write.projection.summary: Writing Projection Summary Files

In PPgp/bayesTFR: Bayesian Fertility Projection

Writing Projection Summary Files

Description

The function creates two files containing projection summaries, such as the median, mean, the lower and upper bound of the 80 and 90% probability intervals, respectively, the +/- 0.5 child variant and the constant variant. One file is in a user-friendly format, whereas the other is in a UN-specific format with internal coding of the time and the variants. In addition, a file containing some of the model parameters is created.

Usage

write.projection.summary(dir = file.path(getwd(), "bayesTFR.output"), 
    subdir = "predictions", output.dir = NULL, revision = NULL, adjusted = FALSE, 
    est.uncertainty = FALSE, ...)

Arguments

dir	Directory containing the prediction object. It should correspond to the output.dir argument of the tfr.predict function.
subdir	Subdirectory of dir containing the predictions.
output.dir	Directory in which the resulting file will be stored. If NULL the same directory is used as for the prediction.
revision	UN WPP revision number. If NULL it is determined from the corresponding WPP year: WPP 2008 corresponds to revision 13, every subsequent WPP increases the revision number by one. Used as a constant in the second file only.
adjusted	Logical. By default the function writes summary using the original BHM projections. If the projection medians are adjusted (using e.g. tfr.median.set), setting this argument to TRUE causes writing the adjusted projections.
est.uncertainty	Logical. If TRUE and the simulation was generated with uncertainty around estimation, that uncertainty info is included in the summaries.
...	Additional arguments passed to the underlying functions. Here, argument precision can be set to determine the number of significant digits (default is 4).

Details

The first file that the function creates is called ‘projection_summary_user_friendly.csv’ (or ‘projection_summary_user_friendly_adjusted.csv’ if adjusted=TRUE), it is a comma-separated table with the following columns:

country_name

country name

country_code

country code

variant

name of the variant, such as “median”, “lower 80”, “upper 80”, “lower 95”, “upper 95”, “mean”, “-0.5child”, “+0.5child”, “constant”

period1:

e.g. “2005-2010”: TFR for the first time period. If est.uncertainty is TRUE, the first time period is the first observed time period. Otherwise it is the last observed one.

period2:

e.g. “2010-2015”: TFR for the second time period

...

further columns with TFR projections

The second file, called ‘projection_summary.csv’ (or ‘projection_summary_adjusted.csv’ if adjusted=TRUE), also comma-separated table, contains the same information as above in a UN-specific format:

RevID

revision number, passed to the function as an argument

VarID

variant identifier, extracted from the UN_variants dataset

LocID

country code

TimeID

time identifier, extracted from the UN_time dataset

TFR

the total fertility rate for this variant, location and time period

The third comma-separated file, called ‘projection_summary_parameters.csv’ contains the following columns:

country_name

country name

country_code

country code

TF_time_start_decline

start period of TFR decline

TF_max

TFR at the onset of the fertitility transition (median of the U_c parameter)

TF_max_decrement

maximum decrement of TFR decline (median of the d_c parameter)

TF_end_level

median of the end level of the fertility transition (\Delta_{c4} parameter)

TF_end_level_low

2.5 percentile of the \Delta_{c4} distribution

TF_end_level_high

97.5 percentile of the \Delta_{c4} distribution

TF_time_end_decline

period of the end decline, measured using the prediction median

Note that this file is not created if adjusted=TRUE.

Note

This function is automatically called from the tfr.predict function, therefore in standard cases it will not be needed to call it directly.

Author(s)

Hana Sevcikova

See Also

convert.tfr.trajectories, tfr.predict

PPgp/bayesTFR documentation built on Nov. 5, 2024, 1:57 p.m.