newuoaControl: Control for newuoa estimation method in nlmixr2

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

Control for newuoa estimation method in nlmixr2

Description

Control for newuoa estimation method in nlmixr2

Usage

newuoaControl(
  npt = NULL,
  rhobeg = NULL,
  rhoend = NULL,
  iprint = 0L,
  maxfun = 100000L,
  returnNewuoa = FALSE,
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  indTolRelax = TRUE,
  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,
  rxControl = NULL,
  optExpression = TRUE,
  sumProd = FALSE,
  literalFix = TRUE,
  literalFixRes = TRUE,
  addProp = c("combined2", "combined1"),
  calcTables = TRUE,
  compress = FALSE,
  covMethod = c("r", ""),
  adjObf = TRUE,
  ci = 0.95,
  sigdig = 3,
  sigdigTable = NULL,
  boundedTransform = TRUE,
  eventSens = c("jump", "fd"),
  ...
)

Arguments

npt	Number of points for bobyqa's quadratic approximation to the objective; must be in '[n+2, (n+1)(n+2)/2]'. Defaults to '2*n + 1'. (bobyqa)
rhobeg	Initial trust region radius for the bobyqa outer optimizer (with 'rhoend', must satisfy '0 < rhoend < rhobeg'). Default '0.2' (20 'abs(upper-lower)/2'. (bobyqa)
rhoend	Final trust region radius. If not defined, '10^(-sigdig)' is used. (bobyqa)
iprint	Controls amount of printing ('0'=none, '1'=start/end only, '2'=each new rho, '3'=every function evaluation, '>3'=every 'iprint' evaluations). Default '0'.
maxfun	The maximum allowed number of function evaluations. If this is exceeded, the method will terminate.
returnNewuoa	return the newuoa output instead of the nlmixr2 fit
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.
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.
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'.
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).
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; "" 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.
boundedTransform	When 'TRUE' (default), bounded parameters are transformed for unbounded optimization methods and back-transformed for final estimates. 'FALSE' optimizes on the original scale with bounds passed to the optimizer. 'NA' transforms for optimization but skips the final back-transform.
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.
...	Ignored parameters

Value

newuoa control structure

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="newuoa")

print(fit2)

# you can also get the nlm output with

fit2$newuoa

# The nlm control has been modified slightly to include
# extra components and name the parameters

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