A convenience wrapper around [emviControl()] with `pointEstimate = FALSE`, i.e. the variational posterior covers the unconstrained population vector (under flat priors) as well as the per-subject etas, rather than point-estimating the population parameters by an M-step. See [emviControl()] for the full parameter list.
Arguments
- ...
Parameters passed to [emviControl()].
- pointEstimate
Which of the two methods to run, normally left at its `NULL` default so it follows `est`: `est="emvi"` implies `TRUE` and `est="fbvi"` implies `FALSE`. `est` wins over a contradicting value, and says so – it has to, because re-estimating a fit with the other method pipes the completed fit's control forward. `TRUE` runs the variational-EM hybrid: the variational posterior covers the per-subject etas only, and the population parameters (thetas / omega / residual error) are point estimates maximized by the ELBO gradient; output semantics match FOCEi/SAEM. `FALSE` runs full Bayes: the variational posterior also covers the unconstrained population vector, with flat priors.
Two things about "flat" are worth being explicit about, because they define the prior rather than merely describe the implementation. (1) A BOUNDED theta is fitted on its unconstrained scale, and the log-determinant of that constraining transform IS added to the full-Bayes objective, so the flat prior is flat on the NATURAL parameter, as in Stan. It is deliberately NOT added when `pointEstimate=TRUE`: a maximum-likelihood estimate has to stay invariant to reparameterization, which is why Stan's own `optimize` defaults to `jacobian=0`. (2) The between-subject variances are carried as per-eta LOG-variances and no Jacobian is applied to them, so the prior is flat on `log(omega)` – the conventional weakly-informative choice for a scale parameter, but a choice, not an accident: it is not flat on `omega`.
These point estimates maximize the ELBO, NOT the likelihood, and the difference is not merely cosmetic. Since `ELBO = log p(y|theta) - KL(q || p(eta|y,theta))`, any dependence of that KL on `theta` displaces the maximizer from the MLE – "variational maximum likelihood". For VARIANCE components the displacement has a known direction: a variational family that understates posterior spread makes the omega M-step, `Omega = mean_i(mu_i mu_i' + Sigma_i)`, inherit that understatement, so between-subject variability is biased DOWNWARD. The bias is worst for `viFamily="meanField"`, which cannot represent within-subject posterior correlation at all; `"fullRank"` can, which is why it is the default. Structural (typical-value) parameters are far less affected. If the between-subject variances are themselves the quantity of interest, prefer `"fullRank"` and cross-check against `est="focei"` or `est="saem"`.
Examples
fbviControl()
#> $seed
#> [1] 42
#>
#> $iters
#> [1] 300
#>
#> $nMc
#> [1] 1
#>
#> $viFamily
#> [1] "fullRank"
#>
#> $pointEstimate
#> [1] FALSE
#>
#> $optim
#> [1] "advi"
#>
#> $adaptEta
#> [1] TRUE
#>
#> $perNoCor
#> [1] 0.75
#>
#> $etaCandidates
#> [1] 0.010 0.025 0.050 0.100 0.250
#>
#> $tau
#> [1] 1
#>
#> $alpha
#> [1] 0.1
#>
#> $tol
#> [1] 0.001
#>
#> $evalElbo
#> [1] 100
#>
#> $klWarmup
#> [1] 0
#>
#> $temperInit
#> [1] 10
#>
#> $likelihood
#> [1] "focei"
#>
#> $returnVi
#> [1] FALSE
#>
#> $resume
#> NULL
#>
#> $covMethod
#> [1] "vi"
#>
#> $optExpression
#> [1] TRUE
#>
#> $sumProd
#> [1] FALSE
#>
#> $literalFix
#> [1] TRUE
#>
#> $literalFixRes
#> [1] TRUE
#>
#> $addProp
#> [1] "combined2"
#>
#> $calcTables
#> [1] TRUE
#>
#> $compress
#> [1] FALSE
#>
#> $adjObf
#> [1] TRUE
#>
#> $ci
#> [1] 0.95
#>
#> $sigdig
#> [1] 3
#>
#> $sigdigTable
#> [1] 3
#>
#> $stickyRecalcN
#> [1] 4
#>
#> $maxOdeRecalc
#> [1] 5
#>
#> $odeRecalcFactor
#> [1] 3.162278
#>
#> $indTolRelax
#> [1] TRUE
#>
#> $eventSens
#> [1] "jump"
#>
#> $iterPrintControl
#> $every
#> [1] 1
#>
#> $ncol
#> [1] 4
#>
#> $headerEvery
#> [1] 10
#>
#> $useColor
#> [1] TRUE
#>
#> $simple
#> [1] FALSE
#>
#> attr(,"class")
#> [1] "iterPrintControl" "list"
#>
#> $rxControl
#> $scale
#> NULL
#>
#> $method
#> liblsoda
#> 2
#>
#> $atol
#> [1] 1e-06
#>
#> $rtol
#> [1] 0.001
#>
#> $maxsteps
#> [1] 70000
#>
#> $hmin
#> [1] 0
#>
#> $hmax
#> [1] NA
#>
#> $hini
#> [1] 0
#>
#> $maxordn
#> [1] 12
#>
#> $maxords
#> [1] 5
#>
#> $covsInterpolation
#> locf
#> 1
#>
#> $addCov
#> [1] TRUE
#>
#> $returnType
#> rxSolve
#> 0
#>
#> $sigma
#> NULL
#>
#> $sigmaDf
#> NULL
#>
#> $nCoresRV
#> [1] 1
#>
#> $sigmaIsChol
#> [1] FALSE
#>
#> $sigmaSeparation
#> [1] "auto"
#>
#> $sigmaXform
#> identity
#> 4
#>
#> $nDisplayProgress
#> [1] 10000
#>
#> $amountUnits
#> [1] NA
#>
#> $timeUnits
#> [1] "hours"
#>
#> $addDosing
#> [1] FALSE
#>
#> $stateTrim
#> [1] Inf
#>
#> $updateObject
#> [1] FALSE
#>
#> $omega
#> NULL
#>
#> $omegaDf
#> NULL
#>
#> $omegaIsChol
#> [1] FALSE
#>
#> $omegaSeparation
#> [1] "auto"
#>
#> $omegaXform
#> variance
#> 6
#>
#> $nSub
#> [1] 1
#>
#> $thetaMat
#> NULL
#>
#> $thetaDf
#> NULL
#>
#> $thetaIsChol
#> [1] FALSE
#>
#> $nStud
#> [1] 1
#>
#> $dfSub
#> [1] 0
#>
#> $dfObs
#> [1] 0
#>
#> $seed
#> NULL
#>
#> $nsim
#> NULL
#>
#> $minSS
#> [1] 10
#>
#> $maxSS
#> [1] 10000
#>
#> $strictSS
#> [1] 1
#>
#> $infSSstep
#> [1] 12
#>
#> $istateReset
#> [1] TRUE
#>
#> $subsetNonmem
#> [1] TRUE
#>
#> $hmaxSd
#> [1] 0
#>
#> $maxAtolRtolFactor
#> [1] 0.1
#>
#> $from
#> NULL
#>
#> $to
#> NULL
#>
#> $by
#> NULL
#>
#> $length.out
#> NULL
#>
#> $iCov
#> NULL
#>
#> $keep
#> NULL
#>
#> $keepF
#> character(0)
#>
#> $drop
#> NULL
#>
#> $warnDrop
#> [1] TRUE
#>
#> $omegaLower
#> [1] -Inf
#>
#> $omegaUpper
#> [1] Inf
#>
#> $sigmaLower
#> [1] -Inf
#>
#> $sigmaUpper
#> [1] Inf
#>
#> $thetaLower
#> [1] -Inf
#>
#> $thetaUpper
#> [1] Inf
#>
#> $indLinPhiM
#> [1] 0
#>
#> $indLinPhiTol
#> [1] 1e-07
#>
#> $indLinMatExpType
#> expokit
#> 2
#>
#> $indLinMatExpOrder
#> [1] 6
#>
#> $idFactor
#> [1] TRUE
#>
#> $mxhnil
#> [1] 0
#>
#> $hmxi
#> [1] 0
#>
#> $warnIdSort
#> [1] TRUE
#>
#> $ssAtol
#> [1] 1e-05
#>
#> $ssRtol
#> [1] 0.01
#>
#> $safeZero
#> [1] 1
#>
#> $sumType
#> pairwise
#> 1
#>
#> $prodType
#> long double
#> 1
#>
#> $resample
#> NULL
#>
#> $resampleID
#> [1] TRUE
#>
#> $maxwhile
#> [1] 100000
#>
#> $cores
#> [1] 0
#>
#> $atolSens
#> [1] 1e-06
#>
#> $rtolSens
#> [1] 0.001
#>
#> $ssAtolSens
#> [1] 1e-05
#>
#> $ssRtolSens
#> [1] 0.01
#>
#> $simVariability
#> [1] NA
#>
#> $nLlikAlloc
#> NULL
#>
#> $useStdPow
#> [1] 0
#>
#> $naTimeHandle
#> ignore
#> 1
#>
#> $addlKeepsCov
#> [1] FALSE
#>
#> $addlDropSs
#> [1] TRUE
#>
#> $ssAtDoseTime
#> [1] TRUE
#>
#> $ss2cancelAllPending
#> [1] FALSE
#>
#> $naInterpolation
#> locf
#> 1
#>
#> $keepInterpolation
#> na
#> 2
#>
#> $safeLog
#> [1] 1
#>
#> $safePow
#> [1] 1
#>
#> $ssSolved
#> [1] TRUE
#>
#> $linCmtSensType
#> auto
#> 100
#>
#> $linCmtSensH
#> [1] 1e-04
#>
#> $linCmtGillFtol
#> [1] 0
#>
#> $linCmtGillK
#> [1] 20
#>
#> $linCmtGillStep
#> [1] 4
#>
#> $linCmtGillRtol
#> [1] 1.490116e-08
#>
#> $linCmtShiErr
#> [1] 1.490116e-08
#>
#> $linCmtShiMax
#> [1] 20
#>
#> $linCmtScale
#> [1] 0 0 0 0 0 0 0
#>
#> $linCmtHcmt
#> [1] 1
#>
#> $linCmtHmeanI
#> geometric
#> 2
#>
#> $linCmtHmeanO
#> geometric
#> 2
#>
#> $linCmtSuspect
#> [1] 1e-06
#>
#> $linCmtForwardMax
#> [1] 2
#>
#> $indOwnAlloc
#> [1] -1
#>
#> $maxExtra
#> [1] 1000
#>
#> $tolFactor
#> NULL
#>
#> $serializeFile
#> NULL
#>
#> $dense
#> [1] FALSE
#>
#> $cvodeLinSolver
#> dense
#> 1
#>
#> $stiff2
#> [1] 0
#>
#> $autoSwitchMaxStiff
#> [1] 10
#>
#> $autoSwitchMaxNonstiff
#> [1] 3
#>
#> $autoSwitchStiffFirst
#> [1] 0
#>
#> $autoSwitchNonstifftol
#> [1] 0.9
#>
#> $autoSwitchStifftol
#> [1] 0.9
#>
#> $autoSwitchDtfac
#> [1] 2
#>
#> $autoSwitchSwitchMax
#> [1] 5
#>
#> $useLinCmt
#> [1] TRUE
#>
#> $file
#> NULL
#>
#> $chunkSize
#> NULL
#>
#> $parallel
#> [1] 0
#>
#> $.zeros
#> NULL
#>
#> attr(,"class")
#> [1] "rxControl"
#>
#> $genRxControl
#> [1] TRUE
#>
#> attr(,"class")
#> [1] "emviControl"