R: Restore model state from a checkpoint file

mxRestore {OpenMx} | R Documentation

## Restore model state from a checkpoint file

### Description

Restore model state from a checkpoint file

### Usage

    mxRestore(
      model,
      chkpt.directory = mxOption(model, "Checkpoint directory"),
      chkpt.prefix = mxOption(model, "Checkpoint Prefix"),
      line = NULL,
      strict = FALSE
    )

    mxRestoreFromDataFrame(model, checkpoint, line = NULL)

### Arguments

`model` | an MxModel object
`chkpt.directory` | character. Directory where the checkpoint file is located
`chkpt.prefix` | character. Prefix of the checkpoint file
`line` | integer. Which line from the checkpoint file to restore (defaults to the last line)
`strict` | logical. Require that the checkpoint name and model name match
`checkpoint` | a data.frame containing the model state

### Details

In general, the arguments ‘chkpt.directory’ and ‘chkpt.prefix’ should be identical to the `mxOption`: ‘Checkpoint Directory’ and ‘Checkpoint Prefix’ that were specified on the model before execution.

Alternatively, the checkpoint file can be manually loaded as a data.frame in R and passed to `mxRestoreFromDataFrame`. Use `read.table` with the options `header=TRUE, sep="\t", stringsAsFactors=FALSE, check.names=FALSE`.

### Value

Returns an MxModel object with free parameters updated to the last saved values. When ‘line’ is provided, the MxModel is updated to the values on that line within the checkpoint file.

### References

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

### See Also

Other model state: `mxComputeCheckpoint()`, `mxSave()`

### Examples

    library(OpenMx)

    # Simulate some data

    x=rnorm(1000, mean=0, sd=1)
    y= 0.5*x + rnorm(1000, mean=0, sd=1)
    tmpFrame <- data.frame(x, y)
    tmpNames <- names(tmpFrame)

    dir <- tempdir()  # safe place to create files
    mxOption(key="Checkpoint Directory", value=dir)

    # Create a model that includes an expected covariance matrix,
    # an expectation function, a fit function, and an observed covariance matrix

    data <- mxData(cov(tmpFrame), type="cov", numObs = 1000)
    expCov <- mxMatrix(type="Symm", nrow=2, ncol=2, values=c(.2,.1,.2), free=TRUE, name="expCov")
    expFunction <- mxExpectationNormal(covariance="expCov", dimnames=tmpNames)
    fitFunction <- mxFitFunctionML()
    testModel <- mxModel(model="testModel", expCov, data, expFunction, fitFunction)

    #Use mxRun to optimize the free parameters in the expected covariance matrix
    modelOut <- mxRun(testModel, checkpoint = TRUE)
    modelOut$expCov

    #Use mxRestore to load the last checkpoint saved state of the model
    modelRestore <- mxRestore(testModel)
    modelRestore$expCov

[Package OpenMx version 2.19.5 Index]