beast (version 1.1)

beast: Main function

Description

This is the main function of the package, implementing the MCMC sampler described in Papastamoulis et al (2017).

Usage

beast(myDataList, burn, nIter, mhPropRange, mhSinglePropRange, startPoint,
    timeScale, savePlots, zeroNormalization, LRange, tau,
    gammaParameter, nu0, alpha0, beta0, subsetIndex, saveTheta, sameVariance,
    Prior
)

Arguments

myDataList

Observed data in the form of a list with length \(R\), denoting the dimensionality of the multivariate time-series data. For each \(r = 1,\ldots,R\), myDataList[[r]] should correspond to \(T\times N\) array, with myDataList[[r]][t, n] corresponding to the observed data for time-series \(n = 1,\ldots,N\) and time-point \(t = 1,\ldots,T\).

burn

Number of iterations that will be discarder as burn-in period. Default value: burn = 20000.

nIter

Number of MCMC iterations. Default value: nIter = 70000.

mhPropRange

Positive integer corresponding to the parameter \(d_1\) of MCMC Move 3.a of Papastamoulis et al (2017). Default value: mhPropRange = 1.

mhSinglePropRange

Positive integer denoting the parameter \(d_2\) of Papastamoulis et al (2017). Default value: mhPropRange = 40.

startPoint

An (optional) positive integer pointing at the minimum time-point where changes are allowed to occur. Default value: startPoint = 2 (all possible values are taken into account).

timeScale

Null.

savePlots

Character denoting the name of the folder where various plots will be saved to.

zeroNormalization

Logical value denoting whether to normalize to zero all time time-series for \(t=1\). Default: zeroNormalization = FALSE.

LRange

Range of possible values for the number of change-points. Default value: LRange = 0:30.

tau

Positive real number corresponding to parameter \(c\) in Move 2 of Papastamoulis et al (2017). Default value: tau = 0.05.

gammaParameter

Positive real number corresponding to parameter \(\alpha\) of the exponential prior distribution. Default value: gammaParameter = 2.

nu0

Positive real number corresponding to prior parameter \(\nu_0\) in Papastamoulis et al (2017). Default value: nu0 = 0.1.

alpha0

Positive real number corresponding to prior parameter \(\alpha_0\) in Papastamoulis et al (2017). Default value: alpha0 = 1.

beta0

Positive real number corresponding to prior parameter \(\beta_0\) in Papastamoulis et al (2017). Default value: beta0 = 1.

subsetIndex

Optional subset of integers corresponding to time-series indexes. If not null, the sampler will be applied only to the specified subset.

saveTheta

Logical value indicating whether to save the generated values of the mean per time-point across the MCMC trace. Default: FALSE.

sameVariance

Logical value indicating whether to assume the same variance per time-point across time-series. Default value: sameVariance = TRUE.

Prior

Character string specifying the prior distribution of the number of change-points. Allowed values: Prior = "complexity" (default) or Prior = "Poisson" (not suggested).

Value

The output of the sampler is returned as a list, with the following features:

Cutpoint_posterior_median

The estimated medians per change-point, conditionally on the most probable number of change-points per time-series.

Cutpoint_posterior_variance

The estimated variances per change-points, conditionally on the most probable number of change-points per time-series.

NumberOfCutPoints_posterior_distribution

Posterior distributions of number of change-points per time-series.

NumberOfCutPoints_MAP

The most probable number of change-points per time-series.

Metropolis-Hastings_acceptance_rate

Acceptance of the MCMC move-types.

subject_ID

the identifier of individual time-series.

Cutpoint_mcmc_trace_map

The sampled values of each change-point per time series, conditionally on the MAP values.

theta

The sampled values of the means per time-series, conditionally on the MAP values.

nCutPointsTrace

The sampled values of the number of change-points, per time-series.

References

Papastamoulis P., Furukawa T., van Rhijn N., Bromley M., Bignell E. and Rattray M. (2017). Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling. arXiv:1709.06111 [stat.AP]

Examples

# NOT RUN {
# toy-example (MCMC iterations not enough)
library('beast')	# load package
data("FungalGrowthDataset")	# load dataset
myIndex <- c(392, 62, 3, 117)	# run the sampler only for the 
#                                 specific subset of time-series
set.seed(1)	
# Run MCMC sampler with very small number of iterations (nIter):
run_mcmc <- beast(myDataList = FungalGrowthDataset, subsetIndex = myIndex, 
			zeroNormalization = TRUE, nIter = 40, burn = 20) 
# Print output:
print(run_mcmc)
# Plot output to file: "beast_plot.pdf"
plot(run_mcmc, fileName = "beast_plot_toy.pdf", timeScale=1/6, xlab = "hours", ylab = "growth")
# Run the following commands to obtain convergence:

# }
# NOT RUN {
# This example illustrates the package using a subset of four 
#      time-series of the fungal dataset. 
library('beast')	# load package
data("FungalGrowthDataset")	# load dataset
myIndex <- c(392, 62, 3, 117)	# run the sampler only for the 
#                                 specific subset of time-series
set.seed(1)		# optional
# Run MCMC sampler with the default number of iterations (nIter =70000):
run_mcmc <- beast(myDataList = FungalGrowthDataset, subsetIndex = myIndex, 
			zeroNormalization = TRUE) 
# Print output:
print(run_mcmc)
# Plot output to file: "beast_plot.pdf"
plot(run_mcmc, fileName = "beast_plot.pdf", timeScale=1/6, xlab = "hours", ylab = "growth")
# NOTE 1: for a complete analysis remove the `subsetIndex = myIndex` argument.
# NOTE 2: `zeroNormalization = TRUE` is an optional argument that forces all 
#	   time-series to start from zero. It is not supposed to be used 
#	   for other applications.
# }