OpenMx (version 2.5.1)

mxOption: Set or Clear an Optimizer Option

Description

The function sets, shows, or clears an option that is specific to the optimizer in the back-end.

Usage

mxOption(model, key, value, reset = FALSE)

Arguments

model

An MxModel object or NULL

key

The name of the option.

value

The value of the option.

reset

If TRUE then reset all options to their defaults.

Value

• If a model is provided, it is returned with the optimizer option either set or cleared. If value is empty, the current value is returned.

Details

mxOption is used to set, clear, or query an option (given in the key argument) in the back-end optimizer. Valid option keys are listed below.

Use value = NULL to remove an existing option. Leaving value blank will return the current value of the option specified by key.

To reset all options to their default values, use reset = TRUE. When reset = TRUE, key and value are ignored.

If the model argument is set to NULL, the default optimizer option (i.e those applying to all models by default) will be set.

To see the defaults, use getOption('mxOptions').

Before the model is submitted to the back-end, all keys and values are converted into strings using the as.character function.

The Default optimizer option can only be set globally (i.e., with model=NULL), and not specifically to a given MxModel.

Currently, options Gradient algorithm and Gradient iterations are ignored by all optimizers other than SLSQP. Also, currently option Analytic Gradients only affects SLSQP and NPSOL.

The maximum number of major iterations (the option Major iterations) for optimization for NPSOL can be specified either by using a numeric value (such as 50, 1000, etc) or by specifying a user-defined function. The user-defined function should accept two arguments as input, the number of parameters and the number of constraints, and return a numeric value as output.

OpenMx options

rcl{ Number of Threads i the number of processor cores to use. Use detectCores() to find how many are available. Calculate Hessian [Yes | No] calculate the Hessian explicitly after optimization. Standard Errors [Yes | No] return standard error estimates from the explicitly calculate hessian. Default optimizer [NPSOL | SLSQP | CSOLNP] the gradient-descent optimizer to use Number of Threads [0|1|2|...|10|...] number of threads used for optimization. This is how parallelism works. Default value of 0 uses detectCores() - 1. Feasibility tolerance r the maximum acceptable absolute violations in linear and nonlinear constraints. Optimality tolerance r the maximum acceptable difference in fit. Gradient algorithm see list finite difference method, either 'forward' or 'central'. Gradient iterations 1:4 the number of Richardson extrapolation iterations Analytic Gradients [Yes | No] should the optimizer use analytic gradients (if available)? loglikelihoodScale i factor by which the loglikelihood is scaled. }

NPSOL-specific options

rcl{ Nolist this option suppresses printing of the options Print level i the value of i controls the amount of printout produced by the major iterations Minor print level i the value of i controls the amount of printout produced by the minor iterations Print file i for i > 0 a full log is sent to the file with logical unit number i. Summary file i for i > 0 a brief log will be output to file i. Function precision r a measure of accuracy with which the fitfunction and constraint functions can be computed. Infinite bound size r if r > 0 defines the "infinite" bound bigbnd. Major iterations i or a function the maximum number of major iterations before termination. Verify level [-1:3 | Yes | No] see NPSOL manual. Line search tolerance r controls the accuracy with which a step is taken. Derivative level [0-3] see NPSOL manual. Hessian [Yes | No] return the Hessian (Yes) or the transformed Hessian (No). Step Limit r maximum change in free parameters at first step of linesearch. }

CSOLNP-specific options

rcl{ Major iteration_CSOLNP i maximum number of major iterations. Minor iteration_CSOLNP i maximum number of minor iterations. Function precision_CSOLNP i a measure of accuracy with which the fitfunction and constraint functions can be computed. }

Checkpointing options

rcl{ Always Checkpoint [Yes | No] whether to checkpoint all models during optimization. Checkpoint Directory path the directory into which checkpoint files are written. Checkpoint Prefix string the string prefix to add to all checkpoint filenames. Checkpoint Fullpath path overrides the directory and prefix (useful to output to /dev/fd/2) Checkpoint Units see list the type of units for checkpointing: 'minutes', 'iterations', or 'evaluations'. Checkpoint Count i the number of units between checkpoint intervals. }

Model transformation options

rcl{ Error Checking [Yes | No] whether model consistency checks are performed in the OpenMx front-end No Sort Data character vector of model names for which FIML data sorting is not performed RAM Inverse Optimization [Yes | No] whether to enable solve(I - A) optimization RAM Max Depth i the maximum depth to be used when solve(I - A) optimization is enabled }

Multivariate normal integration parameters

rcl{ mvnMaxPointsA i base number of integration points mvnMaxPointsB i number of integration points per row mvnMaxPointsC i number of integration points per rows^2 mvnAbsEps i absolute tolerance mvnRelEps i relative tolerance }

References

The OpenMx User's guide can be found at http://openmx.psyc.virginia.edu/documentation.

See Also

mxModel all uses of mxOption are via an mxModel whose options are set or cleared.

Examples

# set the Numbder of Threads (cores to use)
mxOption(NULL, "Number of Threads", detectCores() - 1)

testModel <- mxModel(model = "testModel") # make a model to use for example
testModel$options   # show the model options (none yet)
options()$mxOptions # list all mxOptions (global settings)

testModel <- mxOption(testModel, "Function precision", 1e-5) # set precision
testModel <- mxOption(testModel, "Function precision", NULL) # clear precision
# N.B. This is model-specific precision (defaults to global setting)

# may optimize for speed
# at cost of not getting standard errors
testModel <- mxOption(testModel, "Calculate Hessian", "No")
testModel <- mxOption(testModel, "Standard Errors"  , "No")

testModel$options # see the list of options you set