BayesianTools (version 0.1.6)

getSample: Extracts the sample from a bayesianOutput

Description

Extracts the sample from a bayesianOutput

Usage

getSample(sampler, parametersOnly = T, coda = F, start = 1,
  end = NULL, thin = 1, numSamples = NULL, whichParameters = NULL,
  includesProbabilities = F, reportDiagnostics = FALSE, ...)

Arguments

sampler

an object of class mcmcSampler, mcmcSamplerList, smcSampler, smcSamplerList, mcmc, mcmc.list, double, numeric

parametersOnly

if F, likelihood, posterior and prior values are also provided in the output

coda

works only for mcmc classes - provides output as a coda object. Note: if mcmcSamplerList contains mcmc samplers such as DE that have several chains, the internal chains will be collapsed. This may not be the desired behavior for all applications.

start

for mcmc samplers start value in the chain. For SMC samplers, start particle

end

for mcmc samplers end value in the chain. For SMC samplers, end particle

thin

thinning parameter. Either an integer determining the thinning intervall (default is 1) or "auto" for automatic thinning.

numSamples

sample size (only used if thin = 1). If you want to use numSamples set thin to 1.

whichParameters

possibility to select parameters by index

includesProbabilities

applies only to getSample.Matrix. logical, determining whether probabilities should be included in the result.

reportDiagnostics

logical, determines whether settings should be included in the output

...

further arguments

Details

If thin is greater than the total number of samples in the sampler object the first and the last element (of each chain if a sampler with multiples chains is used) are sampled. If numSamples is greater than the total number of samples all samples are selected. In both cases a warning is displayed.

If thin and numSamples is passed, the function will use the thin argument if it is valid and greater than 1, else numSamples will be used.

Examples

# NOT RUN {
ll = function(x) sum(dnorm(x, log = TRUE))

setup = createBayesianSetup(ll, lower = c(-10,-10), upper = c(10,10))

settings = list(nrChains = 2, iterations = 1000)
out <- runMCMC(bayesianSetup = setup, sampler = "DEzs", settings = settings)

# population MCMCs divide the interations by the number of internal chains,
# so the end of the 3 chains is 1000/3 = 334
sample <- getSample(out, start = 100, end = 334, thin = 10) 

# sampling with number of samples instead of thinning and
# returning a coda object
sample <- getSample(out, start = 100, numSamples = 60, coda = TRUE)
plot(sample)


# MCMC with a single chain:
settings_2 <- list(nrChains = 1, iterations = 1000)
out_2 <- runMCMC(setup, sampler = "Metropolis", settings = settings_2)
sample_2 <- getSample(out_2, numSamples = 100)

# }