nlminbControl: nlmixr2 nlminb defaults

In nlmixr2est: Nonlinear Mixed Effects Models in Population PK/PD, Estimation Routines

nlmixr2 nlminb defaults

Description

nlmixr2 nlminb defaults

Usage

nlminbControl(
  eval.max = 200,
  iter.max = 150,
  trace = 0,
  abs.tol = 0,
  rel.tol = NULL,
  x.tol = NULL,
  xf.tol = 2.2e-14,
  step.min = 1,
  step.max = 1,
  sing.tol = rel.tol,
  scale = 1,
  scale.init = NULL,
  diff.g = NULL,
  rxControl = NULL,
  optExpression = TRUE,
  sumProd = FALSE,
  literalFix = TRUE,
  literalFixRes = TRUE,
  returnNlminb = FALSE,
  solveType = c("hessian", "grad", "fun"),
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  indTolRelax = TRUE,
  eventType = c("central", "forward"),
  shiErr = (.Machine$double.eps)^(1/3),
  shi21maxFD = 20L,
  optimHessType = c("central", "forward"),
  hessErr = (.Machine$double.eps)^(1/3),
  shi21maxHess = 20L,
  useColor = NULL,
  printNcol = NULL,
  print = 1L,
  normType = c("rescale2", "mean", "rescale", "std", "len", "constant"),
  scaleType = c("nlmixr2", "norm", "mult", "multAdd"),
  scaleCmax = 1e+05,
  scaleCmin = 1e-05,
  scaleC = NULL,
  scaleTo = 1,
  gradTo = 1,
  addProp = c("combined2", "combined1"),
  eventSens = c("jump", "fd"),
  sensMethod = c("default", "forward"),
  calcTables = TRUE,
  compress = TRUE,
  covMethod = c("r", "nlminb", ""),
  adjObf = TRUE,
  ci = 0.95,
  sigdig = 3,
  sigdigTable = NULL,
  ...
)

Arguments

eval.max	Maximum number of evaluations of the objective function allowed. Defaults to 200.
iter.max	Maximum number of iterations allowed. Defaults to 150.
trace	The value of the objective function and the parameters is printed every trace'th iteration. When 0 no trace information is to be printed
abs.tol	Absolute tolerance. Defaults to 0 so the absolute convergence test is not used. If the objective function is known to be non-negative, the previous default of '1e-20' would be more appropriate
rel.tol	Relative tolerance. When 'NULL' (default) it is derived from 'sigdig' the way 'foceiControl()' does ('10^(-sigdig)').
x.tol	X tolerance. When 'NULL' (default) it is derived from 'sigdig' ('10^(-sigdig)').
xf.tol	false convergence tolerance. Defaults to '2.2e-14'.
step.min	Minimum step size. Default to '1.'.
step.max	Maximum step size. Default to '1.'.
sing.tol	singular convergence tolerance; defaults to 'rel.tol;.
scale	See PORT documentation (or leave alone).
scale.init	... probably need to check PORT documentation
diff.g	an estimated bound on the relative error in the objective function value
rxControl	'rxode2' ODE solving options during fitting, created with 'rxControl()'
optExpression	Optimize the rxode2 expression to speed up calculation. By default this is turned on.
sumProd	Is a boolean indicating if the model should change multiplication to high precision multiplication and sums to high precision sums using the PreciseSums package. By default this is FALSE.
literalFix	boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.
literalFixRes	boolean, substitute fixed population values as literals and re-adjust ui and parameter estimates after optimization; Default is 'TRUE'.
returnNlminb	logical; when TRUE this will return the nlminb result instead of the nlmixr2 fit object
solveType	controls whether ‘nlm' uses nlmixr2’s analytical gradients (event-related parameters like lag time/duration/rate/F use Shi2021 finite differences instead): '"hessian"' builds a Hessian from the analytical gradient via finite differences, '"gradient"' supplies the gradient and lets 'nlm' compute the finite-difference Hessian, and '"fun"' lets 'nlm' compute both by finite differences.
stickyRecalcN	The number of bad ODE solves before reducing the atol/rtol for the rest of the problem.
maxOdeRecalc	Maximum number of times to reduce the ODE tolerances and try to resolve the system if there was a bad ODE solve.
odeRecalcFactor	The ODE recalculation factor when ODE solving goes bad, this is the factor the rtol/atol is reduced
indTolRelax	When 'TRUE' (default), only subjects whose ODE solve produced NaN/Inf have their tolerances relaxed, and the relaxed tolerance persists across optimizer calls (sticky). When 'FALSE', all subjects have their tolerances relaxed on each retry and tolerances are reset afterward.
eventType	Event gradient type for dosing events; Can be "central" or "forward"
shiErr	This represents the epsilon when optimizing the ideal step size for numeric differentiation using the Shi2021 method
shi21maxFD	The maximum number of steps for the optimization of the forward difference step size when using dosing events (lag time, modeled duration/rate and bioavailability)
optimHessType	Hessian type for numeric-difference individual Hessians in generalized log-likelihood estimation: "central" (matches R's 'optimHess()', default) or "forward" (faster).
hessErr	This represents the epsilon when optimizing the Hessian step size using the Shi2021 method.
shi21maxHess	Maximum number of times to optimize the best step size for the hessian calculation
useColor	Logical (or 'NULL') emit ANSI bold/color escapes in the iteration print. 'NULL' (default) defers to [crayon::has_color()].
printNcol	Integer (or 'NULL') parameter columns per row before wrapping. 'NULL' (default) uses 'floor((getOption("width") - 23) / 12)'.
print	Either a scalar print-frequency ('0' = suppress, '1' (default) = every evaluation, 'N' = every Nth), OR a pre-built [iterPrintControl()] object. Equivalent to 'iterPrintControl(every = print, ncol = printNcol, useColor = useColor)'.
normType	Parameter normalization/scaling used to get scaled initial values for scaleType, of the form Vscaled = (Vunscaled-C1)/C2 (see Feature Scaling; rescale2 follows the OptdesX manual): "rescale2" scales all parameters to (-1, 1); "rescale" (min-max) scales to (0, 1); "mean" centers on the mean with range (0, 1); "std" standardizes by mean/sd; "len" scales to unit (Euclidean) length; "constant" performs no normalization (C1=0, C2=1).
scaleType	The scaling scheme for nlmixr2: "nlmixr2" (default) scales as (current-init)*scaleC[i] + scaleTo, with scaleTo from normType and scales from scaleC; "norm" uses the simple scaling from normType; "mult" scales multiplicatively as current/init*scaleTo; "multAdd" scales linearly ((current-init)+scaleTo) for parameters in an exponential block (e.g. exp(theta)) and multiplicatively otherwise.
scaleCmax	Maximum value of the scaleC to prevent overflow.
scaleCmin	Minimum value of the scaleC to prevent underflow.
scaleC	Scaling constant used with scaleType="nlmixr2"; when not specified, chosen by parameter type to keep gradient sizes similar on a log scale: '1' for exp()-transformed/power/boxCox/ yeoJohnson parameters, '0.5*abs(est)' for additive/proportional/ lognormal error parameters, 'abs(1/digamma(est+1))' for factorials, and 'log(abs(est))*abs(est)' for log-scale parameters. May be set explicitly per parameter if these defaults don't apply well.
scaleTo	Scale the initial parameter estimate to this value. By default this is 1. When zero or below, no scaling is performed.
gradTo	this is the factor that the gradient is scaled to before optimizing. This only works with scaleType="nlmixr2".
addProp	Type of additive-plus-proportional error: '"combined1"', where standard deviations add: y = f + (a + b\times f^c) \times \varepsilon ; or '"combined2"', where variances add: y = f + \sqrt{a^2 + b^2\times f^{2\times c}} \times \varepsilon . Here y = observed, f = predicted, a = additive sd, b = proportional/power sd, c = power exponent (1 in the proportional case).
eventSens	Controls how dosing/event-parameter ('alag', 'F', 'rate', 'dur') sensitivities are computed for THETA/ETA gradients: ‘"jump"' (default) uses rxode2’s analytic event sensitivities; '"fd"' uses the legacy finite-difference behavior.
sensMethod	Method used to compute the ODE parameter sensitivities. '"forward"' uses the classic variational (forward) sensitivity ODEs; '"default"' is the same thing.
calcTables	This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE
compress	Should the object have compressed items
covMethod	Method for calculating the covariance. "r" (the default) uses nlmixr2's nlmixr2Hess() Hessian; "nlminb" uses the optimizer's own Hessian; "" skips the covariance step.
adjObf	is a boolean to indicate if the objective function should be adjusted to be closer to NONMEM's default objective function. By default this is TRUE
ci	Confidence level for some tables. By default this is 0.95 or 95% confidence.
sigdig	Optimization significant digits. One value drives, with a single consistent formula, the inner/outer optimizer convergence tolerance (10^-sigdig), the boundary check tolerance (5*10^(-sigdig+1)), and the ODE solver tolerances: the rtol exponent IS sigdig and atol sits three orders below, so rtol = 10^-sigdig, atol = 10^(-sigdig-3) for every solver (stiff, non-stiff or auto-switching). The sensitivity (atolSens/rtolSens) tolerances match the main solve (the outer gradient and covariance are built from them); the steady-state (ssAtol/ssRtol) tolerances run one order looser. Keying the optimizer to the same 10^-sigdig means it converges to exactly the precision the solve supports. At the default sigdig = 3 this is atol = 1e-6, rtol = 1e-3.
sigdigTable	Significant digits in the final output table. If not specified, then it matches the significant digits in the 'sigdig' optimization algorithm. If 'sigdig' is NULL, use 3.
...	Further arguments to be supplied to objective.

Author(s)

Matthew L. Fidler

Examples

# A logit regression example with emax model

dsn <- data.frame(i=1:1000)
dsn$time <- exp(rnorm(1000))
dsn$DV=rbinom(1000,1,exp(-1+dsn$time)/(1+exp(-1+dsn$time)))

mod <- function() {
 ini({
   E0 <- 0.5
   Em <- 0.5
   E50 <- 2
   g <- fix(2)
 })
 model({
   v <- E0+Em*time^g/(E50^g+time^g)
   ll(bin) ~ DV * v - log(1 + exp(v))
 })
}

fit2 <- nlmixr(mod, dsn, est="nlminb")

print(fit2)

# you can also get the nlm output with fit2$nlminb

fit2$nlminb

nlmixr2est documentation built on Aug. 5, 2026, 1:11 a.m.