The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

MortalityTables

The goal of MortalityTables is to provide generic base classes and functions to handle all kinds of actuarial actuarial mortality tables (period and cohort life tables). Cohort and static life tables are implemented, observed data can be used, and existing life tables can be blended or extrapolated to derive new tables.

Furthermore, plotting functions are provided for reports and publications.

Installation

You can install the development version of MortalityTables from GitHub with:

# install.packages("devtools")
devtools::install_github("kainhofer/MortalityTables")

About the package

The MortalityTables package provides the mortalityTable base class and some derived classes to handle different types of mortality tables (also called life tables), mainly used for life insurance. Additionally it provides a plot function to compare multiple life tables either directly using the absolute mortalities in log-linear plots or using relative mortalities as percentages of a given reference table.

Types of Life Tables

Provided types of mortality tables are:

Loading the MortalityTables package

library("MortalityTables")

Provided Data Sets

The package provides several real-life life tables published by census bureaus and actuarial associations around the world. You can use the function mortalityTables.list to list all available datasets (if no argument is given) or all datasets that match the given pattern (wildcard character is *). You can then use mortalityTables.load to load either one single data set or all datasets that match the pattern.

# list all datasets for Austria
mortalityTables.list("Austria_*")
#>  [1] "Austria_Annuities"                  "Austria_Annuities_AVOe1996R"       
#>  [3] "Austria_Annuities_AVOe2005R"        "Austria_Annuities_EROMF"           
#>  [5] "Austria_Annuities_RR67"             "Austria_Census"                    
#>  [7] "Austria_Endowments_ADSt2426_2Lives" "Austria_PopulationForecast"        
#>  [9] "Austria_PopulationMCMC"             "Austria_PopulationObserved"        
#> [11] "Austria_VUGesamtbestand_2012-16"

# Load the German annuity table DAV 2004-R
mortalityTables.load("Germany_Annuities_DAV2004R")

# Load all Austrian data sets
mortalityTables.load("Austria_*")
#> Lade nötiges Paket: MortalityLaws
#> Warning: Paket 'MortalityLaws' wurde unter R Version 4.2.3 erstellt

Cohort and Period Mortality Data

Cohort mortality vectors (for a given birth year) or period death probabilities (for a given observation year) can be extracted with the functions periodDeathProbabilities() and deathProbabilities():

mortalityTables.load("Austria_Annuities")
deathProbabilities(AVOe2005R.male, YOB = 1977, ages = 35:50)
#>  [1] 0.0006467352 0.0006741228 0.0007202125 0.0007820113 0.0008524437
#>  [6] 0.0009260103 0.0009794564 0.0010272832 0.0010731228 0.0011227093
#> [11] 0.0011784509 0.0012409740 0.0013080864 0.0013817843 0.0014633494
#> [16] 0.0015513107
deathProbabilities(AVOe2005R.male, YOB = 2023, ages = 35:50)
#>  [1] 0.0001941029 0.0002041420 0.0002200821 0.0002411616 0.0002653197
#>  [6] 0.0002909132 0.0003106056 0.0003288675 0.0003468281 0.0003663459
#> [11] 0.0003882554 0.0004128296 0.0004394048 0.0004687102 0.0005012581
#> [16] 0.0005366270

periodDeathProbabilities(AVOe2005R.male, Period = 2023, ages = 35:50)
#>  [1] 0.0004718782 0.0005066172 0.0005573996 0.0006231746 0.0006993210
#>  [6] 0.0007819197 0.0008511112 0.0009184673 0.0009869854 0.0010620137
#> [11] 0.0011462722 0.0012409740 0.0013445246 0.0014595263 0.0015880515
#> [16] 0.0017292761

If the mortality table is a cohort table, the trend is used to calculate the death probabilities for the given cohort or calendar year. If the table is a static life table, the period and cohort life tables will be identical. If the table is an observed table (i.e. observed death probabilities for each age and year), the data is extracted from the matrix’ rows/columns or diagonals. In all cases, the user does not have use different methods for different underlying tables.

Plotting and Comparing Mortality Data

There are two plotting functions using ggplot: plotMortalityTables() and plotMortalityTableComparisons() to plot the absolute and relative mortalities. For absolute mortalities, the q(x) axis employs a log10-scale. The returned plot is a normal ggplot2 object, so all features provided by ggplot2 can be adde to the plots.

mortalityTables.load("Austria_Annuities")
plotMortalityTables(AVOe2005R.male, AVOe2005R.male.unloaded, AVOe1996R.male, EROM.G1950.male, 
                    YOB = 1977, ages = 0:99, legend.position = c(0.5, 0.65))

plotMortalityTableComparisons(AVOe2005R.male, AVOe2005R.male.unloaded, AVOe1996R.male, EROM.G1950.male, 
                              YOB = 1977, ages = 0:99, legend.position = c(0.5, 0.65))

plotMortalityTrend(AVOe2005R.male, AVOe2005R.male.unloaded, AVOe1996R.male, AVOe1996R.male, EROM.G1950.male)

Further information

For further information on how to use the package, see the “Using the MortalityTables Package” vignette.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.