assertRxUi | R Documentation |
Assert properties of the rxUi models
assertRxUi(ui, extra = "", .var.name = .vname(ui))
assertRxUiPrediction(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))
ui |
Model to check |
extra |
Extra text to append to the error message (like "for focei") |
.var.name |
[ |
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
the rxUi model
Matthew L. Fidler
Other Assertions:
assertCompartmentExists()
,
assertCompartmentName()
,
assertCompartmentNew()
,
assertVariableExists()
,
assertVariableNew()
,
testIniDf()
,
testRxUnbounded()
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)
# assertRxUi(rnorm) # will fail
assertRxUiSingleEndpoint(one.cmt)
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