omxParallelCI {OpenMx} | R Documentation |

`omxParallelCI()`

calculates confidence intervals for an already-run MxModel object that contains an MxInterval object (i.e., an `mxCI()`

statement), without recalculating point estimates, fitfunction derivatives, or expectations. The "parallel" in the function's name refers to the not-yet-implemented feature of running independent submodels in parallel. `omxRunCI()`

is a wrapper for `omxParallelCI()`

with arguments `run=TRUE`

and `independentSubmodels=FALSE`

, and is the recommended interface.

omxParallelCI(model, run = TRUE, verbose=0L, independentSubmodels=TRUE, optimizer=mxOption(NULL,"Default optimizer")) omxRunCI(model, verbose=0L, optimizer="SLSQP")

`model` |
An MxModel object that contains an MxInterval object (i.e., an |

`run` |
Logical; should the resulting MxModel object be passed through |

`verbose` |
Integer; defaults to zero; verbosity level passed to MxCompute* objects. |

`independentSubmodels` |
Logical; defaults to |

`optimizer` |
Character string selecting the gradient-descent optimizer to be used to find confidence limits; one of "NPSOL", "CSOLNP", or "SLSQP". The default for |

When `independentSubmodels=TRUE`

, `omxParallelCI()`

creates an independent MxModel object for each quantity specified in the 'reference' slot of `model`

's MxInterval object, and places these independent MxModels inside `model`

. Each of these independent submodels calculates the confidence limits of its own quantity when the container model is run. When `independentSubmodels=FALSE`

, no submodels are added to `model`

. Instead, `model`

is provided with a dedicated compute plan consisting only of an MxComputeConfidenceInterval step. Note that using `independentSubmodels=FALSE`

will overwrite any compute plan already inside `model`

.

The functions return `model`

, augmented with independent submodels (if `independentSubmodels=TRUE`

) or with a non-default compute plan (if `independentSubmodels=FALSE`

), and possibly having been passed through `mxRun()`

(if `run=TRUE`

). Naturally, if `run=FALSE`

, the user can subsequently run the returned model to obtain confidence intervals. Users are cautioned that the returned model may not be very amenable to being further modified and re-fitted (e.g., having some free parameters fixed via `omxSetParameters()`

and passed through `mxRun()`

to get new point estimates) unless the added submodels or the non-default compute plan are eliminated. The exception is if `run=TRUE`

and `independentSubmodels=TRUE`

(which is always the case with `omxRunCI()`

), since the non-default compute plan is set to be non-persistent, and will automatically be replaced with a default compute plan the next time the model is passed to `mxRun()`

.

`mxCI()`

, MxInterval, `mxComputeConfidenceInterval()`

require(OpenMx) data(demoOneFactor) manifests <- names(demoOneFactor) latents <- c("G") factorModel <- mxModel("One Factor", type="RAM", manifestVars=manifests, latentVars=latents, mxPath(from=latents, to=manifests), mxPath(from=manifests, arrows=2), mxPath(from=latents, arrows=2, free=FALSE, values=1.0), mxData(observed=cov(demoOneFactor), type="cov", numObs=500), # add confidence intervals for free params in A and S matrices mxCI(c('A', 'S'))) factorRun <- mxRun(factorModel) factorCI <- omxParallelCI(factorRun) factorCI2 <- omxRunCI(factorRun)

[Package *OpenMx* version 2.9.6 Index]