mxRestore | R Documentation |
Restore model state from a checkpoint file
mxRestore(
model,
chkpt.directory = mxOption(model, "Checkpoint directory"),
chkpt.prefix = mxOption(model, "Checkpoint Prefix"),
line = NULL,
strict = FALSE
)
mxRestoreFromDataFrame(model, checkpoint, line = NULL)
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 |
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
.
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.
The OpenMx User's guide can be found at https://openmx.ssri.psu.edu/documentation
Other model state:
mxComputeCheckpoint()
,
mxSave()
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
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