nlsControl: nlmixr2 defaults controls for nls

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

nlmixr2 defaults controls for nls

Description

nlmixr2 defaults controls for nls

Usage

nlsControl(
  maxiter = 10000,
  tol = NULL,
  minFactor = 1/1024,
  printEval = FALSE,
  warnOnly = FALSE,
  scaleOffset = 0,
  nDcentral = FALSE,
  algorithm = c("LM", "default", "plinear", "port"),
  ftol = NULL,
  ptol = NULL,
  gtol = 0,
  diag = list(),
  epsfcn = 0,
  factor = 100,
  maxfev = integer(),
  nprint = 0,
  solveType = c("grad", "fun"),
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  indTolRelax = TRUE,
  eventType = c("central", "forward"),
  shiErr = (.Machine$double.eps)^(1/3),
  shi21maxFD = 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,
  trace = FALSE,
  rxControl = NULL,
  optExpression = TRUE,
  sumProd = FALSE,
  literalFix = TRUE,
  returnNls = FALSE,
  addProp = c("combined2", "combined1"),
  eventSens = c("jump", "fd"),
  linCmtSensCarry = c("auto", "none"),
  calcTables = TRUE,
  compress = TRUE,
  adjObf = TRUE,
  ci = 0.95,
  sigdig = 4,
  sigdigTable = NULL,
  boundedTransform = TRUE,
  ...
)

Arguments

maxiter	A positive integer specifying the maximum number of iterations allowed.
tol	A positive numeric value specifying the tolerance level for the relative offset convergence criterion.
minFactor	A positive numeric value specifying the minimum step-size factor allowed on any step in the iteration. The increment is calculated with a Gauss-Newton algorithm and successively halved until the residual sum of squares has been decreased or until the step-size factor has been reduced below this limit.
printEval	a logical specifying whether the number of evaluations (steps in the gradient direction taken each iteration) is printed.
warnOnly	a logical specifying whether nls() should return instead of signalling an error in the case of termination before convergence. Termination before convergence happens upon completion of maxiter iterations, in the case of a singular gradient, and in the case that the step-size factor is reduced below minFactor.
scaleOffset	a constant to be added to the denominator of the relative offset convergence criterion calculation to avoid a zero divide in the case where the fit of a model to data is very close. The default value of 0 keeps the legacy behaviour of nls(). A value such as 1 seems to work for problems of reasonable scale with very small residuals.
nDcentral	only when numerical derivatives are used: logical indicating if central differences should be employed, i.e., numericDeriv(*, central=TRUE) be used.
algorithm	character string specifying the algorithm to use. The default algorithm is a Gauss-Newton algorithm. Other possible values are "plinear" for the Golub-Pereyra algorithm for partially linear least-squares models and "port" for the ‘nl2sol’ algorithm from the Port library – see the references. Can be abbreviated.
ftol	non-negative numeric. Termination occurs when both the actual and predicted relative reductions in the sum of squares are at most ftol. Therefore, ftol measures the relative error desired in the sum of squares.
ptol	non-negative numeric. Termination occurs when the relative error between two consecutive iterates is at most ptol. Therefore, ptol measures the relative error desired in the approximate solution.
gtol	non-negative numeric. Termination occurs when the cosine of the angle between result of fn evaluation fvec and any column of the Jacobian is at most gtol in absolute value. Therefore, gtol measures the orthogonality desired between the function vector and the columns of the Jacobian.
diag	a list or numeric vector containing positive entries that serve as multiplicative scale factors for the parameters. Length of diag should be equal to that of par. If not, user-provided diag is ignored and diag is internally set.
epsfcn	(used if jac is not provided) is a numeric used in determining a suitable step for the forward-difference approximation. This approximation assumes that the relative errors in the functions are of the order of epsfcn. If epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision.
factor	positive numeric, used in determining the initial step bound. This bound is set to the product of factor and the |\code{diag}*\code{par}| if nonzero, or else to factor itself. In most cases factor should lie in the interval (0.1,100). 100 is a generally recommended value.
maxfev	integer; termination occurs when the number of calls to fn has reached maxfev. Note that nls.lm sets the value of maxfev to 100*(length(par) + 1) if maxfev = integer(), where par is the list or vector of parameters to be optimized.
nprint	is an integer; set nprint to be positive to enable printing of iterates
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)
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".
trace	logical value indicating if a trace of the iteration progress should be printed. Default is FALSE. If TRUE the residual (weighted) sum-of-squares, the convergence criterion and the parameter values are printed at the conclusion of each iteration. Note that format() is used, so these mostly depend on getOption("digits"). When the "plinear" algorithm is used, the conditional estimates of the linear parameters are printed after the nonlinear parameters. When the "port" algorithm is used the objective function value printed is half the residual (weighted) sum-of-squares.
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'.
returnNls	logical; when TRUE, will return the nls object instead of the nlmixr object
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. Also gates the analytic moving-boundary correction for a modeled 'alag()'/'f()' on a 'linCmt()' compartment; set '"fd"' if that model infuses a dose into the lagged/scaled compartment (rxode2/rxode2#1236), or if the regimen also doses an *unlagged/unscaled* compartment alongside the lagged/scaled one – a common design for estimating 'f()' from paired IV+oral data (rxode2/rxode2#1237).
linCmtSensCarry	'"auto"' (default) substitutes the exact sensitivity-carry gradient for a 'linCmt()' parameter driven by both an eta and a time-varying covariate (needs an rxode2 with the carry sentinels; silently keeps the standard gradient otherwise); '"none"' always keeps the standard gradient.
calcTables	This boolean is to determine if the foceiFit will calculate tables. By default this is TRUE
compress	Should the object have compressed items
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 ('NULL'), it defaults to 'sigdig'.
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.
...	Additional optional arguments. None are used at present.

Value

nls control object

Pooled (population-only) estimation

'est="nls"' is a *pooled* estimation method. It estimates population parameters only; there is no eta-conditional inner problem, and sensitivities are taken with respect to the population parameters alone.

A model carrying any random effect is therefore refused before fitting rather than being fit to something other than what it says – it stops with 'can only have population estimates for the estimation routine 'nls', try 'focei''.

A successful 'nls' fit consequently has no '$omega', no empirical Bayes estimates and no shrinkage, and its objective function table carries a single 'Pop' row. Use a mixed effects routine – 'est="focei"', 'est="saem"' and so on – for any model with between-subject variability.

Note that this help page inherits its parameter list from 'foceiControl()', 'saemControl()' and 'nlmControl()', because 'nls' shares many options with them. Inherited options that only have meaning for random effects do not apply to 'nls'.

Author(s)

Matthew L. Fidler

Examples

one.cmt <- function() {
  ini({
    tka <- 0.45
    tcl <- log(c(0, 2.7, 100))
    tv <- 3.45
    add.sd <- 0.7
  })
  model({
    ka <- exp(tka)
    cl <- exp(tcl)
    v <- exp(tv)
    linCmt() ~ add(add.sd)
  })
}

# Note that `one.cmt` declares no random effects: `nls` is a pooled
# method and refuses a model that has any.

# Uses nlsLM from minpack.lm if available

fit1 <- nlmixr(one.cmt, nlmixr2data::theo_sd, est = "nls", nlsControl(algorithm = "LM"))

# Uses port and respect parameter boundaries
fit2 <- nlmixr(one.cmt, nlmixr2data::theo_sd, est = "nls", nlsControl(algorithm = "port"))

# You can access the underlying nls object with `$nls`
fit2$nls

nlmixr2est documentation built on Sept. 20, 2026, 9:08 a.m.