Package 'MortalityTables'

Description

Classes to implement, analyze and plot cohort life tables for actuarial calculations. Birth-year dependent cohort mortality tables using a yearly trend to extrapolate from a base year are implemented, as well as period life table, cohort life tables using an age shift, and merged life tables. Additionally, several data sets from various countries are included to provide widely-used tables out of the box.

Author(s)

Maintainer: Reinhold Kainhofer [email protected] [copyright holder]

See Also

Useful links:

• https://gitlab.open-tools.net/R/r-mortality-tables

• Report bugs at https://gitlab.open-tools.net/R/r-mortality-tables/-/issues

Description

Return the defined ages of the life table

Usage

ages(object, ...)

## S4 method for signature 'mortalityTable.period'
ages(object, ...)

## S4 method for signature 'mortalityTable.mixed'
ages(object, ...)

## S4 method for signature 'mortalityTable.jointLives'
ages(object, ...)

## S4 method for signature 'mortalityTable.observed'
ages(object, ...)

Arguments

Methods (by class)

• ages(mortalityTable.period): Return the defined ages of the period life table

• ages(mortalityTable.mixed): Return the defined ages of the mixed life table

• ages(mortalityTable.jointLives): Return the defined ages of the joint lives mortality table (returns the ages of the first table used for joint lives)

• ages(mortalityTable.observed): Return the defined ages of the observed life table

Examples

mortalityTables.load("Austria_Annuities")
ages(AVOe2005R.male)
ages(AVOe1996R.male)

mortalityTables.load("Austria_Census")
ages(mort.AT.census.2011.male)

Description

Return the age shift of the age-shifted life table given the birth year

Usage

ageShift(object, YOB = 1975, ...)

## S4 method for signature 'mortalityTable'
ageShift(object, YOB = 1975, ...)

## S4 method for signature 'mortalityTable.ageShift'
ageShift(object, YOB = 1975, ...)

Arguments

Methods (by class)

• ageShift(mortalityTable): Age shifts apply only to mortalityTagle.ageShift, so all other tables return NA.

• ageShift(mortalityTable.ageShift): Return the age shift of the age-shifted life table given the birth year

Examples

mortalityTables.load("Austria_Annuities")
ageShift(AVOe2005R.male.av, YOB=1910)
ageShift(AVOe2005R.male.av, YOB=1955)
ageShift(AVOe2005R.male.av, YOB=2010)
# A table with trend does NOT have any age shift, so NA is returned:
ageShift(AVOe2005R.male, YOB=1910)

Description

Return the base table of the life table

Usage

baseTable(object, ...)

## S4 method for signature 'mortalityTable'
baseTable(object, ...)

## S4 method for signature 'mortalityTable.period'
baseTable(object, ...)

## S4 method for signature 'mortalityTable.jointLives'
baseTable(object, ...)

Arguments

Methods (by class)

• baseTable(mortalityTable): Return the base table of the life table

• baseTable(mortalityTable.period): Return the base table of the life table

• baseTable(mortalityTable.jointLives): Return the base table of the joint lives mortality table (returns the base table of the first table used for joint lives)

Examples

mortalityTables.load("Austria_Annuities")
baseTable(AVOe2005R.male)

Description

Return the base year of the life table

Usage

baseYear(object, ...)

## S4 method for signature 'mortalityTable'
baseYear(object, ...)

## S4 method for signature 'mortalityTable.mixed'
baseYear(object, ...)

## S4 method for signature 'mortalityTable.jointLives'
baseYear(object, ...)

Arguments

Methods (by class)

• baseYear(mortalityTable): Return the base year of the life table

• baseYear(mortalityTable.mixed): Return the base year of the life table

• baseYear(mortalityTable.jointLives): Return the base year of the life table

Examples

mortalityTables.load("Austria_Annuities")
baseYear(AVOe2005R.male)

Calculate the improvement factors for the given birth-year and the mortalityTable.improvementFactors object

Description

Calculate the improvement factors for the given birth-year and the mortalityTable.improvementFactors object

Usage

calculateImprovements(object, ...)

## S4 method for signature 'mortalityTable.improvementFactors'
calculateImprovements(object, ..., Period = NULL, YOB = 1982)

Arguments

Methods (by class)

• calculateImprovements(mortalityTable.improvementFactors): Calculate the total mortality improvement factors relative to the base year for the given birth-year and the mortalityTable.improvementFactors object

Examples

pensionTables.load("USA_PensionPlan_RP2014")
calculateImprovements(RP2014.male@qx, YOB = 2017)

Description

Calculate the commutation numbers for the given parameters, using the mortality table and an interest rate

Usage

commutationNumbers(object, ..., ages = NULL, i = 0.03)

## S4 method for signature 'mortalityTable'
commutationNumbers(object, ..., ages = NULL, i = 0.03)

## S4 method for signature 'numeric'
commutationNumbers(object, ages, i = 0.03)

## S4 method for signature 'pensionTable'
commutationNumbers(object, ..., ages = NULL, i = 0.03)

Arguments

Methods (by class)

• commutationNumbers(mortalityTable): Calculate the commutation numbers for the given parameters, using the mortality table and an interest rate

• commutationNumbers(numeric): Calculate the commutation numbers for the given death probabilities (passed as a numeric vector with argument name "object"), ages and an interest rate Return value is a list of data frames

• commutationNumbers(pensionTable): Calculate the commutation numbers for the given parameters, using the pension table and an interest rate Return value is a list of data frames

Examples

mortalityTables.load("Austria_Annuities")
commutationNumbers(AVOe2005R.male, i = 0.03, YOB = 1975)

Description

Return the (cohort) death probabilities of the life table given the birth year (if needed)

Usage

deathProbabilities(object, ..., ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.period'
deathProbabilities(object, ..., ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.ageShift'
deathProbabilities(object, ..., ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.trendProjection'
deathProbabilities(object, ..., ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.improvementFactors'
deathProbabilities(object, ..., ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.mixed'
deathProbabilities(object, ..., ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.jointLives'
deathProbabilities(object, ..., ageDifferences = c(), ages = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable.observed'
deathProbabilities(object, ..., ages = NULL, YOB = 1975)

Arguments

Methods (by class)

• deathProbabilities(mortalityTable.period): Return the (cohort) death probabilities of the life table given the birth year (if needed)

• deathProbabilities(mortalityTable.ageShift): Return the (cohort) death probabilities of the life table given the birth year (if needed)

• deathProbabilities(mortalityTable.trendProjection): Return the (cohort) death probabilities of the life table given the birth year (if needed)

• deathProbabilities(mortalityTable.improvementFactors): Return the (cohort) death probabilities of the life table given the birth year (if needed)

• deathProbabilities(mortalityTable.mixed): Return the (cohort) death probabilities of the life table given the birth year (if needed)

• deathProbabilities(mortalityTable.jointLives): Return the (cohort) death probabilities of the life table given the birth year (if needed)

• deathProbabilities(mortalityTable.observed): Return the (cohort) death probabilities of the life table given the birth year (if needed)

Examples

mortalityTables.load("Austria_Annuities")
deathProbabilities(AVOe2005R.male, YOB = 1975)
deathProbabilities(AVOe2005R.male, YOB = 2017)

mortalityTables.load("Germany_Census")
table.JL = mortalityTable.jointLives(
    name = "ADSt 24/26 auf verbundene Leben",
    table = mort.DE.census.1924.26.male
)
deathProbabilities(table.JL, YOB = 1977, ageDifferences = c(1, 5, -5, 16))
deathProbabilities(table.JL, YOB = 1977, ageDifferences = c(0))
deathProbabilities(table.JL, YOB = 1977, ageDifferences = c(1, 5, 16))

Return a matrix of the persons' individual death probabilities of a joint-life table (instance of mortalityTable.jointLives)

Description

Return a matrix of the persons' individual death probabilities of a joint-life table (instance of mortalityTable.jointLives)

Usage

deathProbabilitiesIndividual(tables, YOB, ageDifferences)

Arguments

Examples

mortalityTables.load("Germany_Census")
deathProbabilitiesIndividual(list(mort.DE.census.1924.26.male), 1977, c(0, 0))
deathProbabilitiesIndividual(list(mort.DE.census.1924.26.male), 1977, c(0, -5, 13))

Description

Fill the given probabilities with NA to match the desired age range.

Usage

fillAges(probs = c(), givenAges = c(), neededAges = NULL, fill = NA_real_)

Arguments

Examples

# Ages 20-70 have linearly increasing death probabilities. Fill with 0 for the whole age range 0-120
fillAges(probs = c(0:50/50), givenAges = 20:70, neededAges = 0:120, fill = 0)

Description

Fit an exponential function exp(-A*(x-x0)) to the last value (f(100) and f'(100) need to coincide):

Usage

fitExpExtrapolation(data, idx, up = TRUE, verbose = FALSE)

Arguments

Description

Generate a dataframe suitable to be passed to the mortalityTable.ageShift class.

Usage

generateAgeShift(initial = 0, YOBs = c(1900, 2100), step = -1)

Arguments

Examples

generateAgeShift(initial = 1, YOBs = c(1922, 1944, 1958, 1973, 1989, 2006, 2023, 2041, 2056))

Description

Return the cohort life table as a mortalityTable.period object

Usage

getCohortTable(object, YOB, ...)

## S4 method for signature 'mortalityTable'
getCohortTable(object, YOB, ...)

Arguments

Methods (by class)

• getCohortTable(mortalityTable): Return the cohort life table as a mortalityTable.period object

Examples

mortalityTables.load("Austria_Annuities")
tb75 = getCohortTable(AVOe2005R.male, YOB = 1975)
# The tb75 is a fixed table with no trend any more
plot(AVOe2005R.male, tb75, Period = 2017)

Description

Return the maximum age of the life table

Usage

getOmega(object)

## S4 method for signature 'mortalityTable.period'
getOmega(object)

## S4 method for signature 'mortalityTable.mixed'
getOmega(object)

## S4 method for signature 'mortalityTable.jointLives'
getOmega(object)

## S4 method for signature 'mortalityTable.observed'
getOmega(object)

Arguments

Methods (by class)

• getOmega(mortalityTable.period): Return the maximum age of the period life table

• getOmega(mortalityTable.mixed): Return the maximum age of the mixed life table

• getOmega(mortalityTable.jointLives): Return the maximum age of the joint lives mortality table (returns the maximum age of the first table used for joint lives, as the ages of the joint lives are now known to the function)

• getOmega(mortalityTable.observed): Return the maximum age of the life table

Examples

mortalityTables.load("Austria_Annuities")
getOmega(AVOe2005R.male)
getOmega(mortalityTable.deMoivre(omega = 100))

Description

Return the period life table as a mortalityTable.period object

Usage

getPeriodTable(object, Period, ...)

## S4 method for signature 'mortalityTable'
getPeriodTable(object, Period, ...)

Arguments

Methods (by class)

• getPeriodTable(mortalityTable): Return the period life table as a mortalityTable.period object

Examples

mortalityTables.load("Austria_Annuities")
tb17 = getPeriodTable(AVOe2005R.male, Period = 2017)
# The tb17 is a fixed table with no trend any more
plot(AVOe2005R.male, tb17, YOB = 1975)

Description

Return the lifetable object (package lifecontingencies) for the cohort life table

Usage

lifeTable(object, ...)

## S4 method for signature 'mortalityTable'
lifeTable(object, ...)

## S4 method for signature 'array'
lifeTable(object, ...)

## S4 method for signature 'list'
lifeTable(object, ...)

## S4 method for signature ''NULL''
lifeTable(object, ...)

Arguments

Methods (by class)

• lifeTable(mortalityTable): Return the lifetable object (package lifecontingencies) for the cohort life table

• lifeTable(array): Return the lifetable object (package lifecontingencies) from the mortalityTable objects stored in the array

• lifeTable(list): Return the lifetable object (package lifecontingencies) from the mortalityTable objects stored in the list

• lifeTable(`NULL`): Empty dummy function to handle unassigned variables

Examples

if (requireNamespace("lifecontingencies", quietly = TRUE)) {
library("lifecontingencies")
mortalityTables.load("Austria_Annuities")
lifeTable(AVOe2005R.male, YOB = 2017)
axn(lifeTable(AVOe2005R.male, YOB = 1975), x = 65, i = 0.03)
axn(lifeTable(AVOe2005R.male, YOB = 2017), x = 65, i = 0.03)
}

Description

It is not required to call this function manually, plotMortalityTables will automatically do it if object derived from class mortalityTable are passed.

Usage

makeQxDataFrame(..., YOB = 1972, Period = NA, reference = NULL)

Arguments

Examples

mortalityTables.load("Austria_Annuities")
makeQxDataFrame(AVOe2005R.male, AVOe2005R.female, YOB = 1975)

Description

Calculate relative mortalities for age bands and birth years

Usage

mortalityComparisonTable(table1, table2, years, ages, binsize = 5, ...)

Arguments

Examples

mortalityTables.load("Austria_Annuities")
# Compare mortality of Austrian male and female annuitants born 1930 to 2030
mortalityComparisonTable(
    AVOe2005R.male, AVOe2005R.female,
    years = seq(1930, 2030, by = 10),
    ages = 0:119)

# Compare the two Austrian male annuity tables AVOe 2005-R and AVOe 1996-R,
# combining ages 10-19, 20-29, etc.
mortalityComparisonTable(
    AVOe2005R.male, AVOe1996R.male,
    years = seq(1930, 2030, by = 10),
    ages = 0:109, binsize=10)

Description

Return the mortality trend (yearly log-death-probability improvement) of the given period or the given generation.

Usage

mortalityImprovement(object, ..., Period = NULL, YOB = 1975)

## S4 method for signature 'mortalityTable'
mortalityImprovement(object, ..., Period = NULL, YOB = 1975)

Arguments

Methods (by class)

• mortalityImprovement(mortalityTable): Return the yearly log-mortality improvement of the life table given the birth or observation year

Examples

mortalityTables.load("Austria_Annuities")
# AVOe 2005R includes a trend decline by default, compare the exact table
# with the table without decline:
mortalityImprovement(AVOe2005R.male, Period = 2017)
mortalityImprovement(AVOe2005R.male.nodamping, Period = 2017)

Description

Class mortalityTable is the (virtual) base class for all mortality tables. It contains the name and some general values applying to all types of tables, but does not contain any data itself. Use a child class to create actual mortality tables.

Slots

name

The human-readable name of the mortality table

baseYear

The base year of the mortality table (e.g. for tables with trend projection)

modification

A function that will be called with the final death probabilities to give the user a way to modify the final probabilities

loading

Additional security loading on the resulting table (single numeric value, e.g. 0.05 adds 5% security margin to the probabilities)

data

Placeholder list to make it possible to store any kind of data associated with the object inside the object (e.g. the underlying data used to derive the death probabilities, parameters for adjustment, etc.)

Description

A cohort life table, obtained by age-shifting from a given base table (death probabilities

Slots

ageShifts

A data.frame with columns YOB and shifts giving the age shifts for each birth year

Examples

mortalityTables.load("Austria_Annuities_AVOe2005R")
tb = mortalityTable.ageShift(
    ages = ages(AVOe2005R.male),
    deathProbs = deathProbabilities(AVOe2005R.male, YOB = 1992),
    ageShifts = generateAgeShift(1, c(1962, 1985, 2000, 2015, 2040, 2070)))
# The cohort tables for different birth years are just the base probabilities with modified ages
plot(getCohortTable(tb, YOB = 1963), getCohortTable(tb, YOB = 2017))

Description

A period life table with maximum age omega dn the time of death identically distributed on the interval [0, omega]. The only required slot is the maximum age omega, all probabilities are calculated from it. Optionally, a name and loading can be passed (inherited from mortalityTable).

Slots

omega

Maximum age

Examples

mm = mortalityTable.deMoivre(100)
plot(mm,
     mortalityTable.deMoivre(90),
     mortalityTable.deMoivre(50))

Description

A cohort life table, obtained by an improvment factor projection from a given base table (PODs for a given observation year).

Slots

baseYear

The base year for the improvements (baseTable describes the death probabilities in this year)

improvement

Yearly improvement factors per age

Examples

mortalityTables.load("Austria_Annuities_AVOe2005R")
# AVOe 2005R base table with yearly improvements of 3% for age 0, linearly
# decreasing to 0% for age 120.
tb = mortalityTable.improvementFactors(
    ages = ages(AVOe2005R.male),
    deathProbs = periodDeathProbabilities(AVOe2005R.male, Period = 2002),
    baseYear = 2002,
    improvement = 0.03 * (1 - ages(AVOe2005R.male)/121),
    name = "AVOe 2005R base with linearly falling improvements (DEMO)"
)
# Yearly trend is declining:
plotMortalityTrend(tb, AVOe2005R.male, Period = 2017, title = "Mortality Trend")
# The cohort tables for different birth years:
plot(getCohortTable(tb, YOB = 1963), getCohortTable(tb, YOB = 2017))

Description

A cohort life table obtained by calculating joint death probabilities for multiple lives, each possibly using a different mortality table.

Slots

table

The mortalityTable object for all lives (vector if different tables should be used for the different persons)

Examples

mortalityTables.load("Germany_Census")
table.JL = mortalityTable.jointLives(
    name = "ADSt 24/26 auf verbundene Leben",
    table = mort.DE.census.1924.26.male
)
deathProbabilities(table.JL, YOB = 1977, ageDifferences = c(1, 5, -5, 16))
deathProbabilities(table.JL, YOB = 1977, ageDifferences = c(0))
deathProbabilities(table.JL, YOB = 1977, ageDifferences = c(1, 5, 16))

Description

A period life table following Makeham and Gompertz's law of a mortality rate $\mu$ increasing exponentially with age $x$ ($\mu_{x+t} = A + B c^{(x+t)}$). The only required slots are the parameters $A$, $B$ and $c$, all probabilities are calculated from them, for technical reasons a maximum age of 120 is technically assumed. Optionally, a name and loading can be passed (inherited from mortalityTable).

Slots

A

Parameter A of the Makeham-Gompertz distribution

B

Parameter B of the Makeham-Gompertz distribution

c

Parameter c of the Makeham-Gompertz distribution

omega

Maximum age (default: 150)

Examples

# A Gompertz mortality, roughtly approximating the Austrian annuitants 2017
gmp = mortalityTable.MakehamGompertz(A = 0, B = 0.00001, c = 1.11)
mortalityTables.load("Austria_Annuities_AVOe2005R")
plot(gmp, AVOe2005R.male, Period=2017)

# A Makeham-Gompertz mortality, approximating the Austrian annuitants 2017
mg = mortalityTable.MakehamGompertz(A = 0.0002, B = 0.00001, c = 1.11)
plot(mg, gmp, AVOe2005R.male, Period=2017)

Description

A cohort life table obtained by mixing two life tables with the given weights. Typically, when only gender-specific tables are available, unisex tables are generated by mixing the two gender-specific tables for males and for females with a pre-defined, constant proportion (e.g. 60:30 or 40:60, depending on the portfolio and on the security margins).

Slots

table1

The first mortalityTable

table2

The second mortalityTable

weight1

The weight of the first mortality table

weight2

The weight of the second mortality table

loading

Additional security loading

Examples

mortalityTables.load("Austria_Annuities_AVOe2005R")
# Generate a unisex table with mixing relation 60:40 from male + female tables
AVOe2005R.myUnisex = mortalityTable.mixed(
    table1 = AVOe2005R.male, table2 = AVOe2005R.female,
    weight1 = 0.6, weight2 = 0.4,
    name = "My custom AVOe 2005R unisex (60:40)")
plot(AVOe2005R.myUnisex, AVOe2005R.male, AVOe2005R.female, Period = 2017)

Description

Empty mortality table indicating NA

Usage

mortalityTable.NA

Format

An object of class mortalityTable.period of length 1.

Description

A cohort life table described by actual observations (data frame of PODs per year and age)

Slots

deathProbs

The observed death probabilities (age-specific probability of dying within one year)

years

The observation years

ages

The observation ages

Description

Generate a (deterministic) mortality table with only one probability set to 1 (for the given age)

Usage

mortalityTable.once(
  transitionAge,
  name = "Deterministic mortality table",
  ages = 0:99
)

Arguments

Description

Generate a (deterministic) mortality table with all probabilities starting at a given age set to 1

Usage

mortalityTable.onceAndFuture(
  transitionAge,
  name = "Deterministic mortality table",
  ages = 0:99
)

Arguments

Description

A period life table, giving death probabilities for each age, up to maximum age omega. The baseYear slot can be used to hold information about the period.

Slots

ages

The ages corresponding to the entries of the deathProbs

deathProbs

The one-year death probabilities for the ages

exposures

(Optional) exposured used to determine death probabilities (can be used as weights for smoothing, for variances, etc.)

Examples

linTable = mortalityTable.period(name="linear mortality", ages = 0:50, deathProbs = 0:50/50)
constTable = mortalityTable.period(name="const. mortality", ages = 0:50,
                                   deathProbs = c(rep(0.1, 50), 1))
plot(linTable, constTable, title="Comparison of linear and constand death probabilities")

# A sample observation table with exposures and raw probabilities
obsTable = mortalityTable.period(
    name = "trivial observed table",
    ages = 0:15,
    deathProbs = c(
        0.0072, 0.00212, 0.00081, 0.0005, 0.0013,
        0.001, 0.00122, 0.00142, 0.007, 0.0043,
        0.0058, 0.0067, 0.0082, 0.0091, 0.0075, 0.01),
    exposures = c(
        150, 222, 350, 362, 542,
        682, 1022, 1053, 1103, 1037,
        968, 736, 822, 701, 653, 438))
plot(obsTable, title = "Observed death probabilities")

Description

A cohort mortality table, obtained by a trend projection from a given base table (PODs for a given observation year). Typically, the trend is obtained by the Lee-Carter method or some other trend estimation. The dampingFunction can be used to modify the cumulative years (e.g. G(tau+x) instead of tau+x) If trend2 is given, the G(tau+x) gives the weight of the first trend, 1-G(tau+x) the weight of the second trend

Slots

baseYear

The base year of the trend projection (baseTable describes the death probabilities in this year)

trend

The yearly improvements of the log-death probabilities (per age)

dampingFunction

A possible damping of the trend. This is a function damping(delta_years) that gets a vector of years from the baseYear and should return the dampened values.

trend2

The alternate trend. If given, the damping function interpolates between trend and trend2, otherwise the dumping function simply modifies the coefficients of trend.

Examples

obsTable = mortalityTable.trendProjection(
    name = "Const. table with trend",
    baseYear = 2018,
    ages = 0:15,
    deathProbs = rep(0.02, 16),
    trend = c(
         0.045, 0.04, 0.03, 0.04, 0.042, 0.041, 0.038, 0.035,
         0.032, 0.031, 0.028, 0.020, 0.015, 0.01, 0.005, 0))
# In 2018 the flat mortality can be seen
plotMortalityTables(obsTable, Period = 2018, title = "Period death probabilities 2018")
# In 2038, the age-specific trend affected the probabilities differently for 20 years:
plotMortalityTables(obsTable, Period = 2038, title = "Period death probabilities 2038")
# Consequently, a person born 2018 will also not have constand death probabilities
plotMortalityTables(obsTable, YOB = 2018, title = "Cohort death probabilities, YOB 2018")

plotMortalityTables(
    lapply(2018:2033, function(y) getCohortTable(obsTable, YOB = y)),
    title = "Cohort tables for different YOBs", legend.position = c(0.99, 0.01))
plotMortalityTables(
   lapply(2018:2033, function(y) getPeriodTable(obsTable, Period = y)),
   title = "Period tables for different years", legend.position = c(0.99, 0.01))

Description

A period life table following Weibulls's law of a mortality rate $\mu$ increasing as a power of $t$:

$\mu_{x+t} = k * (x+t)^n$

The only required slots are the parameters $k>0$ and $n>0$, all probabilities are calculated from them, for technical reasons a maximum age of 150 is technically assumed. Optionally, a name and loading can be passed (inherited from mortalityTable).

Slots

k

Parameter k of the Weibull distribution

n

Parameter n of the Weibull distribution

omega

Maximum age (default: 120)

Examples

# A Weibull mortality
wbl = mortalityTable.Weibull(k = 0.0000000001, n = 4.8)
mortalityTables.load("Austria_Annuities_AVOe2005R")
plot(wbl, AVOe2005R.male, Period=2017, ylim = c(0.00005, 1))

Description

Generate a mortality table with all probabilities set to zero.

Usage

mortalityTable.zeroes(name = "Zero mortality table", ages = 0:99)

Arguments

List all available sets of life tables provided by the MortalityTables-package package An existing life table can then be loaded with mortalityTables.load.

Description

List all available sets of life tables provided by the MortalityTables-package package An existing life table can then be loaded with mortalityTables.load.

Usage

mortalityTables.list(
  pattern = "*",
  package = c("^MortalityTables", "^PensionTables"),
  prefix = "MortalityTables"
)

Arguments

Examples

mortalityTables.list()
mortalityTables.list("Austria_*")
mortalityTables.list("*Annuities")
mortalityTables.list(package = c("MyCustomPackage"))

Load a named set of mortality tables provided by the MortalityTables package

Description

Load a named set of mortality tables provided by the MortalityTables package

Usage

mortalityTables.load(
  dataset,
  package = c("^MortalityTables", "^PensionTables"),
  prefix = "MortalityTables"
)

Arguments

Examples

mortalityTables.list()
mortalityTables.load("Austria_Annuities_*")
mortalityTables.load("Austria_Annuities_AVOe2005R")
mortalityTables.load("*Annuities")
## Not run: mortalityTables.load("MyCustomTable", package = c("MyCustomPackage"))

Description

The function mt.cleanup removes all non-essential data from a given mortalityTable object.

Usage

mT.cleanup(object)

## S4 method for signature 'mortalityTable'
mT.cleanup(object)

## S4 method for signature 'mortalityTable.period'
mT.cleanup(object)

## S4 method for signature 'mortalityTable.trendProjection'
mT.cleanup(object)

## S4 method for signature 'array'
mT.cleanup(object)

## S4 method for signature 'list'
mT.cleanup(object)

## S4 method for signature 'pensionTable'
mT.cleanup(object)

## S4 method for signature 'mortalityTable.observed'
mT.cleanup(object)

Arguments

Details

Mortality tables are often generated from raw data, that is smoothed, extrapolated, etc. The mortalityTable class and its implementations can internally store the raw probabilities and the intermediate results and parameters. This method removes those information. All essential information (base table, ages, trend functions, etc.) are preserved.

Removed information includes: * all elements of the object@data list, except for dim * exposures * names of named age, deathProbs and trend vectors

For mortality tables with other mortalityTable components (like pension tables or mixed tables), all components are cleaned.

Methods (by class)

• mT.cleanup(mortalityTable): Clean up and remove all non-essential data from the mortalityTable object

• mT.cleanup(mortalityTable.period): Clean up and remove all non-essential data from the mortalityTable.period object

• mT.cleanup(mortalityTable.trendProjection): Clean up and remove all non-essential data from the mortalityTable.trendProjection object

• mT.cleanup(array): Clean up and remove all non-essential data from the mortalityTable objects stored in the array

• mT.cleanup(list): Clean up and remove all non-essential data from the mortalityTable objects stored in the list

• mT.cleanup(pensionTable): Clean up and remove all non-essential data from the mortalityTable objects stored in the array

• mT.cleanup(mortalityTable.observed): Clean up the internal data of the mortality table

Examples

mortalityTables.load("Austria_Census")
# Whittaker-Henderson smoothing stores the raw input and the weights in the
# \code{data} slot of the table:
AT.smoothed = whittaker.mortalityTable(mort.AT.census.2011.male)
AT.smoothed@data$rawProbs
AT.smoothed@data$whittaker

# cleaning up the table removes those non-essential information again:
AT.smoothed.clean = mT.cleanup(AT.smoothed)
AT.smoothed.clean@data$rawProbs
AT.smoothed.clean@data$whittaker

Description

Extrapolate the base table of a mortalityTable object using an exponential function (i.e. the death probabilities decreases towards 0 exponentially). While death probabilities trending towards 0 for old ages is not realistic for overall deaths, it can be useful to model causes of death that vanish in older age. It is, however, most useful to extrapolate an observed base table to low ages (e.g. for an insurance portfolio with practically no persons aged below 16). A decline towards 0 for low ages makes sense in this case.

Usage

mT.extrapolateProbsExp(table, age, up = TRUE)

Arguments

Details

The function needs only one age, from which the extrapolation using an exponential function is applied. the strength of the exponential function is derived from the death probability at that age.

Examples

mortalityTables.load("Austria_Census")
# use the Austrian population mortalities for ages 18-95 and exponentially
# extrapolate them to lower ages
AT11.lowAgesExp = mT.extrapolateProbsExp(mort.AT.census.2011.male, 18, up = FALSE)
plotMortalityTables(mT.setName(AT11.lowAgesExp, "Ages below 16 are extrapolated exponentially"),
                    mort.AT.census.2011.male)

Description

Extrapolate a mortality trend in a mortalityTable object using an exponential function (i.e. the trend decreases towards 0 exponentially). This is mainly used to extrapolate an observed age-specific trend to very old ages. Existing trend function values above (or below, respectively) the idx are overwritten.

Usage

mT.extrapolateTrendExp(table, idx, up = TRUE)

Arguments

Examples

mortalityTables.load("Austria_Annuities_AVOe2005R")
# extrapolate the trend exponentially from age 95 instead (overwriting the existing trend)
avoe2005exp = mT.extrapolateTrendExp(AVOe2005R.male, 95)
plotMortalityTrend(mT.setName(avoe2005exp, "AVÖ 2005R with trend extrapolated from age 85 up"),
                   AVOe2005R.male, Period = 2020, ages = 60:120)

Description

Restrict the given mortalityTable object(s) to given ages, potentially filling with NA values to ensure they cover the full desired age range

Usage

mT.fillAges(table, neededAges, fill = 0)

Arguments

Examples

mortalityTables.load("Austria_Annuities")
# return a table with only ages 100-130, where ages above 120 (not defined
# in the original table) are filled with qx=1:
mT.fillAges(AVOe2005R.male, neededAges = 100:130, fill = 1)

Description

Fit an extrapolation law (from the MortalityLaws Package to the base table of the mortality table and use it for extrapolation.

Usage

mT.fitExtrapolationLaw(
  table,
  method = "LF2",
  law = "HP",
  fit = 75:99,
  extrapolate = 80:120,
  fadeIn = 80:90,
  fadeOut = NULL,
  raw = NULL
)

Arguments

Details

The fit is done using the MortalityLaws::MortalityLaw function, with the ages, death counts, exposures and death rates taken from the table mortality table object. The law and the fitting method can be given in the mT.fitExtrapolationLaw with the law and the fitting method

The age range fit is used to fit the law, while extrapolation is applied only to ages given in parameter extrapolate. As fitting does usually not result a smooth transition, a linear fade in or fade out range can also be provided.

Examples

mortalityTables.load("Austria_Census")
# use Austrian population mortalities for ages 18-95 and exponentially
# extrapolate them to lower ages
AT11.lowAges = mT.fitExtrapolationLaw(mort.AT.census.2011.male, law = "opperman",
                                      fit = 5:15, extrapolate = 0:15,
                                      fadeIn = NULL, fadeOut = 5:15)
AT11.oldAges = mT.fitExtrapolationLaw(mort.AT.census.2011.male, law = "HP",
                                      fit = 75:90, extrapolate = 75:120)
plotMortalityTables(mT.setName(AT11.lowAges, "Low ages fitted (ages 5-15 used)"),
                    mT.setName(AT11.oldAges, "old ages fitted (ages 75-90 used)"),
                    mort.AT.census.2011.male)

Description

The function mt.round rounds all components (base table, potentially also trend functions or yearly improvement factors) to the given number of numerical digits. For pensionTable objects, the function is applied to all components

Usage

mT.round(object, digits = 8)

## S4 method for signature 'mortalityTable'
mT.round(object, digits = 8)

## S4 method for signature 'mortalityTable.period'
mT.round(object, digits = 8)

## S4 method for signature 'mortalityTable.trendProjection'
mT.round(object, digits = 8)

## S4 method for signature 'mortalityTable.improvementFactors'
mT.round(object, digits = 8)

## S4 method for signature 'array'
mT.round(object, digits = 8)

## S4 method for signature 'list'
mT.round(object, digits = 8)

## S4 method for signature 'pensionTable'
mT.round(object, digits = 8)

## S4 method for signature 'mortalityTable.observed'
mT.round(object, digits = 8)

Arguments

Methods (by class)

• mT.round(mortalityTable): Round the given mortalityTable to a given number of digits

• mT.round(mortalityTable.period): Round the given period mortality table to a given number of digits (base table and loadings)

• mT.round(mortalityTable.trendProjection): Round the given mortalityTable with trend projection to a given number of digits (base table, loadings and trend(s))

• mT.round(mortalityTable.improvementFactors): Round the given mortalityTable with improvement factors to a given number of digits (base table, loadings and improvement factors)

• mT.round(array): Round the mortalityTables stored in an array to a given number of digits

• mT.round(list): Round the mortalityTables stored in a list to a given number of digits

• mT.round(pensionTable): Round all components of a pensionTable to a given number of digits

• mT.round(mortalityTable.observed): Return the life table with the values rounded to the given number of digits

Examples

mortalityTables.load("Austria_Census")
AT.rounded1 = mT.round(mort.AT.census.2011.male, 1)
AT.rounded2 = mT.round(mort.AT.census.2011.male, 2)
AT.rounded3 = mT.round(mort.AT.census.2011.male, 3)
plotMortalityTables(mort.AT.census.2001.male,
                    mT.setName(AT.rounded1, "rounded to 1 digit"),
                    mT.setName(AT.rounded3, "rounded to 3 digits"))

Description

Scale all probabilities of the given mortalityTable object(s) by the given factor

Usage

mT.scaleProbs(table, factor = 1, name.postfix = "scaled", name = NULL)

Arguments

Examples

mortalityTables.load("Austria_Annuities")
mT.scaleProbs(AVOe2005R.male, 1.5) # Add 50% to all death probabilities of the table

Description

A mortalityTable can store additional information to be used e.g. as additional dimensions in ggplot calls. Typically, these information include sex, base population, observation year, type of data (raw, smoothed), country, type of risk, etc. These additional dimensions are stored in the tbl@data list and will be used by plotMortalityTables and similar functions.

Usage

mT.setDimInfo(tbl, ..., append = TRUE)

Arguments

Examples

mortalityTables.load("Austria_Census")
mortalityTables.load("Austria_Annuities")
# The annuity tables use the population mortality as starting point. Set either
# population or anuuitants as dimensional info and use that dimension in a ggplot
# Most pre-defined tables already have the most important dimensions (like sex) stored.
at01.m = mT.setDimInfo(mort.AT.census.2001.male, population = "Population")
at01.f = mT.setDimInfo(mort.AT.census.2001.female, population = "Population")
av05r.m = mT.setDimInfo(AVOe2005R.male, population = "Annuitants")
plotMortalityTables(at01.m, at01.f, av05r.m) + aes(linetype = population, color = sex)

Description

Sets a new name for the given mortality table or the list/table/array of mortalityTables

Usage

mT.setName(table, name)

Arguments

Examples

mortalityTables.load("Austria_Annuities")
mT.setName(AVOe2005R.male, name = "Austrian male Annuity table 2005-R")

Description

Set/Add a trend vector for the probabilities of the given mortalityTable object(s). Returns a mortalityTable.trendProjection object

Usage

mT.setTrend(
  table,
  trend,
  trendages = NULL,
  baseYear = NULL,
  dampingFunction = identity
)

mT.addTrend(
  table,
  trend,
  trendages = NULL,
  baseYear = NULL,
  dampingFunction = identity
)

Arguments

Functions

• mT.addTrend(): Add a trend to the mortality table (returns a mortalityTable.trendProjection obect)

Description

This function modifies a mortalityTable by switching moralities at a given age to the mortalities of a second table.

Usage

mT.switchover(table, to, at, weights = NULL)

Arguments

Details

This function mT.switchover modifies the given mortalityTable and replaces the mortalities starting from a given age by the mortalities of a second table. By default, the transition from the original table to the secondary table is a simple 0/1-switch at the given age at. This is done internally by using weights= (age >= at).

By giving custom weights, one can also implement a smooth transition to the secondary table. The weights are used as simple factors of a linear combination of the two tables.

Examples

mortalityTables.load("Austria_Census")
mort.AT.switchover = mT.switchover(mort.AT.census.2011.male, mort.AT.census.2011.female, 60)
plotMortalityTables(mort.AT.census.2011.male,
                    mT.setName(mort.AT.switchover, "Switched to female at age 60"))

# A smooth switchover is possible with custom weights
mort.AT.switchover.smooth = mT.switchover(mort.AT.census.2011.male, mort.AT.census.2011.female,
    weights = c(rep(0, 55), 0:20/20, rep(1, 25)))
plotMortalityTables(mort.AT.census.2011.male,
    mT.setName(mort.AT.switchover.smooth, "Switched to female smoothly at ages 55-75"))

Description

Translate the base table of a cohort life table to a different observation period, using the existing base table and the trend functions. This only has an effect on cohort life tables (e.g. objects of class mortalityTable.trendProjection). For all other life tables (period life tables, observed, etc.), this function has no effect.

Usage

mT.translate(table, baseYear, name = NULL)

Arguments

Details

This function also does not modify the resulting death probabilities of the life table object, it just reparameterizes the internal representation of a life table with trend projection factors.

This functionality is often needed when publisheing life thables. Typically, the table is derived from a certain observation period, so the resulting base table describes the middle of the observation period. Projetion into the future is then done using trend projection factors starting from that base table. On the other hand, for the published table it is often desired to tabulate not the middle of the observation period, but rather the current year as base year for the extrapolation. For the resulting period or cohort death probabilities, it is irrelevant, which base year is used, as long as the shift to another base year (which includes translating the base mortalities of the base year) is done consistenly with the trend functions. The function mT.translate ensures this.

Examples

mortalityTables.load("Austria_Annuities_AVOe2005R")
# The AVOe2005R.male.nodamping has 2001 as the base year. Move its base year
# to 2020 without modifying cohort probabilities
avoe05r.shifted = mT.translate(AVOe2005R.male.nodamping, 2020, "AVÖ 2005-R, translated to 2020")
plotMortalityTables(
    getPeriodTable(AVOe2005R.male.nodamping),
    getPeriodTable(avoe05r.shifted),
    title = "Base tables of the AVÖ 2005R a translated version to 2020")
# Even though the base tables are shifted, the resulting probabilities are
# unchanged (except for numeric artefacts)
abs(periodDeathProbabilities(AVOe2005R.male.nodamping, Period = 2050) -
    periodDeathProbabilities(avoe05r.shifted, Period = 2050)) < 0.00000001
abs(deathProbabilities(AVOe2005R.male.nodamping, YOB = 2050) -
    deathProbabilities(avoe05r.shifted, YOB = 2050)) < 0.00000001

Description

Class pensionTable is the (virtual) base class for all pensions tables. It contains the name and some general values applying to all types of tables. In particular, it holds individual tables for each of the transition probabilities. Possible states are:

• active: healty, no pension, typically paying some kin of premium

• incapacity: disablity pension, in most cases permanent, not working, early pension

• retirement: old age pension, usually starting with a fixed age

• dead

  • Widow/widower pension

Correspondingly, the following transition probabilities can be given:

qxaa

death probability of actives (active -> dead)

ix

invalidity probability (active -> incapacity)

qix

death probability of invalid (invalid -> dead)

rx

reactivation probability (incapacity -> active)

apx

retirement probability (active -> retirement), typically 1 for a fixed age

qpx

death probability of retired (retired -> dead)

hx

probability of a widow at moment of death (dead -> widow), y(x) age difference

qxw

death probability of widows/widowers

qgx

death probability of total group (irrespective of state)

invalids.retire

Flag to indicate whether invalid persons retire like active (one death probability for all retirees) or whether they stay invalid until death with death probabilities specific to invalids.

Slots

qx

Death probability table of actives (derived from mortalityTable)

ix

Invalidity probability of actives (derived from mortalityTable)

qix

Death probability table of invalids (derived from mortalityTable)

rx

Reactivation probability of invalids (derived from mortalityTable)

apx

Retirement probability of actives (derived from mortalityTable)

qpx

Death probability of old age pensioners (derived from mortalityTable)

hx

Probability of a widow at the moment of death (derived from mortalityTable)

qwy

Death probability of widow(er)s (derived from mortality Table)

yx

Age difference of the widow to the deceased

qgx

Death probability of whole group (derived from mortalityTable), irrespective of state

invalids.retire

Whether invalids retire like actives or stay invalid until death

probs.arrange

A function that takes the individual transition probabilities of all the components and creates one object (a data.frame or a list) that will be returned by the method transitionProbabilities. The default arranges all tables without further modification. However, some pension tables (like the german Heubeck table) require the total mortality to be recalculated from the individual mortalities of actives and disabled. In this case, the function assigned to this slot will also calculate that total probability.

List all available sets of pension tables provided by the MortalityTables-package package An existing pension table can then be loaded with pensionTables.load.

Description

List all available sets of pension tables provided by the MortalityTables-package package An existing pension table can then be loaded with pensionTables.load.

Usage

pensionTables.list(
  pattern = "*",
  package = c("^MortalityTables", "^PensionTables")
)

Arguments

Examples

pensionTables.list()
pensionTables.list("USA_*")
pensionTables.list(package = c("MyCustomPackage"))

Load a named set of pension tables provided by the MortalityTables package

Description

Load a named set of pension tables provided by the MortalityTables package

Usage

pensionTables.load(dataset, package = c("^MortalityTables", "^PensionTables"))

Arguments

Description

Return the (period) death probabilities of the life table for a given observation year

Usage

periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

## S4 method for signature 'mortalityTable.period'
periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

## S4 method for signature 'mortalityTable.ageShift'
periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

## S4 method for signature 'mortalityTable.trendProjection'
periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

## S4 method for signature 'mortalityTable.improvementFactors'
periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

## S4 method for signature 'mortalityTable.mixed'
periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

## S4 method for signature 'mortalityTable.jointLives'
periodDeathProbabilities(
  object,
  ...,
  ageDifferences = c(),
  ages = NULL,
  Period = 1975
)

## S4 method for signature 'mortalityTable.observed'
periodDeathProbabilities(object, ..., ages = NULL, Period = 1975)

Arguments

Methods (by class)

• periodDeathProbabilities(mortalityTable.period): Return the (period) death probabilities of the life table for a given observation year

• periodDeathProbabilities(mortalityTable.ageShift): Return the (period) death probabilities of the life table for a given observation year

• periodDeathProbabilities(mortalityTable.trendProjection): Return the (period) death probabilities of the life table for a given observation year

• periodDeathProbabilities(mortalityTable.improvementFactors): Return the (period) death probabilities of the life table for a given observation year

• periodDeathProbabilities(mortalityTable.mixed): Return the (period) death probabilities of the life table for a given observation year

• periodDeathProbabilities(mortalityTable.jointLives): Return the (period) death probabilities of the joint lives mortality table for a given observation year

• periodDeathProbabilities(mortalityTable.observed): Return the (period) death probabilities of the life table for a given observation year If the observed mortality table does not provide data for the desired period, the period closest to the 'Period' argument will be used and a warning printed.

Examples

mortalityTables.load("Austria_Annuities")
periodDeathProbabilities(AVOe2005R.male, Period = 1975)
periodDeathProbabilities(AVOe2005R.male, Period = 2017)

mortalityTables.load("Germany_Census")
table.JL = mortalityTable.jointLives(
    name = "ADSt 24/26 auf verbundene Leben",
    table = mort.DE.census.1924.26.male
)
periodDeathProbabilities(table.JL, Period = 2017, ageDifferences = c(1, 5, -5, 16))
periodDeathProbabilities(table.JL, Period = 2017, ageDifferences = c(0))
periodDeathProbabilities(table.JL, Period = 2017, ageDifferences = c(1, 5, 16))

Return a matrix of the persons' individual period death probabilities of a joint-life table (instance of mortalityTable.jointLives)

Description

Return a matrix of the persons' individual period death probabilities of a joint-life table (instance of mortalityTable.jointLives)

Usage

periodDeathProbabilitiesIndividual(tables, period, ageDifferences)

Arguments

Examples

mortalityTables.load("Germany_Census")
periodDeathProbabilitiesIndividual(list(mort.DE.census.1924.26.male), 1977, c(0, 0))
periodDeathProbabilitiesIndividual(list(mort.DE.census.1924.26.male), 1977, c(0, -5, 13))

Description

Return all period transition probabilities of the pension table

Usage

periodTransitionProbabilities(object, ...)

## S4 method for signature 'pensionTable'
periodTransitionProbabilities(
  object,
  Period = 2017,
  ...,
  ages = NULL,
  OverallMortality = FALSE,
  retirement = NULL,
  invalids.retire = object@invalids.retire,
  as.data.frame = TRUE
)

Arguments

Methods (by class)

• periodTransitionProbabilities(pensionTable): Return all transition probabilities of the pension table for the period Period

Examples

pensionTables.load("USA_PensionPlans")
# transitionProbabilities internally calls periodTransitionProbabilities
# if a Period is given:
transitionProbabilities(RP2014.male, Period = 1955)
periodTransitionProbabilities(RP2014.male, Period = 1955)
periodTransitionProbabilities(RP2014.male, Period = 2025)

Description

plot.mortalityTable displays multiple life tables (objects of child classes of mortalityTable) in one plot, with a legend showing the names of the tables. If the argument reference not given, all mortality rates are plotted on a log-linear scale for comparison. If the argument reference is given and is a valid life table, then all death probabilities are scaled by the given reference table and the y-axis shows the death rates as percentage of the reference table.

Usage

## S3 method for class 'mortalityTable'
plot(x, ..., reference = NULL, trend = FALSE)

Arguments

See Also

plotMortalityTables and plotMortalityTableComparisons

Examples

# Load the Austrian census data
mortalityTables.load("Austria_Census")

# Plot some select census tables in a log-linear plot
plot(mort.AT.census.1869.male, mort.AT.census.1869.female,
     mort.AT.census.1971.male, mort.AT.census.1971.female,
     mort.AT.census.2011.male, mort.AT.census.2011.female,
     title="Austrian census tables",
     ylab=expression(q[x]), xlab="Age",
     xlim=c(0,90),
     legend.position=c(0.95,0.05))

# Compare some census tables with the mortality of 2011 Austrian males
plot(mort.AT.census.1869.male, mort.AT.census.1869.female,
     mort.AT.census.1971.male, mort.AT.census.1971.female,
     mort.AT.census.2011.male, mort.AT.census.2011.female,
     title="Austrian Census tables, relative to 2011 males",
     reference=mort.AT.census.2011.male)

Description

plotMortalityTableComparisons prints multiple life tables (objects of child classes of mortalityTable) in one plot and scales each by the given reference table, so that the relative mortality can be easily seen. A legend is added showing the names of the tables.

Usage

plotMortalityTableComparisons(
  data,
  ...,
  aes = NULL,
  ages = NULL,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "",
  legend.position = c(0.9, 0.1),
  legend.justification = c(1, 0),
  legend.title = "Sterbetafel",
  legend.key.width = unit(25, "mm"),
  reference = NULL
)

Arguments

Examples

# Load the Austrian census data
mortalityTables.load("Austria_Census")

# Compare some census tables with the mortality of 2011 Austrian males
# plot with the reference argument is the same as calling plotMortalityTableComparisons
plot(mort.AT.census.1869.male, mort.AT.census.1869.female,
     mort.AT.census.1971.male, mort.AT.census.1971.female,
     mort.AT.census.2011.male, mort.AT.census.2011.female,
     title = "Austrian Census tables, relative to 1971 males",
     reference = mort.AT.census.1971.male)
plotMortalityTableComparisons(mort.AT.census.1869.male, mort.AT.census.1869.female,
     mort.AT.census.1971.male, mort.AT.census.1971.female,
     mort.AT.census.2011.male, mort.AT.census.2011.female,
     title = "Austrian Census tables, relative to 1971 males",
     reference = mort.AT.census.1971.male)

Description

plotMortalityTables prints multiple life tables (objects of child classes of mortalityTable) in one log-linear plot, with a legend showing the names of the tables.

Usage

plotMortalityTables(
  data,
  ...,
  aes = NULL,
  ages = NULL,
  legend.title = "Sterbetafel",
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "",
  legend.position = c(0.9, 0.1),
  legend.justification = c(1, 0),
  legend.key.width = unit(25, "mm"),
  log = TRUE
)

Arguments

Examples

# Load the Austrian census data
mortalityTables.load("Austria_Annuities")
mortalityTables.load("Austria_Census")

# Plot some select census tables in a log-linear plot (plot called
# with mortalityTable objects is equla to calling plotMortalitytTables directly)
plot(mort.AT.census.1869.male, mort.AT.census.1869.female,
     mort.AT.census.1971.male, mort.AT.census.1971.female,
     mort.AT.census.2011.male, mort.AT.census.2011.female,
     title="Austrian census tables",
     ylab=expression(q[x]), xlab="Age",
     xlim=c(0,90),
     legend.position=c(0.95,0.05))

# To compare period or cohort life tables, use the YOB and Period arguments:
plot(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
    Period = 2018, title = "Austrian Annuity Tables, Period 2018")
plot(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
    YOB = 2000, title = "Austrian Annuity Tables for cohort YOB=2000")

Description

plotMortalityTrend prints the trends of multiple life tables (objects of child classes of mortalityTable) in one plot, with a legend showing the names of the tables.

Usage

plotMortalityTrend(
  data,
  ...,
  aes = NULL,
  ages = NULL,
  xlim = NULL,
  ylim = NULL,
  xlab = NULL,
  ylab = NULL,
  title = "",
  legend.position = c(0.9, 0.9),
  legend.justification = c(1, 1),
  legend.title = "Sterbetafel",
  legend.key.width = unit(25, "mm")
)

Arguments

Examples

# Load the Austrian aunnity data
mortalityTables.load("Austria_Annuities")

# Compare the trends of these tables
plotMortalityTrend(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
    Period = 2002, title = "Trends of Austrian Annuity Tables")
# For tables with a non-constant trend, the Period and YOB can be used to compare
# the age-specific trends that apply to the death probabilities during a given
# period or for a given birth year
plotMortalityTrend(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
    YOB = 1950, title = "Trends of Austrian Annuity Tables for cohort YOB=1950")
plotMortalityTrend(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
    YOB = 2000, title = "Trends of Austrian Annuity Tables for cohort YOB=2000")
plotMortalityTrend(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
   Period = 1999, title = "Trends of Austrian Annuity Tables for Period 2002")
plotMortalityTrend(AVOe2005R.male, AVOe2005R.female, AVOe1996R.male, AVOe1996R.female,
   Period = 2030, title = "Trends of Austrian Annuity Tables for Period 2030")
#' @import scales

Description

This data.frame hold the official population counts (in terms of exposure) as well as the death counts for Austria during the calendar year 2017.

Usage

data(PopulationData.AT2017)

Format

A data frame holding female, male and total exposures as well as death counts, indexed by age.

Details

The data was downloaded from https://www.mortality.org/, where is was submitted by Statistik Austria.

Source

https://www.mortality.org/, https://www.statistik.at/

Description

The function pT.calculateTotalMortality calculates the overall mortality from the mortality of actives and disabled

Usage

pT.calculateTotalMortality(object, ...)

pT.recalculateTotalMortality(object, ...)

Arguments

Details

Since a pension tables describes mortalities of actives and of disabled separately, the overall mortality is a function of these two. The function pT.calculateTortalMortality calculates this overall mortality in a way that is consistent with the individual transition probabilities of the pension table.

In particular, the pension table describes the mortalities of the individual sub-populations of actives, disabled and old-age pensioners. The overall mortality is the mortality that results when one discards the additional information about the state and just observes deaths. Internally, the overall mortality is calculated by starting from 10,000 actives and applying the transition dynamics of the pension table to the sub-populations.

For a detailled description, see e.g. the documentation of the Austrian pension table AVÖ 2018-P or the German Heubeck Table DAV 2005-G.

Functions

• pT.recalculateTotalMortality(): Calculate the total mortality of a pension table and assign it to the qgx slot of that table.

References

R. Kainhofer, J. Hirz, A. Schubert. AVÖ 2018-P: Rechnungsgrundlagen für die Pensionsversicherung. Dokumentation der Pensionstafel. AVÖ-Arbeitskreis Rechnungsgrundlagen, 2008. https://avoe.at/rechnungsgrundlagen/pensionskassen/

Description

This function pT.getSubTable allows access to the individual components of a pension table. In contrast to a "normal" mortalityTable, which describes probablilities for only mortality or a single population, a pension table describes transition probabilities for other states, too:

• active population (i.e. not disabled, not retired)

• disabled population (occupational disability)

• old-age pensioners

• widows/widowers

Usage

pT.getSubTable(table, subtable = "qx")

Arguments

Details

The corresponding transition probabilities are:

qx

mortality $q^a_x$ of actives (probability of death)

ix

morbidity $i_x$ of actives (probability occupational disability)

qix

mortality $q^i_x$ of disabled (probability of death)

rx

reactivation $r_x$ of invalids (probability of becoming active again)

qpx

mortality $q^p_x$ of old-age pensioners

qgx

mortality $q^g_x$ of the whole population (including actives and disabled)

hx

probability $h_x$ of leaving a widow/widower when dying at age $x$

yx

average age $y(x)$ of surviving widow/widower when dying at age $x$

qwx

mortality $q^w_x$ of widows

The function pT.getSubTable extracts a single transition probability from the pension table, using the keys given above. The returned object is also a mortalityTable object.

Description

A mortalityTable can store additional information to be used e.g. as additional dimensions in ggplot calls. Typically, these information include sex, base population, observation year, type of data (raw, smoothed), country, type of risk, etc. These additional dimensions are stored in the tbl@data list and will be used by plotMortalityTables and similar functions. pT.setDimInfo works just like mT.setDimInfo, except that it sets the information for all sub-tables of the pension table at the same time.

Usage

pT.setDimInfo(tbl, ..., append = TRUE)

Arguments

Examples

# For examples, please see the \code{mT.setDimInfo} function.

Description

Return a copy of the table with an additional loading added

Usage

setLoading(object, loading = 0)

## S4 method for signature 'mortalityTable'
setLoading(object, loading = 0)

Arguments

Methods (by class)

• setLoading(mortalityTable): Return the life table with the given loading set

Examples

mortalityTables.load("Austria_Census")
# Austrian census mortality 2011 reduced by 30%
setLoading(mort.AT.census.2011.male, loading = -0.3)

Description

Return a copy of the table with the given modification function added

Usage

setModification(object, modification = 0)

## S4 method for signature 'mortalityTable'
setModification(object, modification = 0)

Arguments

Methods (by class)

• setModification(mortalityTable): Return the life table with the given modification set

Examples

mortalityTables.load("Austria_Census")
# Austrian census mortality 2011, with a lower floor of 0.1% death probability
at11.mod1perm = setModification(mort.AT.census.2011.male,
    modification = function(qx) {pmax(qx, 0.001)})
at11.mod1perm@name = paste(at11.mod1perm@name, "at least 0.1%")
# Austrian census mortality 2011, modified with 40% selection for ages
# below 60, vanishing linearly to age 80
at11.modSelection = setModification(mort.AT.census.2011.male,
    modification = function(qx) {
        qx * c(rep(0.6, 60), 0.6 + 0.4 * (0:20)/20, rep(1, length(qx)-81))
    })
at11.modSelection@name = paste(at11.modSelection@name, " 40% selection below 60")

plot(mort.AT.census.2011.male, at11.mod1perm, at11.modSelection,
    title = "Austrian census mortality with modifications", legend.position = c(0.99, 0.01))

Description

Return all transition probabilities of the pension table (generational probabilities)

Usage

transitionProbabilities(object, ...)

## S4 method for signature 'pensionTable'
transitionProbabilities(
  object,
  YOB = 1982,
  ...,
  ages = NULL,
  OverallMortality = FALSE,
  Period = NULL,
  retirement = NULL,
  invalids.retire = object@invalids.retire,
  as.data.frame = TRUE
)

Arguments

Methods (by class)

• transitionProbabilities(pensionTable): Return all transition probabilities of the pension table for the generation YOB

Examples

pensionTables.load("USA_PensionPlans")
transitionProbabilities(RP2014.male, YOB = 1962)
transitionProbabilities(RP2014.male, Period = 1955)
transitionProbabilities(RP2014.male, Period = 2025)

Description

Return a mortalityTable.trendProjection object with the trend damping removed.

Usage

undampenTrend(object)

## S4 method for signature 'mortalityTable.trendProjection'
undampenTrend(object)

Arguments

Methods (by class)

• undampenTrend(mortalityTable.trendProjection): Return a mortalityTable.trendProjection object with the trend damping removed.

Examples

mortalityTables.load("Austria_Annuities")
AVOe2005R.male.undamped = undampenTrend(AVOe2005R.male)
AVOe2005R.male.undamped@name = paste(AVOe2005R.male.undamped@name, "no trend dampening")

plot(AVOe2005R.male, AVOe2005R.male.undamped,
    title = "AVOe 2005R with trend dampening and without", YOB = 2000)

Description

whittaker.mortalityTable uses the Whittaker-Henderson graduation method to smooth a table of raw observed death probabilities, optionally using the exposures stored in the table as weights (if no exposures are given, equal weights are applied). The weights (either explicitly given, implicitly taken from the exposures or implicit equal weights) will be normalized to sum 1. The parameter lambda indicates the importance of smootheness. A lower value of lambda will put more emphasis on reproducing the observation as good as possible at the cost of less smoothness. In turn, a higher value of lambda will force the smoothed result to be as smooth as possible with possibly larger deviation from the input data. All ages with a death probability of NA will be interpolated in the Whittaker-Henderson method (see e.g. Lowrie)

Usage

whittaker.mortalityTable(
  table,
  lambda = 10,
  d = 2,
  name.postfix = ", smoothed",
  ...,
  weights = NULL,
  log = TRUE
)

Arguments

References

Walter B. Lowrie: An Extension of the Whittaker-Henderson Method of Graduation, Transactions of Society of Actuaries, 1982, Vol. 34, pp. 329–372

See Also

whittaker

Examples

# A sample observation table with exposures and raw probabilities
obsTable = mortalityTable.period(
    name = "trivial observed table",
    ages = 0:15,
    deathProbs = c(
        0.0072, 0.00212, 0.00081, 0.0005, 0.0013,
        0.001, 0.00122, 0.00142, 0.007, 0.0043,
        0.0058, 0.0067, 0.0082, 0.0091, 0.0075, 0.01),
    exposures = c(
        150, 222, 350, 362, 542,
        682, 1022, 1053, 1103, 1037,
        968, 736, 822, 701, 653, 438))

# Effect of the different parameters
obsTable.smooth = whittaker.mortalityTable(obsTable,
    lambda = 1/10, d = 2, name.postfix = " smoothed (d=2, lambda=1/10)")
obsTable.smooth1 = whittaker.mortalityTable(obsTable,
    lambda = 1, d = 2, name.postfix = " smoothed (d=2, lambda=1)")
obsTable.smooth2 = whittaker.mortalityTable(obsTable,
    lambda = 1/10, d = 3, name.postfix = " smoothed (d=3, lambda=1/10)")
plot(obsTable, obsTable.smooth, obsTable.smooth1, obsTable.smooth2,
    title = "Observed death probabilities")

# Missing values are interpolated from the Whittaker Henderson
obsTable.missing = obsTable
obsTable.missing@deathProbs[c(6,10,11,12)] = NA_real_
obsTable.interpolated = whittaker.mortalityTable(obsTable,
    lambda = 1/10, d = 2, name.postfix = " missing values interpolated")
plot(obsTable.missing, obsTable.interpolated,
    title = "Missing values are automatically interpolated") + geom_point(size = 3)