bayesPop (version 4.3-0)

pop.aggregate: Aggregation of Population Projections

Description

Aggregation of existing countries' population projections into projections of given regions, and accessing such aggregations.

Usage

pop.aggregate(pop.pred, regions, 
    method = c('independence', 'regional'), name = method,
    inputs = list(e0F.sim.dir = NULL, e0M.sim.dir = "joint_", tfr.sim.dir = NULL), 
    my.location.file = NULL, verbose = FALSE)
    
get.pop.aggregation(sim.dir = NULL, pop.pred = NULL, name = NULL)

Arguments

pop.pred

Object of class bayesPop.prediction containing country-specific population projections.

regions

Vector of numerical codes of regions. It should correspond to values in the column area_code in the UNlocations dataset or in my.location.file (see below).

method

There are two methods for aggregating projections, independence and regional, see Details.

name

Name of the aggregation. It becomes a part of a directory name where aggregation results are stored.

inputs

This argument is only used when the regional method is selected. It is a list of inputs of probabilistic components of the projection: [object Object],[object Object],[object Object]

my.location.file

User-defined location file that can contain other agreggation groups than the default UN location file. It should have the same structure as the UNlocations dataset, see below.

verbose

Logical switching log messages on and off.

sim.dir

Simulation directory where aggregation is stored. It is the same directory used for creating the pop.pred object. Alternatively, pop.pred can be used. Either sim.dir or pop.pred must be given.

Value

• Object of class bayesPop.prediction containing the aggregated results. In addition it contains elements aggregation.method giving the method used, and aggregated.countries which is a list of countries aggregated for each region.

Details

The dataset UNlocations or my.location.file is used to determine countries to be aggregated, in particular the field location_type of the entries corresponding to the codes given in the regions argument. One can aggregate over the following location types: Type 0 means aggregating all countries of the world, type 2 is aggregating over continents, type 3 is aggregating over regions within continents, and any other integer (except 4) correponds to user-defined aggregations. Note that type 4 is reserved as a location type of countries and thus, all aggregations are performed over entries of this type. For type 2, countries are matched using the area_code column; for type 3 the matching is done using the reg_code column of the UNlocations dataset. E.g., if regions=908 (Europe) which has location type 2 in the default UNlocations dataset, all countries are aggregated for which values of 908 are found in the area_code column. If the location type is other than 0, 2, 3 and 4, there must be a column in the file called agcode_$x$ with $x$ being the location type. This column is then used to match the countries to be aggregated. There are two methods available for generating aggregations of population projection: [object Object],[object Object] Aggregations can be performed for any area/region given in the UNlocations table. Their identifiers correspond to the column area_code. In case of the regional method, the same codes must be also included in predictions of life expectancy and TFR.

Results of the aggregations are stored in the same top directory as the pop.pred object, in a sudirectory called aggregations_name. They can be accessed using the function get.pop.aggregation. Note that multiple runs of this function with the same name will overwrite previous aggregations results of the same name.

See Also

pop.predict, tfr.predict.extra, e0.predict.extra

Examples

sim.dir <- tempfile()
pred <- pop.predict(countries=c(528,218,450), output.dir=sim.dir)
aggr <- pop.aggregate(pred, 900) # aggregating World (i.e. all countries available in pred)
pop.trajectories.plot(aggr, 900, sum.over.ages=TRUE)
# countries over which we aggregated:
UNlocations[is.element(UNlocations[,'country_code'], aggr$aggregated.countries[['900']]),]
unlink(sim.dir, recursive=TRUE)