A wrapper around [impmapControl()] that reuses the shared FOCEI family plumbing for the nonparametric adaptive grid engine. The nonparametric support-point knobs are added in a later milestone.
Arguments
- points
Initial Sobol grid size (support points). `NULL` (default) picks it automatically from the number of support-point dimensions (etas): `max(2028, 512 * n_eta)` – a fixed grid (Pmetrics uses 2028) covers a low-dimensional model but grows sparse and can collapse in high dimensions, so the auto size floors at 2028 and scales up per added eta. Supply an integer to override.
- cycles
Maximum adaptive-grid cycles.
- gammaOptimize
Use a global assay-error multiplier (gamma) as a per-cycle warm start for the overall residual magnitude, folded into the variance-scale coefficients (`add`/`prop`/`lnorm`). The per-endpoint values, the add/prop ratio, and the transform/autocorrelation parameters come from
residOptimize. Only valid for normal endpoints; censoring and transform-both-sides are supported.- residOptimize
How to estimate the residual-error thetas (every endpoint's `add`/`prop`/`lnorm`, each transform `lambda`, each `ar`) with the support points and weights held fixed, using bounded
minqa::bobyqaon the EXTENDED LEAST SQUARES objective `sum_obs((f-dv)^2/r + log(r))` at the posterior-mean etas. The `log(r)` term keeps the residual from drifting to zero on a flexible support (which the marginal likelihood would reward), giving the saem/focei residual; each variance scale is warm-started from the per-endpoint moment (additive SD from `sqrt(mean(err^2))`, proportional from `sqrt(mean((err/f)^2))`, on the transform- both-sides scale)."alternate"(default) optimizes every cycle (block- coordinate ascent);"final"optimizes once at the converged support;"none"holds them at their initial values. Fixed residual parameters are always held. After the residual thetas converge, a final adaptive-grid pass re-optimizes the support with them held constant so the support remains the nonparametric MLE (D(F) ~ 0) for the fitted residual.- muExpand
how to estimate non-mu structural fixed-effect parameters (a theta with no eta, e.g. `ke <- exp(tke)`; npag's grid otherwise covers only mu-referenced and residual/likelihood parameters). `FALSE` (default) optimizes them directly as "regressors" in the residual step – `bobyqa` moves them alongside the residual parameters, re-deriving the posterior-mean etas each candidate (so the eta grid cannot stale-absorb the structural shift) – which identifies them well (e.g. recovering a clearance from a poor start). Not available for mix() models (the ELS step is not mixture-aware; component parameters are held). `TRUE` instead uses the saem-style mu-expansion: inject a pseudo-eta (`ke <- exp(tke + eta.tke)`), grid-estimate, and recover it as a fixed effect at finalization (support-mean folded into the theta, injected random effect collapsed). The regressor default usually identifies these parameters more sharply than the grid.
- gridWidth
support-point box half-width, in initial-eta SDs, for the `gridBounds="auto"` grid (default 4). A narrower box focuses the initial Sobol grid on the plausible region – useful for high-dimensional models where a wide box wastes points on near-zero-density support (which can collapse the fit).
- gridBounds
how to set the initial support-point box: `"auto"` (default) uses `+/- gridWidth * initial eta SD`; `"ini"` uses each mu-referenced parameter's ini-block lower/upper bounds where finite (else auto); `"both"` uses the ini bounds when present and auto otherwise. For a high-dimensional model, bounded ini estimates + `"ini"` keep the grid in range.
- dfScan
Size of the Sobol scan used for the D(F) global-optimality certificate: `-1` (default) auto-sizes it to `max(2048, 2 * points)`, `0` skips the certificate (`npagDF` is `NA`), and a positive value sets an explicit scan size. The scan does not affect the fit, only the reported certificate; a smaller scan is faster.
- cores
Number of threads used for the parallel per-subject conditional- likelihood solves. `NULL` (default) uses the current `rxode2` thread count (`rxode2::getRxThreads()`); an integer sets the thread count for the fit (restored afterwards). Results are independent of the thread count.
- rhoend
Final trust-region radius (`rhoend`) of the inner bounded `bobyqa` that fits the residual-error thetas each cycle. A fixed default of `1e-4`, matching the optimizer convergence tolerance `10^(-sigdig)` at `sigdig = 4` (npag has no `sigdig`, so this is not derived from it).
- ...
Parameters passed to [impmapControl()].
Details
Note: the npag objective is the nonparametric marginal log-likelihood and uses a different constant convention than NONMEM/FOCEI, so its `-2LL` is NOT comparable to nlmixr2's FOCEI/SAEM/FOCE `-2LL`. Compare npag runs to each other or to Pmetrics NPAG.
Note on residual error with a flexible support distribution: the residual
parameters are estimated against the nonparametric objective (see
residOptimize) with the support-point distribution held fixed. Because
that distribution is flexible, it can absorb variability a parametric model
(FOCEI/SAEM) would attribute to residual error – especially the additive term
of a combined additive+proportional model at low concentrations. As a result
the additive coefficient of a combined error model may be estimated smaller
(sometimes toward zero) than the corresponding parametric fit, while the
proportional term and per-endpoint magnitudes are recovered well. This is an
expected property of nonparametric estimation, not a convergence failure; use
residOptimize = "none" to hold the residual parameters at their initial
values if a fixed error model is desired.
Examples
npagControl()
#> $maxOuterIterations
#> [1] 5000
#>
#> $maxInnerIterations
#> [1] 1000
#>
#> $n1qn1nsim
#> [1] 10001
#>
#> $iterPrintControl
#> $every
#> [1] 1
#>
#> $ncol
#> [1] 4
#>
#> $headerEvery
#> [1] 10
#>
#> $useColor
#> [1] TRUE
#>
#> $simple
#> [1] FALSE
#>
#> attr(,"class")
#> [1] "iterPrintControl" "list"
#>
#> $lbfgsLmm
#> [1] 7
#>
#> $lbfgsPgtol
#> [1] 0
#>
#> $lbfgsFactr
#> [1] 4.5036e+12
#>
#> $scaleTo
#> [1] 1
#>
#> $epsilon
#> [1] 0.001
#>
#> $derivEps
#> [1] 2.980232e-07 2.980232e-07
#>
#> $derivMethod
#> [1] 3
#>
#> $covDerivMethod
#> [1] 1
#>
#> $covMethod
#> [1] 2
#>
#> $covType
#> [1] "analytic"
#>
#> $covMethodDeferred
#> [1] NA
#>
#> $covSolveTol
#> NULL
#>
#> $covFull
#> [1] TRUE
#>
#> $fast
#> [1] FALSE
#>
#> $fdChartrand
#> [1] 1
#>
#> $centralDerivEps
#> [1] 2.980232e-07 2.980232e-07
#>
#> $eigen
#> [1] 1
#>
#> $diagXform
#> [1] "sqrt"
#>
#> $iovXform
#> [1] "sd"
#>
#> $sumProd
#> [1] FALSE
#>
#> $optExpression
#> [1] TRUE
#>
#> $literalFix
#> [1] TRUE
#>
#> $literalFixRes
#> [1] TRUE
#>
#> $outerOpt
#> [1] -1
#>
#> $ci
#> [1] 0.95
#>
#> $sigdig
#> [1] 3
#>
#> $sigdigTable
#> [1] 3
#>
#> $scaleObjective
#> [1] 0
#>
#> $boundTol
#> [1] 0.05
#>
#> $calcTables
#> [1] TRUE
#>
#> $noAbort
#> [1] 1
#>
#> $interaction
#> [1] 1
#>
#> $foce
#> [1] "nonmem"
#>
#> $foceType
#> [1] 0
#>
#> $cholSEtol
#> [1] 6.055454e-06
#>
#> $foceEbeTol
#> [1] 1e-09
#>
#> $hessEps
#> [1] 6.055454e-06
#>
#> $hessEpsLlik
#> [1] 6.055454e-06
#>
#> $optimHessType
#> [1] 1
#>
#> $optimHessCovType
#> [1] 1
#>
#> $censOption
#> [1] 0
#>
#> $cholAccept
#> [1] 0.001
#>
#> $resetEtaSize
#> [1] 1.439531
#>
#> $resetThetaSize
#> [1] Inf
#>
#> $resetThetaFinalSize
#> [1] Inf
#>
#> $diagOmegaBoundUpper
#> [1] 5
#>
#> $diagOmegaBoundLower
#> [1] 100
#>
#> $cholSEOpt
#> [1] 0
#>
#> $cholSECov
#> [1] 0
#>
#> $fo
#> [1] 0
#>
#> $covTryHarder
#> [1] 0
#>
#> $outerOptFun
#> function (par, fn, gr, lower = -Inf, upper = Inf, control = list(),
#> ...)
#> {
#> .ctl <- .controlMaxfun(control)
#> if (is.null(.ctl$npt))
#> .ctl$npt <- length(par) * 2 + 1
#> .ctl$iprint <- 0L
#> .ctl <- .ctl[names(.ctl) %in% c("npt", "rhobeg", "rhoend",
#> "iprint", "maxfun")]
#> .ret <- minqa::bobyqa(par, fn, control = .ctl, lower = lower,
#> upper = upper)
#> .ret$x <- .ret$par
#> .ret$message <- .ret$msg
#> .ret$convergence <- .ret$ierr
#> .ret$value <- .ret$fval
#> .ret
#> }
#> <bytecode: 0x5566fd4801d8>
#> <environment: namespace:nlmixr2est>
#>
#> $rhobeg
#> [1] 0.2
#>
#> $rhoend
#> [1] 1e-04
#>
#> $npt
#> NULL
#>
#> $rel.tol
#> [1] 0.001
#>
#> $x.tol
#> [1] 0.001
#>
#> $eval.max
#> [1] 4000
#>
#> $iter.max
#> [1] 2000
#>
#> $innerOpt
#> [1] 1
#>
#> $abstol
#> [1] 0.001
#>
#> $reltol
#> [1] 0.001
#>
#> $derivSwitchTol
#> [1] 0.002
#>
#> $resetHessianAndEta
#> [1] 0
#>
#> $muModel
#> [1] "lin"
#>
#> $muRefCovAlg
#> [1] TRUE
#>
#> $muModelTol
#> [1] 1e-05
#>
#> $muModelMaxCycles
#> [1] 20
#>
#> $muModelClampRetries
#> [1] 10
#>
#> $stateTrim
#> [1] Inf
#>
#> $gillK
#> [1] 10
#>
#> $gillKcov
#> [1] 10
#>
#> $gillKcovLlik
#> [1] 10
#>
#> $gillRtol
#> [1] 1.490116e-08
#>
#> $gillStep
#> [1] 4
#>
#> $gillStepCov
#> [1] 2
#>
#> $gillStepCovLlik
#> [1] 4.5
#>
#> $scaleType
#> [1] 2
#>
#> $normType
#> [1] 1
#>
#> $scaleC
#> NULL
#>
#> $scaleCmin
#> [1] 1e-05
#>
#> $scaleCband
#> [1] 0.1 10.0
#>
#> $scaleCmax
#> [1] 1e+05
#>
#> $scaleC0
#> [1] 1e+05
#>
#> $outerOptTxt
#> [1] "bobyqa"
#>
#> $outerOptDefault
#> [1] TRUE
#>
#> $rmatNorm
#> [1] 1
#>
#> $rmatNormLlik
#> [1] 1
#>
#> $smatNorm
#> [1] 1
#>
#> $smatNormLlik
#> [1] 1
#>
#> $covGillF
#> [1] 1
#>
#> $optGillF
#> [1] 1
#>
#> $gillFtol
#> [1] 0
#>
#> $gillFtolCov
#> [1] 0
#>
#> $gillFtolCovLlik
#> [1] 0
#>
#> $covSmall
#> [1] 1e-05
#>
#> $adjLik
#> [1] TRUE
#>
#> $gradTrim
#> [1] Inf
#>
#> $gradCalcCentralSmall
#> [1] 1e-04
#>
#> $gradCalcCentralLarge
#> [1] 10000
#>
#> $etaNudge
#> [1] 1.131586
#>
#> $etaNudge2
#> [1] 1.518182
#>
#> $maxOdeRecalc
#> [1] 5
#>
#> $odeRecalcFactor
#> [1] 3.162278
#>
#> $nRetries
#> [1] 3
#>
#> $seed
#> [1] 42
#>
#> $resetThetaCheckPer
#> [1] 0.1
#>
#> $etaMat
#> NULL
#>
#> $repeatGillMax
#> [1] 1
#>
#> $stickyRecalcN
#> [1] 4
#>
#> $outerMaxOdeRecalc
#> [1] 5
#>
#> $outerOdeRecalcFactor
#> [1] 3.162278
#>
#> $outerStickyRecalcN
#> [1] 4
#>
#> $indTolRelax
#> [1] TRUE
#>
#> $eventType
#> [1] 2
#>
#> $eventSens
#> [1] "jump"
#>
#> $gradProgressOfvTime
#> [1] 10
#>
#> $addProp
#> [1] "combined2"
#>
#> $badSolveObjfAdj
#> [1] 100
#>
#> $compress
#> [1] FALSE
#>
#> $rxControl
#> $scale
#> NULL
#>
#> $method
#> liblsoda
#> 2
#>
#> $atol
#> [1] 1e-06
#>
#> $rtol
#> [1] 0.001
#>
#> $maxsteps
#> [1] 500000
#>
#> $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
#>
#> $skipCov
#> NULL
#>
#> $fallbackFD
#> [1] FALSE
#>
#> $shi21maxOuter
#> [1] 0
#>
#> $shi21maxInner
#> [1] 20
#>
#> $shi21maxInnerCov
#> [1] 20
#>
#> $shi21maxFD
#> [1] 20
#>
#> $shi21hMax
#> [1] 2
#>
#> $shi21hMin
#> [1] 1e-04
#>
#> $smatPer
#> [1] 0.6
#>
#> $sdLowerFact
#> [1] 0.001
#>
#> $zeroGradFirstReset
#> [1] TRUE
#>
#> $zeroGradRunReset
#> [1] TRUE
#>
#> $zeroGradBobyqa
#> [1] TRUE
#>
#> $mceta
#> [1] -2
#>
#> $warm
#> [1] 1
#>
#> $nAGQ
#> [1] 0
#>
#> $agqHi
#> [1] Inf
#>
#> $agqLow
#> [1] -Inf
#>
#> $sensMethod
#> [1] "default"
#>
#> $boundedTransform
#> [1] TRUE
#>
#> $zeroTheta
#> [1] 0.001
#>
#> $impCov
#> [1] TRUE
#>
#> $isample
#> [1] 300
#>
#> $nIter
#> [1] 100
#>
#> $mapIter
#> [1] 1
#>
#> $gamma
#> [1] 1
#>
#> $gammaMethod
#> [1] "auto"
#>
#> $df
#> [1] 0
#>
#> $auto
#> [1] TRUE
#>
#> $iscaleMin
#> [1] 0.1
#>
#> $iscaleMax
#> [1] 10
#>
#> $iaccept
#> [1] 0.4
#>
#> $nConvWindow
#> [1] 10
#>
#> $impSeed
#> [1] 42
#>
#> $qr
#> [1] FALSE
#>
#> $qrShift
#> [1] TRUE
#>
#> $qrRefresh
#> [1] TRUE
#>
#> $sir
#> [1] FALSE
#>
#> $sirSample
#> [1] 30
#>
#> $est
#> [1] "npag"
#>
#> $points
#> [1] NA
#>
#> $cycles
#> [1] 100
#>
#> $dfScan
#> [1] -1
#>
#> $npCores
#> [1] NA
#>
#> $gammaOptimize
#> [1] TRUE
#>
#> $residOptimize
#> [1] "alternate"
#>
#> $muExpand
#> [1] FALSE
#>
#> $gridWidth
#> [1] 4
#>
#> $gridBounds
#> [1] "auto"
#>
#> attr(,"class")
#> [1] "impmapControl"