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Assert properties of the rxUi models

Source: R/assert.R
assertRxUi.Rd

Assert properties of the rxUi models

Usage

assertRxUi(ui, extra = "", .var.name = .vname(ui))

assertRxUiPrediction(ui, extra = "", .var.name = .vname(ui))

assertRxUiIovNoCor(ui, extra = "", .var.name = .vname(ui))

assertRxUiNoMix(ui, extra = "", .var.name = .vname(ui))

assertRxUiSingleEndpoint(ui, extra = "", .var.name = .vname(ui))

assertRxUiTransformNormal(ui, extra = "", .var.name = .vname(ui))

assertRxUiNormal(ui, extra = "", .var.name = .vname(ui))

assertRxUiMuRefOnly(ui, extra = "", .var.name = .vname(ui))

assertRxUiEstimatedResiduals(ui, extra = "", .var.name = .vname(ui))

assertRxUiPopulationOnly(ui, extra = "", .var.name = .vname(ui))

assertRxUiMixedOnly(ui, extra = "", .var.name = .vname(ui))

assertRxUiRandomOnIdOnly(ui, extra = "", .var.name = .vname(ui))

Arguments

ui

Model to check

extra

Extra text to append to the error message (like "for focei")

.var.name

[character(1)]
Name of the checked object to print in assertions. Defaults to the heuristic implemented in vname.

Value

the rxUi model

Details

These functions have different types of assertions

  • assertRxUi – Make sure this is a proper rxode2 model (if not throw error)

  • assertRxUiSingleEndpoint – Make sure the rxode2 model is only a single endpoint model (if not throw error)

  • assertRxUiTransformNormal – Make sure that the model residual distribution is normal or transformably normal

  • assertRxUiNormal – Make sure that the model residual distribution is normal

  • assertRxUiEstimatedResiduals – Make sure that the residual error parameters are estimated (not modeled).

  • assertRxUiPopulationOnly – Make sure the model is the population only model (no mixed effects)

  • assertRxUiMixedOnly – Make sure the model is a mixed effect model (not a population effect, only)

  • assertRxUiPrediction – Make sure the model has predictions

  • assertRxUiMuRefOnly – Make sure that all the parameters are mu-referenced

  • assertRxUiRandomOnIdOnly – Make sure there are only random effects at the ID level

  • assertRxUiIovNoCor – Make sure that the IOV model does not have any correlations

  • assertRxUiNoMix – Make sure that the model does not have a mixture model inside it

See also

Other Assertions: assertCompartmentExists(), assertCompartmentName(), assertCompartmentNew(), assertVariableExists(), assertVariableNew(), testIniDf(), testRxUnbounded()

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
 })
 model({
   ka <- exp(tka + eta.ka)
   cl <- exp(tcl + eta.cl)
   v <- exp(tv + eta.v)
   linCmt() ~ add(add.sd)
 })
}

assertRxUi(one.cmt)
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
# assertRxUi(rnorm) # will fail

assertRxUiSingleEndpoint(one.cmt)
#>  
#>  
#> ℹ parameter labels from comments are typically ignored in non-interactive mode
#> ℹ Need to run with the source intact to parse comments
# }