run_data: Run a Bayesian EAM Model for Fixed-effect or Random-effect
In TasCL/ggdmc: Dynamic Model of Choice with Parallel Computation, and C++ Capabilities
Description
Usage
Arguments
Value
Description
These are the C++ functions hierarchical Bayesian models. The user usually
should not call this function directly. Use h.run.dmc or
run.dmc wrapper instead. Two parallel versions of run uses
Open MPI to calculate diffusion probability density. Use them by setting
cores greater than 2 in h.run.dmc
Usage
1
2
3
4
5
6
7
run_data(samples, setting, debug = FALSE)
run_data_parallel(samples, setting, debug = FALSE)
run_hyper(samples, setting)
run_hyper_parallel(samples, setting)
Arguments
samples
a DMC sample/object
setting
a container to store all settings for running model fitting
debug
a debugging switch to test chain randomisation. Default false
Value
a DMC sample
TasCL/ggdmc documentation built on May 9, 2019, 4:19 p.m.
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Description Usage Arguments Value
Description
These are the C++ functions hierarchical Bayesian models. The user usually
should not call this function directly. Use h.run.dmc or
run.dmc wrapper instead. Two parallel versions of run uses
Open MPI to calculate diffusion probability density. Use them by setting
cores greater than 2 in h.run.dmc
Usage
1 2 3 4 5 6 7 | run_data(samples, setting, debug = FALSE)
run_data_parallel(samples, setting, debug = FALSE)
run_hyper(samples, setting)
run_hyper_parallel(samples, setting)
|
Arguments
samples |
a DMC sample/object |
setting |
a container to store all settings for running model fitting |
debug |
a debugging switch to test chain randomisation. Default false |
Value
a DMC sample
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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