sem (version 2.1-1)

optimizers: sem Optimizers

Description

These functions call optimizers (nlm, optim, or nlminb), to fit structural equation models, and are called by the sem function. The user would not normally call these functions directly, but rather supply one of them in the optimizer argument to sem. Users may also write them own optimizer functions.

Usage

optimizerNlm(start, objective=objectiveML, gradient=TRUE, 
	maxiter, debug, par.size, model.description, warn, ...)
	
optimizerOptim(start, objective=objectiveML, gradient=TRUE, 
	maxiter, debug, par.size, model.description, warn, method="CG", ...)

optimizerNlminb(start, objective=objectiveML, gradient=TRUE, maxiter, 
debug, par.size, model.description, warn, ...)

Arguments

start

a vector of start values for the parameters.

objective

the objective function to be optimized; see objective.functions.

gradient

TRUE if an analytic gradient is to be used (if one is available).

maxiter

the maximum number of iterations allowed.

debug

TRUE to show the iteration history and other available information about the optimization.

par.size

"startvalues" to have the optimizer scale the problem according to the magitudes of the start values (ignored by optimizerNlminb).

model.description

a list with elements describing the structural-equation model (see the code for details).

warn

if FALSE, suppress warnings during the optimization.

method

the method to be employed by the optim optimizer; the default is "CG" (conjugate-gradient).

...

additional arguments for the nlm, optim, or nlminb optimizer.

Value

• An object of class "semResult", with elements:
• convergence
  TRUE if the optimization apparently converged.
• iterations
  the number of iterations required.
• par
  the vector of parameter estimates.
• vcov
  the estimated covariance matrix of the parameter estimates, based on a numeric Hessian; not supplied by optimizerNlminb.
• criterion
  the optimized value of the objective function.
• C
  the model-implied covariance or moment matrix at the parameter estimates.
• A
  the estimated $A$ matrix.
• P
  the estimated $P$ matrix.

See Also

sem, objective.functions, nlm, optim, nlminb