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VPC based on ui model

Usage

vpcCensTad(..., cens = TRUE, idv = "tad")

Arguments

...

Additional arguments passed to nlmixr2plot::vpcCensTad().

cens

is a boolean to show if this is a censoring plot or not. When cens=TRUE this is actually a censoring vpc plot (with vpcCens() and vpcCensTad()). When cens=FALSE this is traditional VPC plot (vpcPlot() and vpcPlotTad()).

idv

Name of independent variable. For vpcPlot() and vpcCens() the default is "time" for vpcPlotTad() and vpcCensTad() this is "tad"

Value

Simulated dataset (invisibly)

Author

Matthew L. Fidler

Examples

# \donttest{
one.cmt <- function() {
 ini({
   tka <- 0.45; label("Ka")
   tcl <- log(c(0, 2.7, 100)); label("Cl")
   tv <- 3.45; label("V")
   eta.ka ~ 0.6
   eta.cl ~ 0.3
   eta.v ~ 0.1
   add.sd <- 0.7; label("Additive residual error")
 })
 model({
   ka <- exp(tka + eta.ka)
   cl <- exp(tcl + eta.cl)
   v <- exp(tv + eta.v)
   linCmt() ~ add(add.sd)
 })
}

fit <-
  nlmixr2est::nlmixr(
    one.cmt,
    data = nlmixr2data::theo_sd,
    est = "saem",
    control = list(print = 0)
  )
#>  
#>  
#>  
#>  
#>  parameter labels from comments are typically ignored in non-interactive mode
#>  Need to run with the source intact to parse comments
#>  
#>  
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of saem model...
#>  done
#> → finding duplicate expressions in saem model...
#>  done
#>  calculate uninformed etas
#>  done
#> Calculating covariance matrix
#> → loading into symengine environment...
#> → pruning branches (`if`/`else`) of saem model...
#>  done
#> → finding duplicate expressions in saem predOnly model 0...
#> → finding duplicate expressions in saem predOnly model 1...
#> → finding duplicate expressions in saem predOnly model 2...
#>  done
#>  
#>  
#> → Calculating residuals/tables
#>  done
#> → compress origData in nlmixr2 object, save 5952
#> → compress phiM in nlmixr2 object, save 63664
#> → compress parHistData in nlmixr2 object, save 13816
#> → compress saem0 in nlmixr2 object, save 29968

vpcPlot(fit)
#>  
#>  

# }