Control for saemix estimation method in nlmixr2

Usage

saemixControl(
  map = TRUE,
  fim = TRUE,
  ll.is = TRUE,
  ll.gq = FALSE,
  nbiter.saemix = c(300, 100),
  nbiter.sa = NA,
  nbiter.burn = 5,
  nbiter.map = 5,
  nb.chains = 1,
  fix.seed = TRUE,
  seed = 23456,
  nmc.is = 5000,
  nu.is = 4,
  print.is = FALSE,
  nbdisplay = 100,
  displayProgress = FALSE,
  print = FALSE,
  save = FALSE,
  save.graphs = TRUE,
  directory = "newdir",
  warnings = FALSE,
  nbiter.mcmc = c(2, 2, 2, 0),
  proba.mcmc = 0.4,
  stepsize.rw = 0.4,
  rw.init = 0.5,
  alpha.sa = 0.97,
  nnodes.gq = 12,
  nsd.gq = 4,
  maxim.maxiter = 100,
  nb.sim = 1000,
  nb.simpred = 100,
  ipar.lmcmc = 50,
  ipar.rmcmc = 0.05,
  rxControl = NULL,
  stickyRecalcN = 4,
  maxOdeRecalc = 5,
  odeRecalcFactor = 10^(0.5),
  useColor = NULL,
  printNcol = NULL,
  normType = c("rescale2", "mean", "rescale", "std", "len", "constant"),
  scaleType = c("none", "nlmixr2", "norm", "mult", "multAdd"),
  scaleCmax = 1e+05,
  scaleCmin = 1e-05,
  scaleC = NULL,
  scaleTo = 1,
  addProp = c("combined2", "combined1"),
  calcTables = TRUE,
  compress = TRUE,
  ci = 0.95,
  sigdigTable = NULL,
  sigdig = 4,
  ...
)

Arguments

map: logical, whether to compute MAP estimates (default TRUE)
fim: logical, whether to compute FIM (default TRUE)
ll.is: logical, whether to compute log-likelihood by Importance Sampling (default TRUE)
ll.gq: logical, whether to compute log-likelihood by Gaussian Quadrature (default FALSE)
nbiter.saemix: integer vector of length 2, number of iterations for phase 1 (exploratory) and phase 2 (smoothing) of SAEM (default c(300, 100))
nbiter.sa: number of iterations for simulated annealing (default NA)
nbiter.burn: number of iterations for burn-in (default 5)
nbiter.map: number of iterations for MAP estimation (default 5)
nb.chains: number of chains (default 1)
fix.seed: logical, whether to fix the random seed (default TRUE)
seed: random seed (default 23456)
nmc.is: number of Monte Carlo samples for Importance Sampling (default 5000)
nu.is: number of degrees of freedom for the student distribution in Importance Sampling (default 4)
print.is: logical, whether to print progress during Importance Sampling (default FALSE)
nbdisplay: number of iterations between progress displays (default 100)
displayProgress: logical, whether to display graphical progress (default FALSE)
print: whether saemix prints algorithm progress (default FALSE). Accepts the legacy logical/integer scalar or a pre-built nlmixr2est::iterPrintControl() object; internally the control stores a single iterPrintControl sub-list (matching the nlmixr2est iteration-print unification), and any nonzero every enables the saemix progress output
save: logical, whether to save results to files (default TRUE)
save.graphs: logical, whether to save graphs (default TRUE)
directory: directory where results and graphs are saved (default "newdir")
warnings: logical, whether to show warnings (default FALSE)
nbiter.mcmc: integer vector of length 4, number of iterations for MCMC kernel updates (default c(2, 2, 2, 0))
proba.mcmc: probability for MCMC kernel selection (default 0.4)
stepsize.rw: stepsize for random walk kernel (default 0.4)
rw.init: initial standard deviation for random walk kernel (default 0.5)
alpha.sa: parameter for simulated annealing (default 0.97)
nnodes.gq: number of nodes for Gaussian Quadrature (default 12)
nsd.gq: number of standard deviations for Gaussian Quadrature (default 4)
maxim.maxiter: maximum number of iterations for maximization step (default 100)
nb.sim: number of simulations for visual predictive check (default 1000)
nb.simpred: number of simulations for predictions (default 100)
ipar.lmcmc: parameter for L-MCMC (default 50)
ipar.rmcmc: parameter for R-MCMC (default 0.05)
rxControl: `rxode2` ODE solving options during fitting, created with `rxControl()`
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
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)`.
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.
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
ci: Confidence level for some tables. By default this is 0.95 or 95% confidence.
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.
sigdig: Optimization significant digits; controls the inner/outer optimization tolerance (10^-sigdig), ODE solver tolerance (0.5*10^(-sigdig-2), or 0.5*10^(-sigdig-1.5) for sensitivity/steady-state with liblsoda), and boundary check tolerance (5*10^(-sigdig+1)).
...: Ignored parameters

Value

saemix control structure

Author

Matthew L. Fidler & Antigravity

Examples

# \donttest{
# Example showing control creation:
ctrl <- saemixControl(seed = 123456)
# }