Repeatedly estimate model using resampling with replacement
This is a low-level compute plan object to perform resampling with replacement.
mxComputeBootstrap(data, plan, replications=200, ...,
verbose=0L, parallel=TRUE, freeSet=NA_character_,
OK=c("OK", "OK/green"), only=NA_integer_)data |
A vector of dataset or model names. |
plan |
The compute plan used to optimize the model for each data set. |
replications |
The number of resampling replications. If available, replications from prior mxBootstrap invocations will be reused. |
... |
Not used. Forces remaining arguments to be specified by name. |
verbose |
For levels greater than 0, enables runtime diagnostics |
parallel |
Whether to process the replications in parallel |
freeSet |
names of matrices containing free variables |
OK |
The set of status code that are considered successful |
only |
When provided, only the given replication from a prior
run of |
The ‘only’ option facilitates investigation of a single replication attempt.
Output is stored in the compute object's output
slot. Specifically, model$compute$output$raw contains a data
frame with parameters in columns and replications in rows. In addition
to parameters, the seed, fit, and statusCode
of the replication is also included.
When ‘only’ is set to a particular replications, the weight
vectors (one per dataset) are also returned in the compute object's
output slot. model$compute$output$weight is a character
vector (by dataset name) of numeric vectors (the weights). These
weights can be used to recreate a model identical to the model used
in the given replication.
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