bayesMig-package: Bayesian Projection of Migration

In bayesMig: Bayesian Projection of Migration

Bayesian Projection of Migration

Description

Collection of functions for making probabilistic projections of net migration rate for all countries of the world, using a Bayesian hierarchical model (BHM) and the United Nations demographic time series. The model can be also applied to user-defined data for other locations, such as subnational units. Methodological details are provided in Azose & Raftery (2015). The projected rates can be used as input to population projections generated via the bayesPop package.

Details

The package is implemented in a similar way as the bayesTFR package and thus, many functions have their equivalents in bayesTFR. The main functions of the package are:

• run.mig.mcmc: Runs a Markov Chain Monte Carlo (MCMC) simulation. It results in posterior samples of the model parameters.

• mig.predict: Using the posterior parameter samples, trajectories of future net migration rates are generated for all countries or given locations.

The following functions can be used to analyze results:

• mig.trajectories.plot: Shows the posterior trajectories for a given location, including the median and given probability intervals.

• mig.trajectories.table: Shows a tabular form of the posterior trajectories for a given location.

• mig.map, mig.ggmap and mig.map.gvis: Show a world map of migration rates for a given projection or observed period, or for country-specific parameter estimates.

• mig.partraces.plot and mig.partraces.cs.plot: Plot the MCMC traces of country-independent parameters and country-specific parameters, respectively.

• mig.pardensity.plot and mig.pardensity.cs.plot: Plot the posterior density of the country-independent parameters and country-specific parameters, respectively.

• summary.bayesMig.mcmc.set: Summary method for the MCMC results.

• summary.bayesMig.prediction: Summary method for the prediction results.

For MCMC diagnostics, function mig.coda.list.mcmc creates an object of type “mcmc.list” that can be used with the coda package. Furthermore, function mig.diagnose analyzes the MCMCs using the Raftery diagnostics implemented in the coda package and gives information about parameters that did not converge.

Existing results can be accessed using the get.mig.mcmc (estimation) and get.mig.prediction (prediction) functions. Existing convergence diagnostics can be accessed using the get.mig.convergence and get.mig.convergence.all functions.

Historical data on migration rates are taken from the wpp2019 (default), wpp2024 , wpp2022 or wpp2017 package, depending on users settings. Alternatively, users can input their own data. These can be either 5-year or annual time series. An example file with historical annual US migration rates is included in the package. Its usage is shown in the Example of mig.predict.

Note

As this package has been designed for simulating migration on a national level, many functions use arguments and terminology related to countries. However, a “country” is to be interpreted as any location included in the simulation.

Author(s)

Jon Azose, Hana Sevcikova and Adrian Raftery

Maintainer: Hana Sevcikova [e-mail]

References

Azose, J. J., & Raftery, A. E. (2015). Bayesian probabilistic projection of international migration. Demography, 52(5), 1627-1650. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s13524-015-0415-0")}.

Azose, J.J., Ševčíková, H., Raftery, A.E. (2016): Probabilistic population projections with migration uncertainty. Proceedings of the National Academy of Sciences 113:6460–6465. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.1606119113")}.

See Also

Useful links:

• http://bayespop.csss.washington.edu

Examples

## Not run: 
# Run a real simulation (can take long time)
sim.dir <- tempfile()
m <- run.mig.mcmc(nr.chains = 4, iter = 10000, thin = 10, output.dir = sim.dir,
        verbose.iter = 1000)

# Prediction for all countries 
# (use argument save.as.ascii for passing predictions into bayesPop)
pred <- mig.predict(sim.dir = sim.dir, nr.traj = 1000, burnin = 1000)

# Explore results
summary(pred, country = "Germany")
mig.trajectories.plot(pred, country = "Germany", nr.traj = 50)

# Check convergence diagnostics
mig.diagnose(sim.dir, burnin = 4000, thin = 1)

unlink(sim.dir, recursive = TRUE)
# For annual projections on sub-national level, see ?mig.predict.

## End(Not run)

bayesMig documentation built on April 3, 2025, 8:59 p.m.