hopit.control {hopit}

Auxiliary for controlling the fitting of a hopit model

Description

An auxiliary function for controlling the fitting of a hopit model. Use this function to set the control parameters of the hopit and other related functions.

Usage

hopit.control(
  grad.eps = 3e-05,
  bgfs.maxit = 10000,
  cg.maxit = 10000,
  nlm.maxit = 150,
  bgfs.reltol = 5e-10,
  cg.reltol = 5e-10,
  nlm.gradtol = 1e-07,
  nlm.steptol = 1e-07,
  fit.methods = "BFGS",
  nlm.fit = FALSE,
  trace = TRUE,
  transform.latent = "none",
  transform.thresh = "none"
)

Arguments

grad.eps

an epsilon parameter ("a very small number") used to calculate the Hessian from the gradient function.

bgfs.maxit, cg.maxit, nlm.maxit

the maximum number of iterations. See optim and nlm for details.

bgfs.reltol, cg.reltol

the relative convergence tolerances for the BFGS and the CG methods. See optim for details.

nlm.gradtol, nlm.steptol

a tolerance at which the scaled gradient is considered close enough to zero and a minimum allowable relative step length for the nlm method. See nlm.

fit.methods

"CG", "BFGS", or both. If both, the CG is run first, followed by the BFGS. See optim.

nlm.fit

a logical; if FALSE (default) the nlm optimization method is omitted and only the BFGS and/or the CG methods are run.

trace

a logical for whether to trace the process of model fitting.

transform.latent, transform.thresh

a type of transformation applied to the all of the latent's or all of the threshold's numeric variables. Possible values:

• "none" : no transformation

• "min" : subtract the minimum from a variable

• "scale_01" : transform the variable to fit the range from 0 to 1

• "standardize" or "standardise" : subtract the mean from a variable then divide it by its standard deviation

• "standardize_trunc" or "standardise_trunc" : subtract the minimum from a variable then divide it by its standard deviation

Author(s)

Maciej J. Danko

See Also

hopit