optimizing_best: Run several instances of the optimization algorithm.
In luisdamiano/BayesHMM: Full Bayesian Inference for Hidden Markov Models
Description
Usage
Arguments
Value
Description
Note that this function returns the results of the converging instance with highest log posterior density while optimizing_all returns all.
Usage
1
optimizing_best(stanDots, nRuns, nCores)
Arguments
stanDots
The arguments to the passed to rstan's optimizing.
nRuns
An optional integer with the number of initializations.
nCores
An optional integer with the number of cores to be used. If equal to one, the instances are run sequentially. Otherwise, doParallel's backend is used for parallel computing. It defaults to one.
Value
An Optimization object.
luisdamiano/BayesHMM documentation built on May 20, 2019, 2:59 p.m.
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Description Usage Arguments Value
Description
Note that this function returns the results of the converging instance with highest log posterior density while optimizing_all returns all.
Usage
1 | optimizing_best(stanDots, nRuns, nCores)
|
Arguments
stanDots |
The arguments to the passed to rstan's |
nRuns |
An optional integer with the number of initializations. |
nCores |
An optional integer with the number of cores to be used. If equal to one, the instances are run sequentially. Otherwise, doParallel's backend is used for parallel computing. It defaults to one. |
Value
An Optimization object.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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