omxParallelCI {OpenMx} | R Documentation |

OpenMx provides two functions to calculate confidence intervals for already-run MxModel objects that contain an MxInterval object (i.e., an `mxCI()`

statement), without recalculating point estimates, fitfunction derivatives, or expectations.

The primary function is `omxRunCI()`

. This is a wrapper for `omxParallelCI()`

with arguments `run=TRUE`

and `independentSubmodels=FALSE`

, and is the recommended interface.

`omxParallelCI()`

does the work of calculating confidence intervals. The "parallel" in the function's name refers to the not-yet-implemented feature of running independent submodels in parallel.

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

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

`run` |
Logical; For |

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

`independentSubmodels` |
Logical; For |

`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)
# 1. Build and run a model, don't compute intervals yet
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)
# 2. Compute the CIs on factorRun, and view summary
factorCI1 <- omxRunCI(factorRun)
summary(factorCI1)$CI
# 3. Use low-level omxParallelCI interface
factorCI2 <- omxParallelCI(factorRun)
# 4. Build, but don't run the newly-created model
factorCI3 <- omxParallelCI(factorRun, run= FALSE)
```

[Package *OpenMx* version 2.21.8 Index]