⁠ below.

modName

a string to be used as the model name. This string is used for naming various aspects of the computations, including generating C symbol names, dynamic libraries, etc. Therefore, it is necessary that modName consists of simple ASCII alphanumeric characters starting with a letter.

wd

character string with a working directory where to create a subdirectory according to modName. When specified, a subdirectory named after the “modName.d” will be created and populated with a C file, a dynamic loading library, plus various other working files. If missing, the files are created (and removed) in the temporary directory, and the rxode2 DLL for the model is created in the current directory named ⁠rx_????_platform⁠, for example rx_129f8f97fb94a87ca49ca8dafe691e1e_i386.dll

filename

A file name or connection object where the ODE-based model specification resides. Only one of model or filename may be specified.

extraC

Extra c code to include in the model. This can be useful to specify functions in the model. These C functions should usually take double precision arguments, and return double precision values.

debug

is a boolean indicating if the executable should be compiled with verbose debugging information turned on.

calcJac

boolean indicating if rxode2 will calculate the Jacobain according to the specified ODEs.

calcSens

boolean indicating if rxode2 will calculate the sensitivities according to the specified ODEs.

collapseModel

boolean indicating if rxode2 will remove all LHS variables when calculating sensitivities.

package

Package name for pre-compiled binaries.

...

ignored arguments.

linCmtSens

The method to calculate the linCmt() solutions

indLin

Calculate inductive linearization matrices and compile with inductive linearization support.

verbose

When TRUE be verbose with the linear compartmental model

fullPrint

When using printf within the model, if TRUE print on every step (except ME/indLin), otherwise when FALSE print only when calculating the d/dt

envir

is the environment to look for R user functions (defaults to parent environment)

Details

The Rx in the name rxode2 is meant to suggest the abbreviation Rx for a medical prescription, and thus to suggest the package emphasis on pharmacometrics modeling, including pharmacokinetics (PK), pharmacodynamics (PD), disease progression, drug-disease modeling, etc.

The ODE-based model specification may be coded inside four places: 

library(rxode2)
mod <- rxode2({
  # simple assignment
  C2 <- centr/V2

  # time-derivative assignment
  d/dt(centr) <- F*KA*depot - CL*C2 - Q*C2 + Q*C3;
})

## using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
## In file included from /usr/share/R/include/R.h:71,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2parse.h:33,
##                  from /home/matt/src/rxode2/inst/include/rxode2.h:9,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2_model_shared.h:3,
##                  from rx_36fd6c4312c660ddf5390102e91a4bb3_.c:117:
## /usr/share/R/include/R_ext/Complex.h:80:6: warning: ISO C99 doesn’t support unnamed structs/unions [-Wpedantic]
##    80 |     };
##       |      ^

mod <- rxode2("
  # simple assignment
  C2 <- centr/V2

  # time-derivative assignment
  d/dt(centr) <- F*KA*depot - CL*C2 - Q*C2 + Q*C3;
")

## using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
## In file included from /usr/share/R/include/R.h:71,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2parse.h:33,
##                  from /home/matt/src/rxode2/inst/include/rxode2.h:9,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2_model_shared.h:3,
##                  from rx_4fdd80e5dd5fd33d1951b83e06f7ad84_.c:117:
## /usr/share/R/include/R_ext/Complex.h:80:6: warning: ISO C99 doesn’t support unnamed structs/unions [-Wpedantic]
##    80 |     };
##       |      ^

writeLines("
  # simple assignment
  C2 <- centr/V2

  # time-derivative assignment
  d/dt(centr) <- F*KA*depot - CL*C2 - Q*C2 + Q*C3;
", "modelFile.rxode2")
mod <- rxode2(filename='modelFile.rxode2')
unlink("modelFile.rxode2")

mod <- function() {
  model({
    # simple assignment
    C2 <- centr/V2

    # time-derivative assignment
    d/dt(centr) <- F*KA*depot - CL*C2 - Q*C2 + Q*C3;
  })
}

mod <- rxode2(mod) # or simply mod() if the model is at the end of the function

# These model functions often have residual components and initial
# (`ini({})`) conditions attached as well. For example the
# theophylline model can be written as:

one.compartment <- function() {
  ini({
    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
    add.sd <- 0.7
  })
  model({
    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
    cp ~ add(add.sd)
  })
}

# after parsing the model
mod <- one.compartment()

For the block statement, character string or text file an internal rxode2 compilation manager translates the ODE system into C, compiles it and loads it into the R session. The call to rxode2 produces an object of class rxode2 which consists of a list-like structure (environment) with various member functions.

For the last type of model (a model function), a call to rxode2 creates a parsed rxode2 ui that can be translated to the rxode2 compilation model. 

mod$simulationModel

## using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
## In file included from /usr/share/R/include/R.h:71,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2parse.h:33,
##                  from /home/matt/src/rxode2/inst/include/rxode2.h:9,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2_model_shared.h:3,
##                  from rx_bbee652505e8bddf28c7e688baef12d9_.c:117:
## /usr/share/R/include/R_ext/Complex.h:80:6: warning: ISO C99 doesn’t support unnamed structs/unions [-Wpedantic]
##    80 |     };
##       |      ^

## rxode2 2.1.2.9000 model named rx_bbee652505e8bddf28c7e688baef12d9 model (ready). 
## x$state: depot, center
## x$stateExtra: cp
## x$params: tka, tcl, tv, add.sd, eta.ka, eta.cl, eta.v, rxerr.cp
## x$lhs: ka, cl, v, cp, ipredSim, sim

# or 
mod$simulationIniModel

## using C compiler: ‘gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0’
## In file included from /usr/share/R/include/R.h:71,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2parse.h:33,
##                  from /home/matt/src/rxode2/inst/include/rxode2.h:9,
##                  from /home/matt/R/x86_64-pc-linux-gnu-library/4.3/rxode2parse/include/rxode2_model_shared.h:3,
##                  from rx_fe01e26500a95dd1ba4d8972bc71bc7a_.c:117:
## /usr/share/R/include/R_ext/Complex.h:80:6: warning: ISO C99 doesn’t support unnamed structs/unions [-Wpedantic]
##    80 |     };
##       |      ^

## rxode2 2.1.2.9000 model named rx_fe01e26500a95dd1ba4d8972bc71bc7a model (ready). 
## x$state: depot, center
## x$stateExtra: cp
## x$params: tka, tcl, tv, add.sd, eta.ka, eta.cl, eta.v, rxerr.cp
## x$lhs: ka, cl, v, cp, ipredSim, sim

This is the same type of function required for nlmixr2 estimation and can be extended and modified by model piping. For this reason will be focused on in the documentation.

This basic model specification consists of one or more statements optionally terminated by semi-colons ⁠;⁠ and optional comments (comments are delimited by ⁠#⁠ and an end-of-line).

A block of statements is a set of statements delimited by curly braces, { ... }.

Statements can be either assignments, conditional if/⁠else if⁠/⁠else⁠, while loops (can be exited by break), special statements, or printing statements (for debugging/testing).

Assignment statements can be:

Note that assignment can be done by =, ⁠<-⁠ or ~.

When assigning with the ~ operator, the simple assignments and time-derivative assignments will not be output. Note that with the rxode2 model functions assignment with ~ can also be overloaded with a residual distribution specification.

Special statements can be:

An example model is shown below: 

   # simple assignment
   C2 <- centr/V2

   # time-derivative assignment
   d/dt(centr) <- F*KA*depot - CL*C2 - Q*C2 + Q*C3;

Expressions in assignment and if statements can be numeric or logical.

Numeric expressions can include the following numeric operators ⁠+, -, *, /, ^⁠ and those mathematical functions defined in the C or the R math libraries (e.g., fabs, exp, log, sin, abs).

You may also access the R’s functions in the R math libraries, like lgammafn for the log gamma function.

The rxode2 syntax is case-sensitive, i.e., ABC is different than abc, Abc, ABc, etc.

Identifiers

Like R, Identifiers (variable names) may consist of one or more alphanumeric, underscore ⁠_⁠ or period . characters, but the first character cannot be a digit or underscore ⁠_⁠.

Identifiers in a model specification can refer to:

Currently, the rxode2 modeling language only recognizes system state variables and “parameters”, thus, any values that need to be passed from R to the ODE model (e.g., age) should be either passed in the params argument of the integrator function rxSolve() or be in the supplied event data-set.

There are certain variable names that are in the rxode2 event tables. To avoid confusion, the following event table-related items cannot be assigned, or used as a state but can be accessed in the rxode2 code:

However the following variables are cannot be used in a model specification:

Sometimes rxode2 generates variables that are fed back to rxode2. Similarly, nlmixr2 generates some variables that are used in nlmixr estimation and simulation. These variables start with the either the rx or nlmixr prefixes. To avoid any problems, it is suggested to not use these variables starting with either the rx or nlmixr prefixes.

Logical Operators

Logical operators support the standard R operators ==, != >= <= > and <. Like R these can be in ⁠if()⁠ or ⁠while()⁠ statements, ifelse() expressions. Additionally they can be in a standard assignment. For instance, the following is valid: 

cov1 = covm*(sexf == "female") + covm*(sexf != "female")

Notice that you can also use character expressions in comparisons. This convenience comes at a cost since character comparisons are slower than numeric expressions. Unlike R, as.numeric or as.integer for these logical statements is not only not needed, but will cause an syntax error if you try to use the function.

Supported functions

All the supported functions in rxode2 can be seen with the rxSupportedFuns().

A brief description of the built-in functions are in the following table:

Function Description Aliases
gamma(x) The Gamma function gammafn
lgamma(x) Natural logarithm of absolute value of gamma function digamma
digamma(x) First derivative of lgamma
trigamma(x) Second derivative of lgamma
tetragamma(x) Third derivative of lgamma
pentagamma(x) Fourth derivative of lgamma
psigamma(x, deriv) n-th derivative of Psi, the digamma function, which is the derivative of lgammafn. In other words, digamma(x) is the same as psigamma(x,0), trigamma(x) == psigamma(x,1), etc.
cospi(x) cos(pi*x)
sinpi(x) sin(pi*x)
tanpi(x) tan(pi*x)
beta(a, b) Beta function
lbeta(a, b) log Beta function
bessel_i(x, nu, expo) Bessel function type I with index nu expo==1 is unscaled expo==2 is scaled by exp(-x)
bessel_j(x, nu) Bessel function type J with index nu
bessel_k(x, ku, expo) Bessel function type K with index nu expo==1 is unscaled expo==2 is scaled by exp(x)
bessel_y(x, nu) Bessel function type Y with index nu
R_pow(x, y) x^y
R_pow_di(x, I) x^y y is an integer
log1pmx log(1+x) - x
log1pexp log(1+exp(x))
expm1(x) exp(x)-1
lgamma1p(x) log(gamma(x+1))
sign(x) Compute the signum function where sign(x) is 1, 0 -1
fsign(x, y) abs(x)*sign(y)
fprec(x, digits) x rounded to digits (after the decimal point, used by signif()
fround(x, digits) Round, used by R’s round()
ftrunc(x) Truncated towards zero
abs(x) absolute value of x fabs
sin(x) sine of x
cos(x) cos of x
tan(x) tan of x
factorial(x) factorial of x
lfactorial(x) log(factorial(x))
log10(x) log base 10
log2(x) log base 2
pnorm(x) Normal CDF of x normcdf, phi
qnorm(x) Normal pdf of x norminv
probit(x, low=0, hi=1) Probit (normal pdf) of x transforming into a range
probitInv(q, low=0, hi=1) Inverse probit of x transforming into a range
acos(x) Inverse cosine
asin(x) Inverse sine
atan(x) Inverse tangent
atan2(a, b) Four quadrant inverse tangent
sinh(x) Hyperbolic sine
cosh(x) Hyperbolic cosine
tanh(x) Hyperbolic tangent
floor(x) Downward rounding
ceil(x) Upward rounding
logit(x, low=0, hi=1) Logit transformation of x transforming into a range
expit(x, low=0, hi=1) expit transofmration in range invLogit, logitInv
gammaq(a, z) Normalized incomplete gamma from boost
gammaqInv(a, q) Normalized incomplete gamma inverse from boost
ifelse(cond, trueValue, falseValue) if else function
gammap(a, z) Normalized lower incomplete gamma from boost
gammapInv(a, p) Inverse of Normalized lower incomplete gamma from boost
gammapInva(x, p) Inverse of Normalized lower incomplete gamma from boost
rxnorm(x) Generate one deviate of from a normal distribution for each observation scale
rxnormV(x) Generate one deviate from low discrepancy normal for each observation
rxcauchy Generate one deviate from the cauchy distribution for each observation
rxchisq Generate one deviate from the chisq distribution for each observation
rxexp Generate one deviate from the exponential distribution for each observation
rxf Generate one deviate from low discrepancy normal for each observation
rxgamma Generate one deviate from the gamma distribution for each observation
rxbeta Generate one deviate from the beta distribution for each observation
rxgeom Generate one deviate from the geometric distribution for each observation
rxpois Generate one deviate from the poission distribution for each observation
rxt Generate one deviate from the t distribtuion for each observation
tad() or tad(x) Time after dose (tad()) or time after dose for a compartment tad(cmt)
tafd() or tafd(x) Time after first dose (tafd()) or time after first dose for a compartment tafd(cmt)
dosenum() Dose Number
tlast() or tlast(cmt) Time of Last dose; This takes into consideration any lag time, so if there is a dose at time 3 and a lag of 1, the time of last dose would be 4. tlast(cmt) calculates the time since last dose of a compartment
tfirst() or tfirst(cmt) Time since first dose or time since first dose of a compartment
prod(…) product of terms; This uses PreciseSums so the product will not have as much floating point errors (though it will take longer)
sum(…) sum of terms; This uses PreciseSums so the product will not have as much floating point errors (though it will take longer)
max(…) maximum of a group of numbers
min(…) Min of a group of numbers
lag(parameter, number=1) Get the lag of an input parameter; You can specify a number of lagged observations
lead(parameter, number=2) Get the lead of an input parameter; You can specify a number of lead observation
diff(par, number=1) Get the difference between the current parameter and the last parameter; Can change the parameter number
first(par) Get the first value of an input parameter
last(par) Get the last value of an input parameter
transit() The transit compartment psuedo function
is.na() Determine if a value is NA
is.nan() Determine if a value is NaN
is.infinite() Check to see if the value is infinite
rinorm(x) Generate one deviate of from a normal distribution for each individual
rinormV(x) Generate one deviate from low discrepancy normal for each individual
ricauchy Generate one deviate from the cauchy distribution for each individual
richisq Generate one deviate from the chisq distribution for each individual
riexp Generate one deviate from the exponential distribution for each individual
rif Generate one deviate from low discrepancy normal for each individual
rigamma Generate one deviate from the gamma distribution for each individual
ribeta Generate one deviate from the beta distribution for each individual
rigeom Generate one deviate from the geometric distribution for each individual
ropois Generate one deviate from the poission distribution for each individual
rit Generate one deviate from the t distribtuion for each individual
simeps Simulate EPS from possibly truncated sigma matrix. Will take sigma matrix from the current study. Simulated at the very last moment.
simeta Simulate ETA from possibly truncated omega matrix. Will take the omega matrix from the current study. Simulated at the initilization of the ODE system or the intialization of lhs

Note that ⁠lag(cmt) =⁠ is equivalent to ⁠alag(cmt) =⁠ and not the same as ⁠= lag(wt)⁠

Reserved keywords

There are a few reserved keywords in a rxode2 model. They are in the following table:

Reserved Name Meaning Alias
time solver time t
podo In Transit compartment models, last dose amount
tlast Time of Last dose
M_E Exp(1)
M_LOG2E log2(e)
M_LOG10E log10(e)
M_LN2 log(2)
M_LN10 log(10)
M_PI pi
M_PI_2 pi/2
M_PI_4 pi/4
M_1_PI 1/pi
M_2_PI 2/pi
M_2_SQRTPI 2/sqrt(pi)
M_SQRT2 sqrt(2)
M_SQRT1_2 1/sqrt(2)
M_SQRT_3 sqrt(3)
M_SQRT_32 sqrt(32)
M_LOG10_2 Log10(2)
M_2PI 2*pi
M_SQRT_PI sqrt(pi)
M_1_SQRT_2PI 1/(sqrt(2*pi))
M_LN_SQRT_PI log(sqrt(pi))
M_LN_SQRT_2PI log(sqrt(2*pi))
M_LN_SQRT_PId2 log(sqrt(pi/2))
pi pi
NA R’s NA value
NaN Not a Number Value
Inf Infinite Value
newind 1: First record of individual; 2: Subsequent record of individual NEWIND
rxFlag Flag for what part of the rxode2 model is being run; 1: ddt; 2: jac; 3: ini; 4: F; 5: lag; 6: rate; 7: dur; 8: mtime; 9: matrix exponential; 10: inductive linearization; 11: lhs

Note that rxFlag will always output 11 or calc_lhs since that is where the final variables are calculated, though you can tweak or test certain parts of rxode2 by using this flag.

Residual functions when using rxode2 functions

In addition to ~ hiding output for certain types of output, it also is used to specify a residual output or endpoint when the input is an rxode2 model function (that includes the residual in the model({}) block).

These specifications are of the form: 

var ~ add(add.sd)

Indicating the variable var is the variable that represents the individual central tendencies of the model and it also represents the compartment specification in the data-set.

You can also change the compartment name using the | syntax, that is: 

var ~ add(add.sd) | cmt

In the above case var represents the central tendency and cmt represents the compartment or dvid specification.

Transformations

For normal and related distributions, you can apply the transformation on both sides by using some keywords/functions to apply these transformations.

Transformation rxode2/nlmixr2 code
Box-Cox +boxCox(lambda)
Yeo-Johnson +yeoJohnson(lambda)
logit-normal +logitNorm(logit.sd, low, hi)
probit-normal +probitNorm(probid.sd, low, hi)
log-normal +lnorm(lnorm.sd)

By default for the likelihood for all of these transformations is calculated on the untransformed scale.

For bounded variables like logit-normal or probit-normal the low and high values are defaulted to 0 and 1 if missing.

For models where you wish to have a proportional model on one of these transformation you can replace the standard deviation with NA

To allow for more transformations, lnorm(), probitNorm() and logitNorm() can be combined the variance stabilizing yeoJohnson() transformation.

Normal and t-related distributions

For the normal and t-related distributions, we wanted to keep the ability to use skewed distributions additive and proportional in the t/cauchy-space, so these distributions are specified differently in comparison to the other supported distributions within nlmixr2:

Distribution How to Add Example
Normal (log-likelihood) +dnorm() cc ~ add(add.sd) + dnorm()
T-distribution +dt(df) cc ~a dd(add.sd) + dt(df)
Cauchy (t with df=1) +dcauchy() cc ~ add(add.sd) + dcauchy()

Note that with the normal and t-related distributions nlmixr2 will calculate cwres and npde under the normal assumption to help assess the goodness of the fit of the model.

Also note that the +dnorm() is mostly for testing purposes and will slow down the estimation procedure in nlmixr2. We suggest not adding it (except for explicit testing). When there are multiple endpoint models that mix non-normal and normal distributions, the whole problem is shifted to a log-likelihood method for estimation in nlmixr2.

Notes on additive + proportional models

There are two different ways to specify additive and proportional models, which we will call combined1 and combined2, the same way that Monolix calls the two distributions (to avoid between software differences in naming).

The first, combined1, assumes that the additive and proportional differences are on the standard deviation scale, or:

y=f+(a+b* f^c)*err

The second, combined2, assumes that the additive and proportional differences are combined on a variance scale:

y=f+[sqrt(a^2+b^2 *f^(2c))]*err

The default in nlmixr2/rxode2 if not otherwise specified is combined2 since it mirrors how adding 2 normal distributions in statistics will add their variances (not the standard deviations). However, the combined1 can describe the data possibly even better than combined2 so both are possible options in rxode2/nlmixr2.

Distributions of known likelihoods

For residuals that are not related to normal, t-distribution or cauchy, often the residual specification is of the form: 

cmt ~ dbeta(alpha, beta)

Where the compartment specification is on the left handed side of the specification.

For generalized likelihood you can specify: 

ll(cmt) ~ llik specification
Ordinal likelihoods

Finally, ordinal likelihoods/simulations can be specified in 2 ways. The first is: 

err ~ c(p0, p1, p2)

Here err represents the compartment and p0 is the probability of being in a specific category:

Category Probability
1 p0
2 p1
3 p2
4 1-p0-p1-p2

It is up to the model to ensure that the sum of the p values are less than 1. Additionally you can write an arbitrary number of categories in the ordinal model described above.

It seems a little off that p0 is the probability for category 1 and sometimes scores are in non-whole numbers. This can be modeled as follows: 

err ~ c(p0=0, p1=1, p2=2, 3)

Here the numeric categories are specified explicitly, and the probabilities remain the same:

Category Probability
0 p0
1 p1
2 p2
3 1-p0-p1-p2
General table of supported residual distributions

In general all the that are supported are in the following table (available in rxode2::rxResidualError)

Error model Functional Form Transformation code addProp lhs
constant None var ~ add(add.sd) response variable
proportional None var ~ prop(prop.sd) response variable
power None var ~ pow(pow.sd, exponent) response variable
additive+proportional combined1 None var ~ add(add.sd) + prop(prop.sd) + combined1() addProp=1 response variable
additive+proportional combined2 None var ~ add(add.sd) + prop(prop.sd) + combined2() addProp=2 response variable
additive+power combined1 None var ~ add(add.sd) + pow(pow.sd, exponent) + combined1() addProp=1 response variable
additive+power combined2 None var ~ add(add.sd) + pow(pow.sd, exponent) + combined2() addProp=2 response variable
constant log var ~ lnorm(add.sd) response variable
proportional log var ~ lnorm(NA) + prop(prop.sd) response variable
power log var ~ lnorm(NA) + pow(pow.sd, exponent) response variable
additive+proportional combined1 log var ~ lnorm(add.sd) + prop(prop.sd) + combined1() addProp=1 response variable
additive+proportional combined2 log var ~ lnorm(add.sd) + prop(prop.sd) + combined2() addProp=2 response variable
additive+power combined1 log var ~ lnorm(add.sd) + pow(pow.sd, exponent) + combined1() addProp=1 response variable
additive+power combined2 log var ~ lnorm(add.sd) + pow(pow.sd, exponent) + combined2() addProp=2 response variable
constant boxCox var ~ boxCox(lambda) + add(add.sd) response variable
proportional boxCox var ~ boxCox(lambda) + prop(prop.sd) response variable
power boxCox var ~ boxCox(lambda) + pow(pow.sd, exponent) response variable
additive+proportional combined1 boxCox var ~ boxCox(lambda) + add(add.sd) + prop(prop.sd) + combined1() addProp=1 response variable
additive+proportional combined2 boxCox var ~ boxCox(lambda) + add(add.sd) + prop(prop.sd) + combined2() addProp=2 response variable
additive+power combined1 boxCox var ~ boxCox(lambda) + add(add.sd) + pow(pop.sd, exponent) + combined1() addProp=1 response variable
additive+power combined2 boxCox var ~ boxCox(lambda) + add(add.sd) + pow(pop.sd, exponent) + combined2() addProp=2 response variable
constant yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) response variable
proportional yeoJohnson var ~ yeoJohnson(lambda) + prop(prop.sd) response variable
power yeoJohnson var ~ yeoJohnson(lambda) + pow(pow.sd, exponent) response variable
additive+proportional combined1 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + prop(prop.sd) + combined1() addProp=1 response variable
additive+proportional combined2 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + prop(prop.sd) + combined2() addProp=2 response variable
additive+power combined1 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + pow(pop.sd, exponent) + combined1() addProp=1 response variable
additive+power combined2 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + pow(pop.sd, exponent) + combined2() addProp=2 response variable
constant logit var ~ logitNorm(logit.sd) response variable
proportional logit var ~ logitNorm(NA) + prop(prop.sd) response variable
power logit var ~ logitNorm(NA) + pow(pow.sd, exponent) response variable
additive+proportional combined1 logit var ~ logitNorm(logit.sd) + prop(prop.sd) addProp=1 response variable
additive+proportional combined2 logit var ~ logitNorm(logit.sd) + prop(prop.sd) addProp=2 response variable
additive+power combined1 logit var ~ logitNorm(logit.sd) + pow(pow.sd, exponent) addProp=1 response variable
additive+power combined2 logit var ~ logitNorm(logit.sd) + pow(pow.sd, exponent) addProp=2 response variable
additive yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) response variable
proportional yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(NA) + prop(prop.sd) response variable
power yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(NA) + pow(pow.sd, exponent) response variable
additive+proportional combined1 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + prop(prop.sd) addProp=1 response variable
additive+proportional combined2 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + prop(prop.sd) addProp=2 response variable
additive+power combined1 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + pow(pow.sd, exponent) addProp=1 response variable
additive+power combined2 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + pow(pow.sd, exponent) addProp=2 response variable
constant logit var ~ probitNorm(probit.sd) response variable
proportional probit var ~ probitNorm(NA) + prop(prop.sd) response variable
power probit var ~ probitNorm(NA) + pow(pow.sd, exponent) response variable
additive+proportional combined1 probit var ~ probitNorm(probit.sd) + prop(prop.sd) + combined1() addProp=1 response variable
additive+proportional combined2 probit var ~ probitNorm(probit.sd) + prop(prop.sd) + combined2() addProp=2 response variable
additive+power combined1 probit var ~ probitNorm(probit.sd) + pow(pow.sd, exponent) + combined1() addProp=1 response variable
additive+power combined2 probit var ~ probitNorm(probit.sd) + pow(pow.sd, exponent) + combined2() addProp=2 response variable
additive yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) response variable
proportional yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(NA) + prop(prop.sd) response variable
power yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(NA) + pow(pow.sd, exponent) response variable
additive+proportional combined1 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + prop(prop.sd) + combined1() addProp=1 response variable
additive+proportional combined2 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + prop(prop.sd) + combined2() addProp=2 response variable
additive+power combined1 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + pow(pow.sd, exponent) + combined1() addProp=1 response variable
additive+power combined2 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + pow(pow.sd, exponent) + combined2() addProp=2 response variable
constant+t None var ~ add(add.sd) + dt(df) response variable
proportional+t None var ~ prop(prop.sd) + dt(df) response variable
power+t None var ~ pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 None var ~ add(add.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 None var ~ add(add.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 None var ~ add(add.sd) + pow(pow.sd, exponent) + dt(df) +combined1() addProp=1 response variable
additive+power+t combined2 None var ~ add(add.sd) + pow(pow.sd, exponent) + dt(df) +combined2() addProp=2 response variable
constant+t log var ~ lnorm(add.sd) + dt(df) response variable
proportional+t log var ~ lnorm(NA) + prop(prop.sd) + dt(df) response variable
power+t log var ~ lnorm(NA) + pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 log var ~ lnorm(add.sd) + prop(prop.sd) + dt(df) +combined1() addProp=1 response variable
additive+proportional+t combined2 log var ~ lnorm(add.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 log var ~ lnorm(add.sd) + pow(pow.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 log var ~ lnorm(add.sd) + pow(pow.sd, exponent) + dt(df) + combined2() addProp=2 response variable
constant+t boxCox var ~ boxCox(lambda) + add(add.sd)+dt(df) response variable
proportional+t boxCox var ~ boxCox(lambda) + prop(prop.sd)+dt(df) response variable
power+t boxCox var ~ boxCox(lambda) + pow(pow.sd, exponent)+dt(df) response variable
additive+proportional+t combined1 boxCox var ~ boxCox(lambda) + add(add.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 boxCox var ~ boxCox(lambda) + add(add.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 boxCox var ~ boxCox(lambda) + add(add.sd) + pow(pop.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 boxCox var ~ boxCox(lambda) + add(add.sd) + pow(pop.sd, exponent) + dt(df) + combined2() addProp=2 response variable
constant+t yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + dt(df) response variable
proportional+t yeoJohnson var ~ yeoJohnson(lambda) + prop(prop.sd) + dt(df) response variable
power+t yeoJohnson var ~ yeoJohnson(lambda) + pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + pow(pop.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + pow(pop.sd, exponent) + dt(df) + combined2() addProp=2 response variable
constant+t logit var ~ logitNorm(logit.sd)+dt(df) response variable
proportional+t logit var ~ logitNorm(NA) + prop(prop.sd)+dt(df) response variable
power+t logit var ~ logitNorm(NA) + pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 logit var ~ logitNorm(logit.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 logit var ~ logitNorm(logit.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 logit var ~ logitNorm(logit.sd) + pow(pow.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 logit var ~ logitNorm(logit.sd) + pow(pow.sd, exponent) + dt(df) + combined2() addProp=2 response variable
additive+t yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + dt(df) response variable
proportional+t yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(NA) + prop(prop.sd) + dt(df) response variable
power+t yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(NA) + pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + pow(pow.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + pow(pow.sd, exponent) + dt(df) + combined2() addProp=2 response variable
constant+t logit var ~ probitNorm(probit.sd) + dt(df) response variable
proportional+t probit var ~ probitNorm(NA) + prop(prop.sd) + dt(df) response variable
power+t probit var ~ probitNorm(NA) + pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 probit var ~ probitNorm(probit.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 probit var ~ probitNorm(probit.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 probit var ~ probitNorm(probit.sd) + pow(pow.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 probit var ~ probitNorm(probit.sd) + pow(pow.sd, exponent) + dt(df) + combined2() addProp=2 response variable
additive+t yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + dt(df) response variable
proportional+t yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(NA) + prop(prop.sd) + dt(df) response variable
power+t yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(NA) + pow(pow.sd, exponent) + dt(df) response variable
additive+proportional+t combined1 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + prop(prop.sd) + dt(df) + combined1() addProp=1 response variable
additive+proportional+t combined2 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + prop(prop.sd) + dt(df) + combined2() addProp=2 response variable
additive+power+t combined1 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + pow(pow.sd, exponent) + dt(df) + combined1() addProp=1 response variable
additive+power+t combined2 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + pow(pow.sd, exponent) + dt(df) +combined2() addProp=2 response variable
constant+cauchy None var ~ add(add.sd) + dcauchy() response variable
proportional+cauchy None var ~ prop(prop.sd) + dcauchy() response variable
power+cauchy None var ~ pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 None var ~ add(add.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 None var ~ add(add.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 None var ~ add(add.sd) + pow(pow.sd, exponent) + dcauchy() +combined1() addProp=1 response variable
additive+power+cauchy combined2 None var ~ add(add.sd) + pow(pow.sd, exponent) + dcauchy() +combined2() addProp=2 response variable
constant+cauchy log var ~ lnorm(add.sd) + dcauchy() response variable
proportional+cauchy log var ~ lnorm(NA) + prop(prop.sd) + dcauchy() response variable
power+cauchy log var ~ lnorm(NA) + pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 log var ~ lnorm(add.sd) + prop(prop.sd) + dcauchy() +combined1() addProp=1 response variable
additive+proportional+cauchy combined2 log var ~ lnorm(add.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 log var ~ lnorm(add.sd) + pow(pow.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 log var ~ lnorm(add.sd) + pow(pow.sd, exponent) + dcauchy() + combined2() addProp=2 response variable
constant+cauchy boxCox var ~ boxCox(lambda) + add(add.sd)+dcauchy() response variable
proportional+cauchy boxCox var ~ boxCox(lambda) + prop(prop.sd)+dcauchy() response variable
power+cauchy boxCox var ~ boxCox(lambda) + pow(pow.sd, exponent)+dcauchy() response variable
additive+proportional+cauchy combined1 boxCox var ~ boxCox(lambda) + add(add.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 boxCox var ~ boxCox(lambda) + add(add.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 boxCox var ~ boxCox(lambda) + add(add.sd) + pow(pop.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 boxCox var ~ boxCox(lambda) + add(add.sd) + pow(pop.sd, exponent) + dcauchy() + combined2() addProp=2 response variable
constant+cauchy yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + dcauchy() response variable
proportional+cauchy yeoJohnson var ~ yeoJohnson(lambda) + prop(prop.sd) + dcauchy() response variable
power+cauchy yeoJohnson var ~ yeoJohnson(lambda) + pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + pow(pop.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 yeoJohnson var ~ yeoJohnson(lambda) + add(add.sd) + pow(pop.sd, exponent) + dcauchy() + combined2() addProp=2 response variable
constant+cauchy logit var ~ logitNorm(logit.sd)+dcauchy() response variable
proportional+cauchy logit var ~ logitNorm(NA) + prop(prop.sd)+dcauchy() response variable
power+cauchy logit var ~ logitNorm(NA) + pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 logit var ~ logitNorm(logit.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 logit var ~ logitNorm(logit.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 logit var ~ logitNorm(logit.sd) + pow(pow.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 logit var ~ logitNorm(logit.sd) + pow(pow.sd, exponent) + dcauchy() + combined2() addProp=2 response variable
additive+cauchy yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + dcauchy() response variable
proportional+cauchy yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(NA) + prop(prop.sd) + dcauchy() response variable
power+cauchy yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(NA) + pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + pow(pow.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 yeoJohnson(logit()) var ~ yeoJohnson(lambda) + logitNorm(logit.sd) + pow(pow.sd, exponent) + dcauchy() + combined2() addProp=2 response variable
constant+cauchy logit var ~ probitNorm(probit.sd) + dcauchy() response variable
proportional+cauchy probit var ~ probitNorm(NA) + prop(prop.sd) + dcauchy() response variable
power+cauchy probit var ~ probitNorm(NA) + pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 probit var ~ probitNorm(probit.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 probit var ~ probitNorm(probit.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 probit var ~ probitNorm(probit.sd) + pow(pow.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 probit var ~ probitNorm(probit.sd) + pow(pow.sd, exponent) + dcauchy() + combined2() addProp=2 response variable
additive+cauchy yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + dcauchy() response variable
proportional+cauchy yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(NA) + prop(prop.sd) + dcauchy() response variable
power+cauchy yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(NA) + pow(pow.sd, exponent) + dcauchy() response variable
additive+proportional+cauchy combined1 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + prop(prop.sd) + dcauchy() + combined1() addProp=1 response variable
additive+proportional+cauchy combined2 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + prop(prop.sd) + dcauchy() + combined2() addProp=2 response variable
additive+power+cauchy combined1 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + pow(pow.sd, exponent) + dcauchy() + combined1() addProp=1 response variable
additive+power+cauchy combined2 yeoJohnson(probit()) var ~ yeoJohnson(lambda) + probitNorm(probit.sd) + pow(pow.sd, exponent) + dcauchy() +combined2() addProp=2 response variable
poission none cmt ~ dpois(lamba) compartment specification
binomial none cmt ~ dbinom(n, p) compartment specification
beta none cmt ~ dbeta(alpha, beta) compartment specification
chisq none cmt ~ dchisq(nu) compartment specification
exponential none cmt ~ dexp(r) compartment specification
uniform none cmt ~ dunif(a, b) compartment specification
weibull none cmt ~ dweibull(a, b) compartment specification
gamma none cmt ~ dgamma(a, b) compartment specification
geometric none cmt ~ dgeom(a) compartment specification
negative binomial form #1 none cmt ~ dnbinom(n, p) compartment specification
negative binomial form #2 none cmt ~ dnbinomMu(size, mu) compartment specification
ordinal probability none cmt ~ c(p0=0, p1=1, p2=2, 3) compartment specification
log-likelihood none ll(cmt) ~ log likelihood expression likelihood + compartment expression

Value

An object (environment) of class rxode2 (see Chambers and Temple Lang (2001)) consisting of the following list of strings and functions: 

* `model` a character string holding the source model specification.
* `get.modelVars`a function that returns a list with 3 character
    vectors, `params`, `state`, and `lhs` of variable names used in the model
    specification. These will be output when the model is computed (i.e., the ODE solved by integration).

  * `solve`{this function solves (integrates) the ODE. This
      is done by passing the code to [rxSolve()].
      This is as if you called `rxSolve(rxode2object, ...)`,
      but returns a matrix instead of a rxSolve object.

      `params`: a numeric named vector with values for every parameter
      in the ODE system; the names must correspond to the parameter
      identifiers used in the ODE specification;

      `events`: an `eventTable` object describing the
      input (e.g., doses) to the dynamic system and observation
      sampling time points (see  [eventTable()]);

      `inits`: a vector of initial values of the state variables
      (e.g., amounts in each compartment), and the order in this vector
      must be the same as the state variables (e.g., PK/PD compartments);


      `stiff`: a logical (`TRUE` by default) indicating whether
      the ODE system is stiff or not.

      For stiff ODE systems (`stiff = TRUE`), `rxode2` uses
      the LSODA (Livermore Solver for Ordinary Differential Equations)
      Fortran package, which implements an automatic method switching
      for stiff and non-stiff problems along the integration interval,
      authored by Hindmarsh and Petzold (2003).

      For non-stiff systems (`stiff = FALSE`), `rxode2` uses `DOP853`,
      an explicit Runge-Kutta method of order 8(5, 3) of Dormand and Prince
      as implemented in C by Hairer and Wanner (1993).

      `trans_abs`: a logical (`FALSE` by default) indicating
      whether to fit a transit absorption term
      (TODO: need further documentation and example);

      `atol`: a numeric absolute tolerance (1e-08 by default);

      `rtol`: a numeric relative tolerance (1e-06 by default).

      The output of \dQuote{solve} is a matrix with as many rows as there
      are sampled time points and as many columns as system variables
      (as defined by the ODEs and additional assignments in the rxode2 model
          code).}

  * `isValid` a function that (naively) checks for model validity,
      namely that the C object code reflects the latest model
      specification.
  * `version` a string with the version of the `rxode2`
      object (not the package).
  * `dynLoad` a function with one `force = FALSE` argument
      that dynamically loads the object code if needed.
  * `dynUnload` a function with no argument that unloads
      the model object code.
  * `delete` removes all created model files, including C and DLL files.
      The model object is no longer valid and should be removed, e.g.,
      `rm(m1)`.
  * `run` deprecated, use `solve`.
  * `get.index` deprecated.
  * `getObj` internal (not user callable) function.

Creating rxode2 models

NA

Author(s)

Melissa Hallow, Wenping Wang and Matthew Fidler

References

Chamber, J. M. and Temple Lang, D. (2001) Object Oriented Programming in R. R News, Vol. 1, No. 3, September 2001. https://cran.r-project.org/doc/Rnews/Rnews_2001-3.pdf.

Hindmarsh, A. C. ODEPACK, A Systematized Collection of ODE Solvers. Scientific Computing, R. S. Stepleman et al. (Eds.), North-Holland, Amsterdam, 1983, pp. 55-64.

Petzold, L. R. Automatic Selection of Methods for Solving Stiff and Nonstiff Systems of Ordinary Differential Equations. Siam J. Sci. Stat. Comput. 4 (1983), pp. 136-148.

Hairer, E., Norsett, S. P., and Wanner, G. Solving ordinary differential equations I, nonstiff problems. 2nd edition, Springer Series in Computational Mathematics, Springer-Verlag (1993).

Plevyak, J. dparser, https://dparser.sourceforge.net/. Web. 12 Oct. 2015.

See Also

eventTable(), et(), add.sampling(), add.dosing()

Examples



mod <- function() {
  ini({
    KA   <- .291
    CL   <- 18.6
    V2   <- 40.2
    Q    <- 10.5
    V3   <- 297.0
    Kin  <- 1.0
    Kout <- 1.0
    EC50 <- 200.0
  })
  model({
    # A 4-compartment model, 3 PK and a PD (effect) compartment
    # (notice state variable names 'depot', 'centr', 'peri', 'eff')
    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
  })
}

m1 <- rxode2(mod)
print(m1)

# Step 2 - Create the model input as an EventTable,
# including dosing and observation (sampling) events

# QD (once daily) dosing for 5 days.

qd <- et(amountUnits = "ug", timeUnits = "hours") %>%
  et(amt = 10000, addl = 4, ii = 24)

# Sample the system hourly during the first day, every 8 hours
# then after
qd <- qd %>% et(0:24) %>%
  et(from = 24 + 8, to = 5 * 24, by = 8)

# Step 3 - solve the system

qd.cp <- rxSolve(m1, qd)

head(qd.cp)
[Package rxode2 version 2.1.3 Index]