`R/utils.R`

`rxDerived.Rd`

This calculates the derived parameters based on what is provided in a data frame or arguments

`rxDerived(..., verbose = FALSE, digits = 0)`

- ...
The input can be:

A data frame with PK parameters in it; This should ideally be a data frame with one pk parameter per row since it will output a data frame with one PK parameter per row.

PK parameters as either a vector or a scalar

- verbose
boolean that when TRUE provides a message about the detected pk parameters and the detected compartmental model. By default this is

`FALSE`

.- digits
represents the number of significant digits for the output; If the number is zero or below (default), do not round.

Return a data.frame of derived PK parameters for a 1-, 2-, or 3-compartment linear model given provided clearances and volumes based on the inferred model type.

The model parameters that will be provided in the data frame are:

`vc`

: Central Volume (for 1-, 2- and 3- compartment models)`kel`

: First-order elimination rate (for 1-, 2-, and 3-compartment models)`k12`

: First-order rate of transfer from central to first peripheral compartment; (for 2- and 3-compartment models)`k21`

: First-order rate of transfer from first peripheral to central compartment, (for 2- and 3-compartment models)`k13`

: First-order rate of transfer from central to second peripheral compartment; (3-compartment model)`k31`

: First-order rate of transfer from second peripheral to central compartment (3-compartment model)`vp`

: Peripheral Volume (for 2- and 3- compartment models)`vp2`

: Peripheral Volume for 3rd compartment (3- compartment model)`vss`

: Volume of distribution at steady state; (1-, 2-, and 3-compartment models)`t12alpha`

: \(t_{1/2,\alpha}\); (1-, 2-, and 3-compartment models)`t12beta`

: \(t_{1/2,\beta}\); (2- and 3-compartment models)`t12gamma`

: \(t_{1/2,\gamma}\); (3-compartment model)`alpha`

: \(\alpha\); (1-, 2-, and 3-compartment models)`beta`

: \(\beta\); (2- and 3-compartment models)`gamma`

: \(\beta\); (3-compartment model)`A`

: true`A`

; (1-, 2-, and 3-compartment models)`B`

: true`B`

; (2- and 3-compartment models)`C`

: true`C`

; (3-compartment model)`fracA`

: fractional A; (1-, 2-, and 3-compartment models)`fracB`

: fractional B; (2- and 3-compartment models)`fracC`

: fractional C; (3-compartment model)

Shafer S. L. `CONVERT.XLS`

Rowland M, Tozer TN. Clinical Pharmacokinetics and Pharmacodynamics: Concepts and Applications (4th). Clipping Williams & Wilkins, Philadelphia, 2010.

```
## Note that rxode2 parses the names to figure out the best PK parameter
params <- rxDerived(cl = 29.4, v = 23.4, Vp = 114, vp2 = 4614, q = 270, q2 = 73)
## That is why this gives the same results as the value before
params <- rxDerived(CL = 29.4, V1 = 23.4, V2 = 114, V3 = 4614, Q2 = 270, Q3 = 73)
## You may also use micro-constants alpha/beta etc.
params <- rxDerived(k12 = 0.1, k21 = 0.2, k13 = 0.3, k31 = 0.4, kel = 10, v = 10)
## or you can mix vectors and scalars
params <- rxDerived(CL = 29.4, V = 1:3)
## If you want, you can round to a number of significant digits
## with the `digits` argument:
params <- rxDerived(CL = 29.4, V = 1:3, digits = 2)
```