This returns the model's parameters that are required to solve the ODE system, and can be used to pipe parameters into an rxode2 solve

## Usage

```
rxParams(obj, ...)
# S3 method for class 'rxode2'
rxParams(
obj,
constants = TRUE,
...,
params = NULL,
inits = NULL,
iCov = NULL,
keep = NULL,
thetaMat = NULL,
omega = NULL,
dfSub = NULL,
sigma = NULL,
dfObs = NULL,
nSub = NULL,
nStud = NULL
)
# S3 method for class 'rxSolve'
rxParams(
obj,
constants = TRUE,
...,
params = NULL,
inits = NULL,
iCov = NULL,
keep = NULL,
thetaMat = NULL,
omega = NULL,
dfSub = NULL,
sigma = NULL,
dfObs = NULL,
nSub = NULL,
nStud = NULL
)
# S3 method for class 'rxEt'
rxParams(
obj,
...,
params = NULL,
inits = NULL,
iCov = NULL,
keep = NULL,
thetaMat = NULL,
omega = NULL,
dfSub = NULL,
sigma = NULL,
dfObs = NULL,
nSub = NULL,
nStud = NULL
)
rxParam(obj, ...)
```

## Arguments

- obj
rxode2 family of objects

- ...
Other arguments including scaling factors for each compartment. This includes S# = numeric will scale a compartment # by a dividing the compartment amount by the scale factor, like NONMEM.

- constants
is a boolean indicting if constants should be included in the list of parameters. Currently rxode2 parses constants into variables in case you wish to change them without recompiling the rxode2 model.

- 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;

- 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);

- iCov
A data frame of individual non-time varying covariates to combine with the

`events`

dataset. The`iCov`

dataset has one covariate per ID and should match the event table- keep
Columns to keep from either the input dataset or the

`iCov`

dataset. With the`iCov`

dataset, the column is kept once per line. For the input dataset, if any records are added to the data LOCF (Last Observation Carried forward) imputation is performed.- thetaMat
Named theta matrix.

- omega
Estimate of Covariance matrix. When omega is a list, assume it is a block matrix and convert it to a full matrix for simulations. When

`omega`

is`NA`

and you are using it with a`rxode2`

ui model, the between subject variability described by the`omega`

matrix are set to zero.- dfSub
Degrees of freedom to sample the between subject variability matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution.

- sigma
Named sigma covariance or Cholesky decomposition of a covariance matrix. The names of the columns indicate parameters that are simulated. These are simulated for every observation in the solved system. When

`sigma`

is`NA`

and you are using it with a`rxode2`

ui model, the unexplained variability described by the`sigma`

matrix are set to zero.- dfObs
Degrees of freedom to sample the unexplained variability matrix from the inverse Wishart distribution (scaled) or scaled inverse chi squared distribution.

- nSub
Number between subject variabilities (

`ETAs`

) simulated for every realization of the parameters.- nStud
Number virtual studies to characterize uncertainty in estimated parameters.

## Value

When extracting the parameters from an rxode2 model, a character vector listing the parameters in the model.

## See also

Other Query model information:
`rxDfdy()`

,
`rxInits()`

,
`rxLhs()`

,
`rxModelVars()`

,
`rxState()`