buildMCMC: Create an MCMC object from a NIMBLE model, or an MCMC...
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buildMCMC R Documentation
Create an MCMC object from a NIMBLE model, or an MCMC configuration object
Description
First required argument, which may be of class MCMCconf (an MCMC configuration object), or inherit from class modelBaseClass (a NIMBLE model object). Returns an uncompiled executable MCMC object. See details.
Usage
buildMCMC(conf, print, ...)
Arguments
conf
Either an MCMC configuration object of class MCMCconf or a NIMBLE model object. An MCMC configuration object would be returned from configureMCMC and contains information on the model, samplers, monitors, and thinning intervals to be used. Alternatively, conf may a NIMBLE model object, in which case default configuration from calling configureMCMC(model, ...l) will be used.
print
A logical argument, specifying whether to print details of the MCMC samplers and monitors.
...
Additional arguments to be passed to configureMCMC if conf is a NIMBLE model object (see help(configureMCMC)).
Details
Calling buildMCMC(conf) will produce an uncompiled MCMC object. The object contains several methods, including the main run function for running the MCMC, a getTimes function for determining the computation time spent in each sampler (see 'getTimes' section below), and functions related to WAIC (getWAIC, getWAICdetails, calculateWAIC (see help(waic)).
The uncompiled run function will have arguments:
niter: The number of iterations to run the MCMC.
nburnin: Number of initial, pre-thinning, MCMC iterations to discard (default = 0).
thin: The thinning interval for the monitors that were specified in the MCMC configuration. If this argument is provided at MCMC runtime, it will take precedence over the thin interval that was specified in the MCMC configuration. If omitted, the thin interval from the MCMC configuration will be used.
thin2: The thinning interval for the second set of monitors (monitors2) that were specified in the MCMC configuration. If this argument is provided at MCMC runtime, it will take precedence over the thin2 interval that was specified in the MCMC configuration. If omitted, the thin2 interval from the MCMC configuration will be used.
reset: Boolean specifying whether to reset the internal MCMC sampling algorithms to their initial state (in terms of self-adapting tuning parameters), and begin recording posterior sample chains anew. Specifying reset = FALSE allows the MCMC algorithm to continue running from where it left off, appending additional posterior samples to the already existing sample chains. Generally, reset = FALSE should only be used when the MCMC has already been run (default = TRUE).
resetMV: Boolean specifying whether to begin recording posterior sample chains anew. This argument is only considered when using reset = FALSE. Specifying reset = FALSE, resetMV = TRUE allows the MCMC algorithm to continue running from where it left off, but without appending the new posterior samples to the already existing samples, i.e. all previously obtained samples will be erased. This option can help reduce memory usage during burn-in (default = FALSE).
resetWAIC: Boolean specifying whether to reset the WAIC summary statistics to their initial states and thereby begin the WAIC calculation anew (default = TRUE). Specifying resetWAIC = FALSE allows the WAIC calculation to continue running from where it left off.
initializeModel: Boolean specifying whether to run the initializeModel routine on the underlying model object, prior to beginning MCMC sampling (default = TRUE).
chain: Integer specifying the MCMC chain number. The chain number is passed to each MCMC sampler's before_chain and after_chain methods. The value for this argument is specified automatically from invocation via runMCMC, and genernally need not be supplied when calling mcmc$run (default = 1).
time: Boolean specifying whether to record runtimes of the individual internal MCMC samplers. When time = TRUE, a vector of runtimes (measured in seconds) can be extracted from the MCMC using the method mcmc$getTimes() (default = FALSE).
progressBar: Boolean specifying whether to display a progress bar during MCMC execution (default = TRUE). The progress bar can be permanently disabled by setting the system option nimbleOptions(MCMCprogressBar = FALSE).
Samples corresponding to the monitors and monitors2 from the MCMCconf are stored into the interval variables mvSamples and mvSamples2, respectively.
These may be accessed and converted into R matrix or list objects via:
as.matrix(mcmc$mvSamples)
as.list(mcmc$mvSamples)
as.matrix(mcmc$mvSamples2)
as.list(mcmc$mvSamples2)
The uncompiled MCMC function may be compiled to a compiled MCMC object, taking care to compile in the same project as the R model object, using:
Cmcmc <- compileNimble(Rmcmc, project = Rmodel)
The compiled object will function identically to the uncompiled object except acting on the compiled model object.
Timing the MCMC samplers
If you want to obtain the computation time spent in each sampler, you can set time=TRUE as a run-time argument to run() and then use the method getTimes() to obtain the times.
Calculating WAIC
Please see help(waic) for more information.
Author(s)
Daniel Turek
References
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11: 3571-3594.
Gelman, A., Hwang, J. and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing 24(6): 997-1016.
Ariyo, O., Quintero, A., Munoz, J., Verbeke, G. and Lesaffre, E. (2019). Bayesian model selection in linear mixed models for longitudinal data. Journal of Applied Statistics 47: 890-913.
See Also
configureMCMC runMCMC nimbleMCMC
Examples
## Not run:
code <- nimbleCode({
mu ~ dnorm(0, 1)
x ~ dnorm(mu, 1)
y ~ dnorm(x, 1)
})
Rmodel <- nimbleModel(code, data = list(y = 0))
conf <- configureMCMC(Rmodel, monitors = c('mu', 'x'), enableWAIC = TRUE)
Rmcmc <- buildMCMC(conf)
Cmodel <- compileNimble(Rmodel)
Cmcmc <- compileNimble(Rmcmc, project=Rmodel)
## Running the MCMC with `run`
Cmcmc$run(10000)
samples <- as.matrix(Cmcmc$mvSamples)
samplesAsList <- as.list(Cmcmc$mvSamples)
head(samples)
## Getting WAIC
waicInfo <- Cmcmc$getWAIC()
waicInfo$WAIC
waicInfo$pWAIC
## Timing the samplers (must set `time = TRUE` when running the MCMC)
Cmcmc$run(10000, time = TRUE)
Cmcmc$getTimes()
## End(Not run)
nimble documentation built on Sept. 11, 2024, 7:10 p.m.
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| buildMCMC | R Documentation |
Create an MCMC object from a NIMBLE model, or an MCMC configuration object
Description
First required argument, which may be of class MCMCconf (an MCMC configuration object), or inherit from class modelBaseClass (a NIMBLE model object). Returns an uncompiled executable MCMC object. See details.
Usage
buildMCMC(conf, print, ...)
Arguments
conf |
Either an MCMC configuration object of class |
print |
A logical argument, specifying whether to print details of the MCMC samplers and monitors. |
... |
Additional arguments to be passed to |
Details
Calling buildMCMC(conf) will produce an uncompiled MCMC object. The object contains several methods, including the main run function for running the MCMC, a getTimes function for determining the computation time spent in each sampler (see 'getTimes' section below), and functions related to WAIC (getWAIC, getWAICdetails, calculateWAIC (see help(waic)).
The uncompiled run function will have arguments:
niter: The number of iterations to run the MCMC.
nburnin: Number of initial, pre-thinning, MCMC iterations to discard (default = 0).
thin: The thinning interval for the monitors that were specified in the MCMC configuration. If this argument is provided at MCMC runtime, it will take precedence over the thin interval that was specified in the MCMC configuration. If omitted, the thin interval from the MCMC configuration will be used.
thin2: The thinning interval for the second set of monitors (monitors2) that were specified in the MCMC configuration. If this argument is provided at MCMC runtime, it will take precedence over the thin2 interval that was specified in the MCMC configuration. If omitted, the thin2 interval from the MCMC configuration will be used.
reset: Boolean specifying whether to reset the internal MCMC sampling algorithms to their initial state (in terms of self-adapting tuning parameters), and begin recording posterior sample chains anew. Specifying reset = FALSE allows the MCMC algorithm to continue running from where it left off, appending additional posterior samples to the already existing sample chains. Generally, reset = FALSE should only be used when the MCMC has already been run (default = TRUE).
resetMV: Boolean specifying whether to begin recording posterior sample chains anew. This argument is only considered when using reset = FALSE. Specifying reset = FALSE, resetMV = TRUE allows the MCMC algorithm to continue running from where it left off, but without appending the new posterior samples to the already existing samples, i.e. all previously obtained samples will be erased. This option can help reduce memory usage during burn-in (default = FALSE).
resetWAIC: Boolean specifying whether to reset the WAIC summary statistics to their initial states and thereby begin the WAIC calculation anew (default = TRUE). Specifying resetWAIC = FALSE allows the WAIC calculation to continue running from where it left off.
initializeModel: Boolean specifying whether to run the initializeModel routine on the underlying model object, prior to beginning MCMC sampling (default = TRUE).
chain: Integer specifying the MCMC chain number. The chain number is passed to each MCMC sampler's before_chain and after_chain methods. The value for this argument is specified automatically from invocation via runMCMC, and genernally need not be supplied when calling mcmc$run (default = 1).
time: Boolean specifying whether to record runtimes of the individual internal MCMC samplers. When time = TRUE, a vector of runtimes (measured in seconds) can be extracted from the MCMC using the method mcmc$getTimes() (default = FALSE).
progressBar: Boolean specifying whether to display a progress bar during MCMC execution (default = TRUE). The progress bar can be permanently disabled by setting the system option nimbleOptions(MCMCprogressBar = FALSE).
Samples corresponding to the monitors and monitors2 from the MCMCconf are stored into the interval variables mvSamples and mvSamples2, respectively.
These may be accessed and converted into R matrix or list objects via:
as.matrix(mcmc$mvSamples)
as.list(mcmc$mvSamples)
as.matrix(mcmc$mvSamples2)
as.list(mcmc$mvSamples2)
The uncompiled MCMC function may be compiled to a compiled MCMC object, taking care to compile in the same project as the R model object, using:
Cmcmc <- compileNimble(Rmcmc, project = Rmodel)
The compiled object will function identically to the uncompiled object except acting on the compiled model object.
Timing the MCMC samplers
If you want to obtain the computation time spent in each sampler, you can set time=TRUE as a run-time argument to run() and then use the method getTimes() to obtain the times.
Calculating WAIC
Please see help(waic) for more information.
Author(s)
Daniel Turek
References
Watanabe, S. (2010). Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research 11: 3571-3594.
Gelman, A., Hwang, J. and Vehtari, A. (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing 24(6): 997-1016.
Ariyo, O., Quintero, A., Munoz, J., Verbeke, G. and Lesaffre, E. (2019). Bayesian model selection in linear mixed models for longitudinal data. Journal of Applied Statistics 47: 890-913.
See Also
configureMCMC runMCMC nimbleMCMC
Examples
## Not run:
code <- nimbleCode({
mu ~ dnorm(0, 1)
x ~ dnorm(mu, 1)
y ~ dnorm(x, 1)
})
Rmodel <- nimbleModel(code, data = list(y = 0))
conf <- configureMCMC(Rmodel, monitors = c('mu', 'x'), enableWAIC = TRUE)
Rmcmc <- buildMCMC(conf)
Cmodel <- compileNimble(Rmodel)
Cmcmc <- compileNimble(Rmcmc, project=Rmodel)
## Running the MCMC with `run`
Cmcmc$run(10000)
samples <- as.matrix(Cmcmc$mvSamples)
samplesAsList <- as.list(Cmcmc$mvSamples)
head(samples)
## Getting WAIC
waicInfo <- Cmcmc$getWAIC()
waicInfo$WAIC
waicInfo$pWAIC
## Timing the samplers (must set `time = TRUE` when running the MCMC)
Cmcmc$run(10000, time = TRUE)
Cmcmc$getTimes()
## End(Not run)
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