R: Control parameters for regression

controlMethod {singleRcapture}

R Documentation

Control parameters for regression

Description

controlMethod constructs a list with all necessary control parameters for regression fitting in estimatePopsizeFit and estimatePopsize.

Usage

controlMethod(
  epsilon = 1e-08,
  maxiter = 1000,
  verbose = 0,
  printEveryN = 1L,
  coefStart = NULL,
  etaStart = NULL,
  optimMethod = "Nelder-Mead",
  silent = FALSE,
  optimPass = FALSE,
  stepsize = 1,
  checkDiagWeights = TRUE,
  weightsEpsilon = 1e-08,
  momentumFactor = 0,
  saveIRLSlogs = FALSE,
  momentumActivation = 5,
  criterion = c("coef", "abstol", "reltol")
)

Arguments

`epsilon`	tolerance for fitting algorithms by default `1e-8`.
`maxiter`	maximum number of iterations.
`verbose`	value indicating whether to trace steps of fitting algorithm for `IRLS` fitting method different values of verbose give the following information: 1 – Returns information on the number of current iteration and current log-likelihood. 2 – Returns information on vector of regression parameters at current iteration (and all of the above). 3 – Returns information on reduction of log-likelihood at current iteration (and all of the above). 4 – Returns information on value of log-likelihood function gradient at current iteration (and all of the above). 5 – Returns information on convergence criterion and values that are taken into account when considering convergence (and all of the above). if `optim` method was chosen verbose will be passed to `stats::optim()` as trace.
`printEveryN`	integer value indicating how often to print information specified in `verbose`, by default set to `1`.
`coefStart`, `etaStart`	initial parameters for regression coefficients or linear predictors if `NULL`. For `IRLS` fitting only `etaStart` is needed so if `coefStart` is provided it will be converted to `etaStart`, for `optim` fitting `coefStart` is necessary and argument `etaStart` will be ignored.
`optimMethod`	method of `stats::optim()` used `"Nelder-Mead"` is the default .
`silent`	logical, indicating whether warnings in `IRLS` method should be suppressed.
`optimPass`	optional list of parameters passed to `stats::optim(..., control = optimPass)` if FALSE then list of control parameters will be inferred from other parameters.
`stepsize`	only for `IRLS`, scaling of updates to `beta` vector lower value means slower convergence but more accuracy by default 1. In general if fitting algorithm fails lowering this value tends to be most effective at correcting it.
`checkDiagWeights`	logical value indicating whether to check if diagonal elements of working weights matrixes in `IRLS` are sufficiently positive so that these matrixes are positive defined. By default `TRUE`.
`weightsEpsilon`	small number to ensure positive definedness of weights matrixes. Only matters if `checkDiagWeights` is set to `TRUE`. By default `1e-8`.
`momentumFactor`	experimental parameter in `IRLS` only allowing for taking previous step into account at current step, i.e instead of updating regression parameters as: \[\boldsymbol{\beta}_{(a)} = \boldsymbol{\beta}_{(a-1)} + \text{stepsize} \cdot \text{step}_{(a)}\] the update will be made as: \[ \boldsymbol{\beta}_{(a)} = \boldsymbol{\beta}_{(a-1)} + \text{stepsize} \cdot (\text{step}_{(a)} + \text{momentum}\cdot\text{step}_{(a-1)})\]
`saveIRLSlogs`	logical value indicating if information specified in `verbose` should be saved to output object, by default `FALSE`.
`momentumActivation`	the value of log-likelihood reduction bellow which momentum will apply.
`criterion`	criterion used to determine convergence in `IRLS`, multiple values may be provided. By default `c("coef", "abstol")`.

Value

List with selected parameters, it is also possible to call list directly.

Author(s)