bayesTFR (version 3.0-8)

run.tfr.mcmc: Running Markov Chain Monte Carlo for Parameters of Total Fertility Rate in Phase II

Description

Runs (or continues running) MCMCs for simulating the total fertility rate of all countries of the world (phase II), using a Bayesian hierarchical model.

Usage

run.tfr.mcmc(nr.chains = 3, iter = 62000, 
    output.dir = file.path(getwd(), "bayesTFR.output"), 
    thin = 1, replace.output = FALSE, 
    start.year = 1750, present.year = 2010, wpp.year = 2010, 
    my.tfr.file = NULL, buffer.size = 100, 
    U.c.low = 5.5, U.up = 8.8, U.width = 3,
    mean.eps.tau0 = -0.25, sd.eps.tau0 = 0.4, nu.tau0 = 2, 
    Triangle_c4.low = 1, Triangle_c4.up = 2.5, 
    Triangle_c4.trans.width = 2,
    Triangle4.0 = 0.3, delta4.0 = 0.8, nu4 = 2,
    S.low = 3.5, S.up = 6.5, S.width = 0.5, 
    a.low = 0, a.up = 0.2, a.width = 0.02, 
    b.low = a.low, b.up = a.up, b.width = 0.02, 
    sigma0.low = 0.01, sigma0.up = 0.6, sigma0.width = 0.1, 
    sigma0.min = 0.001, 
    const.low = 0.8, const.up = 2, const.width = 0.3, 
    d.low = 0.05, d.up = 0.5, d.trans.width = 1, 
    chi0 = -1.5, psi0 = 0.6, nu.psi0 = 2, 
    alpha0.p = c(-1, 0.5, 1.5), delta0 = 1, nu.delta0 = 2, 
    dl.p1 = 9, dl.p2 = 9, 
    S.ini = NULL, a.ini = NULL, b.ini = NULL, sigma0.ini = NULL, 
    Triangle_c4.ini = NULL, const.ini = NULL, gamma.ini = 1, 
    proposal_cov_gammas = NULL,
    seed = NULL, parallel = FALSE, nr.nodes = nr.chains, 
    save.all.parameters = FALSE, compression.type = 'None',
    auto.conf = list(max.loops=5, iter=62000, iter.incr=10000, 
        nr.chains=3, thin=80, burnin=2000),
    verbose = FALSE, verbose.iter = 10, ...)
		
continue.tfr.mcmc(iter, chain.ids=NULL, 
    output.dir=file.path(getwd(), "bayesTFR.output"), 
    parallel = FALSE, nr.nodes = NULL, auto.conf = NULL,
    verbose=FALSE, verbose.iter = 10, ...)

Value

An object of class bayesTFR.mcmc.set which is a list with two components:metaAn object of class bayesTFR.mcmc.meta.mcmc.listA list of objects of class bayesTFR.mcmc, one for each MCMC.

Rdversion

1.1

Details

The function run.tfr.mcmc creates an object of class bayesTFR.mcmc.meta and stores it in output.dir. It launches nr.chains MCMCs, either sequentially or in parallel. Parameter traces of each chain are stored as (possibly compressed) ASCII files in a subdirectory of output.dir, called mcx where x is the identifier of that chain. There is one file per parameter, named after the parameter with the suffix .txt, possibly followed by a compression suffix if compression.type is given. Country-specific parameters ($U, d, \gamma$) have the suffix _cy where y is the country code. In addition to the trace files, each mcx directory contains the object bayesTFR.mcmc in binary format. All chain-specific files are written into disk after the first, last and each buffer.size-th iteration. Using the function continue.tfr.mcmc one can continue simulating an existing MCMCs by iter iterations for either all or selected chains.

For simulation of the hyperparameters of the Bayesian hierarchical model, all countries are used that are included in the UN WPP file (UN2010), possibly complemented by the my.tfr.file, that have include_code (in the UN location file) equal to 2. The hyperparameters are used to simulate country-specific parameters, which is done for all countries with include_code equal 1 or 2. Note that users can overwrite the UN include code by including the column include_code into the the my.tfr.file. The following values are recognized: -1 (do not overwrite the UN WPP include code), 0 (ignore), 1 (include in prediction but not estimation), 2 (include in both, estimation and prediction). Thus, the set of countries included in the estimation and prediction can be fully user-specific.

References

L. Alkema, A. E. Raftery, P. Gerland, S. J. Clark, F. Pelletier, Buettner, T., Heilig, G.K. (2011). Probabilistic Projections of the Total Fertility Rate for All Countries. Demography, Vol. 48, 815-839. (A Working Paper version at http://www.csss.washington.edu/Papers [nr. 97])

See Also

get.tfr.mcmc, summary.bayesTFR.mcmc.set.

Examples

m <- run.tfr.mcmc(nr.chains=1, iter=5, verbose=TRUE)
summary(m)
m <- continue.tfr.mcmc(iter=5, verbose=TRUE)
summary(m)