Add residual error to a model

Usage

addResErr(ui, reserr, endpoint)

Arguments

ui: The model as a function
reserr: The type or types of residual error (currently "addSd", "propSd", and "lnormSd" are accepted)
endpoint: the endpoint to apply the error; will default to the first error in the model

Value

The model with residual error modified

Details

For reserr, the parameter will be named with the dependent variable from the model as a prefix. For example, if the dependent variable in the model is Cc, the parameter name for propSd will become CcpropSd.

Examples

library(rxode2)
readModelDb("PK_1cmt") |> addResErr("addSd")
#>  
#>  
#>  
#>  
#> ! remove population parameter `propSd`
#> ℹ add residual parameter `CcAddSd` and set estimate to 1
#> ℹ change initial estimate of `CcAddSd` to `1`
#>  ── rxode2-based solved PK 1-compartment model ────────────────────────────────── 
#>  ── Initalization: ──  
#> Fixed Effects ($theta): 
#>     lka     lcl     lvc CcAddSd 
#>    0.45    1.00    3.45    1.00 
#> 
#> States ($state or $stateDf): 
#>   Compartment Number Compartment Name Rate   Off Internal #
#> 1                  1            depot TRUE FALSE          1
#> 2                  2          central TRUE FALSE          2
#>  ── Model (Normalized Syntax): ── 
#> function() {
#>     description <- "One compartment PK model with linear clearance"
#>     ini({
#>         lka <- 0.45
#>         label("Absorption rate (Ka)")
#>         lcl <- 1
#>         label("Clearance (CL)")
#>         lvc <- 3.45
#>         label("Central volume of distribution (V)")
#>         CcAddSd <- c(0, 1)
#>     })
#>     model({
#>         ka <- exp(lka)
#>         cl <- exp(lcl)
#>         vc <- exp(lvc)
#>         Cc <- linCmt()
#>         Cc ~ add(CcAddSd)
#>     })
#> }
readModelDb("PK_1cmt") |> addResErr("lnormSd")
#>  
#>  
#>  
#>  
#> ! remove population parameter `propSd`
#> ℹ add residual parameter `CcLnormSd` and set estimate to 1
#> ℹ change initial estimate of `CcLnormSd` to `0.5`
#>  ── rxode2-based solved PK 1-compartment model ────────────────────────────────── 
#>  ── Initalization: ──  
#> Fixed Effects ($theta): 
#>       lka       lcl       lvc CcLnormSd 
#>      0.45      1.00      3.45      0.50 
#> 
#> States ($state or $stateDf): 
#>   Compartment Number Compartment Name Rate   Off Internal #
#> 1                  1            depot TRUE FALSE          1
#> 2                  2          central TRUE FALSE          2
#>  ── Model (Normalized Syntax): ── 
#> function() {
#>     description <- "One compartment PK model with linear clearance"
#>     ini({
#>         lka <- 0.45
#>         label("Absorption rate (Ka)")
#>         lcl <- 1
#>         label("Clearance (CL)")
#>         lvc <- 3.45
#>         label("Central volume of distribution (V)")
#>         CcLnormSd <- c(0, 0.5)
#>     })
#>     model({
#>         ka <- exp(lka)
#>         cl <- exp(lcl)
#>         vc <- exp(lvc)
#>         Cc <- linCmt()
#>         Cc ~ lnorm(CcLnormSd)
#>     })
#> }
readModelDb("PK_1cmt") |> addResErr(c("addSd", "propSd"))
#>  
#>  
#>  
#>  
#> ! remove population parameter `propSd`
#> ℹ add residual parameter `CcAddSd` and set estimate to 1
#> ℹ add residual parameter `CcPropSd` and set estimate to 1
#> ℹ change initial estimate of `CcAddSd` to `1`
#> ℹ change initial estimate of `CcPropSd` to `0.5`
#>  ── rxode2-based solved PK 1-compartment model ────────────────────────────────── 
#>  ── Initalization: ──  
#> Fixed Effects ($theta): 
#>      lka      lcl      lvc  CcAddSd CcPropSd 
#>     0.45     1.00     3.45     1.00     0.50 
#> 
#> States ($state or $stateDf): 
#>   Compartment Number Compartment Name Rate   Off Internal #
#> 1                  1            depot TRUE FALSE          1
#> 2                  2          central TRUE FALSE          2
#>  ── Model (Normalized Syntax): ── 
#> function() {
#>     description <- "One compartment PK model with linear clearance"
#>     ini({
#>         lka <- 0.45
#>         label("Absorption rate (Ka)")
#>         lcl <- 1
#>         label("Clearance (CL)")
#>         lvc <- 3.45
#>         label("Central volume of distribution (V)")
#>         CcAddSd <- c(0, 1)
#>         CcPropSd <- c(0, 0.5)
#>     })
#>     model({
#>         ka <- exp(lka)
#>         cl <- exp(lcl)
#>         vc <- exp(lvc)
#>         Cc <- linCmt()
#>         Cc ~ add(CcAddSd) + prop(CcPropSd)
#>     })
#> }