lme4: Nelder_Mead – R documentation

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Nelder_Mead

Nelder-Mead Optimization of Parameters, Possibly (Box) Constrained

Description

Nelder-Mead optimization of parameters, allowing optimization subject to box constraints (contrary to the default, method = "Nelder-Mead", in R's optim()), and using reverse communications.

Usage

Nelder_Mead(fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, n),
            control = list())

Arguments

`fn`	a `function` of a single numeric vector argument returning a numeric scalar.
`par`	numeric vector of starting values for the parameters.
`lower`	numeric vector of lower bounds (elements may be `-Inf`).
`upper`	numeric vector of upper bounds (elements may be `Inf`).
`control`	a named list of control settings. Possible settings are iprint numeric scalar - frequency of printing evaluation information. Defaults to 0 indicating no printing. maxfun numeric scalar - maximum number of function evaluations allowed (default:10000). FtolAbs numeric scalar - absolute tolerance on change in function values (default: 1e-5) FtolRel numeric scalar - relative tolerance on change in function values (default:1e-15) XtolRel numeric scalar - relative tolerance on change in parameter values (default: 1e-7) MinfMax numeric scalar - maximum value of the minimum (default: .Machine$double.xmin) xst numeric vector of initial step sizes to establish the simplex - all elements must be non-zero (default: rep(0.02,length(par))) xt numeric vector of tolerances on the parameters (default: xst*5e-4) verbose numeric value: 0=no printing, 1=print every 20 evaluations, 2=print every 10 evalutions, 3=print every evaluation. Sets ‘iprint’, if specified, but does not override it. warnOnly a logical indicating if non-convergence (codes -1,-2,-3) should not `stop(.)`, but rather only call `warning` and return a result which might inspected. Defaults to `FALSE`, i.e., stop on non-convergence.

Value

a list with components

`fval`	numeric scalar - the minimum function value achieved
`par`	numeric vector - the value of `x` providing the minimum
`convergence`	integer valued scalar, if not `0`, an error code: -4 `nm_evals`: maximum evaluations reached -3 `nm_forced`: ? -2 `nm_nofeasible`: cannot generate a feasible simplex -1 `nm_x0notfeasible`: initial x is not feasible (?) 0 successful convergence
`message`	a string specifying the kind of convergence.
`control`	the `list` of control settings after substituting for defaults.
`feval`	the number of function evaluations.

Examples

fr <- function(x) {   ## Rosenbrock Banana function
    x1 <- x[1]
    x2 <- x[2]
    100 * (x2 - x1 * x1)^2 + (1 - x1)^2
}
p0 <- c(-1.2, 1)

oo  <- optim(p0, fr) ## also uses Nelder-Mead by default
o.  <- Nelder_Mead(fr, p0)
o.1 <- Nelder_Mead(fr, p0, control=list(verbose=1))# -> some iteration output
stopifnot(identical(o.[1:4], o.1[1:4]),
          all.equal(o.$par, oo$par, tolerance=1e-3))# diff: 0.0003865


o.2 <- Nelder_Mead(fr, p0, control=list(verbose=3, XtolRel=1e-15, FtolAbs= 1e-14))
all.equal(o.2[-5],o.1[-5], tolerance=1e-15)# TRUE, unexpectedly

lme4

Linear Mixed-Effects Models using 'Eigen' and S4

v1.1-26

GPL (>= 2)

Authors

Douglas Bates [aut] (<https://orcid.org/0000-0001-8316-9503>), Martin Maechler [aut] (<https://orcid.org/0000-0002-8685-9910>), Ben Bolker [aut, cre] (<https://orcid.org/0000-0002-2127-0443>), Steven Walker [aut] (<https://orcid.org/0000-0002-4394-9078>), Rune Haubo Bojesen Christensen [ctb] (<https://orcid.org/0000-0002-4494-3399>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Bin Dai [ctb], Fabian Scheipl [ctb] (<https://orcid.org/0000-0001-8172-3603>), Gabor Grothendieck [ctb], Peter Green [ctb] (<https://orcid.org/0000-0002-0238-9852>), John Fox [ctb], Alexander Bauer [ctb], Pavel N. Krivitsky [ctb, cph] (<https://orcid.org/0000-0002-9101-3362>, shared copyright on simulate.formula)

Initial release