# Using the MortalityTables Package

#### 2020-12-13

required <- c("tidyverse")
if (!all(sapply(required,
function(pkg) requireNamespace(pkg, quietly = TRUE)))) {
message(paste("This vignette needs the followig packages:\n\t",
paste(required, collapse = " "),
"\nSince not all are installed, code will not be executed: "))
knitr::opts_chunk$set(eval = FALSE) } 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. # 1 Types of Life Tables Provided types of mortality tables are: • Base class Class mortalityTable • Period life table Class mortalityTable.period(ages, deathProbs, ..., baseYear=2000) Death probabilities observed / predicted for one observation year; No dependency on the bith year is assumed. • Cohort life table using age-specific trends Class mortalityTable.trendProjection Death probabilities of a given base year are projected into the future using age-specific trends $$\lambda_x$$. The death probability of an $$x$$-year old in year baseYear + n is calculated as: $q_x^{(baseYear+n)} = q_x^{(baseYear)} \cdot e^{-n\cdot\lambda_x}$ Consequently, the death probabilities for a person born in year YOB can be calculated as $q_x^{YOB} = q_x^{(base)} \cdot e^{-(YOB+x-baseYear)\cdot \lambda_x}$ • Cohort life table approximation using age shift Class mortalityTable.ageShift Death probabilities for cohort $$YOB$$ are obtained by using death probabilities for cohort $$X$$ and modifying the technical age with a birth-year dependent shift: $q_x^{YOB} = q_{x+shift(YOB)}^{(base)}$ • Observed life table Class mortalityTable.observed Death probabilities observed during several years. The probabilities are stored as a matrix with observation year and age as dimensions. • Mixed life table Class mortalityTable.mixed Arithmetic mean of two life tables with given weights. This approach is often used to generate unisex life tables by mixing male and female mortalities with given weights (e.g. 70:30 or 40:60) • Cohort life table using age-specific improvement factors Class mortalityTable.improvementFactors Project base life table using age-specific improvement factors. • Pension tables Class pensionTable Transition probabilities for a four-state pension model (active, invalid, retirement and death, with a possible widow in the event of death). # 2 Loading the MortalityTables package library("MortalityTables") # 3 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 available data sets mortalityTables.list() #> [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" "Germany_Annuities" #> [13] "Germany_Annuities_DAV1994R" "Germany_Annuities_DAV2004R" #> [15] "Germany_Census" "Germany_Endowments" #> [17] "Germany_Endowments_DAV1994T" "Germany_Endowments_DAV2008T" #> [19] "USA_Annuities" "USA_Annuities_1971IAM" #> [21] "USA_Annuities_1983a" "USA_Annuities_1994GAR" #> [23] "USA_Annuities_2012IAM" "USA_Annuities_Annuity2000" #> [25] "Austria_PK-Bestand_2010-16" "Austria_Annuities" #> [27] "Austria_Annuities_AVOe1996R" "Austria_Annuities_AVOe2005R" #> [29] "Austria_Annuities_EROMF" "Austria_Annuities_RR67" #> [31] "Austria_Census" "Austria_Endowments_ADSt2426_2Lives" #> [33] "Austria_PopulationForecast" "Austria_PopulationMCMC" #> [35] "Austria_PopulationObserved" "Austria_VUGesamtbestand_2012-16" #> [37] "Germany_Annuities" "Germany_Annuities_DAV1994R" #> [39] "Germany_Annuities_DAV2004R" "Germany_Census" #> [41] "Germany_Endowments" "Germany_Endowments_DAV1994T" #> [43] "Germany_Endowments_DAV2008T" "USA_Annuities" #> [45] "USA_Annuities_1971IAM" "USA_Annuities_1983a" #> [47] "USA_Annuities_1994GAR" "USA_Annuities_2012IAM" #> [49] "USA_Annuities_Annuity2000" # 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" "Austria_PK-Bestand_2010-16" #> [13] "Austria_Annuities" "Austria_Annuities_AVOe1996R" #> [15] "Austria_Annuities_AVOe2005R" "Austria_Annuities_EROMF" #> [17] "Austria_Annuities_RR67" "Austria_Census" #> [19] "Austria_Endowments_ADSt2426_2Lives" "Austria_PopulationForecast" #> [21] "Austria_PopulationMCMC" "Austria_PopulationObserved" #> [23] "Austria_VUGesamtbestand_2012-16" # Load the German annuity table DAV 2004-R mortalityTables.load("Germany_Annuities_DAV2004R") # Load all Austrian annuity data sets mortalityTables.load("Austria_Annuities*") mortalityTables.load("Austria_Census") In the next few sections we will always use some of the provided life tables for demonstration purposes. # 4 Working with life table objects ## 4.1 Plotting life tables The package provides several functions to plot lifetables: • plotMortalityTables(table1, table2, ...) A log-linear plot comparing all given life tables. • plotMortalityTableComparisons(table1, table2, ..., reference=reftable) Plot the given life tables as percentages relative to the reference table • plotMortalityTrend(table1, table2, ..., YOB, Period) Plot the yearly mortality improvement factors (for either the given observation year Period or the birth-year YOB) These functionalities are also combined into the S3 plot function for the mortalityTable class, so you can usually just call plot on the mortality tables. If the trend = TRUE argument is given, plotMortalityTrend is used, if the reference argument is given, plotMortalityTableComparisons is used, otherwise plotMortalityTables is called. # Log-linear plot comparing some Austrian census tables plot(mort.AT.census.1951.male, mort.AT.census.1991.male, mort.AT.census.2001.male, mort.AT.census.2011.male, legend.position = c(1,0))  # Relative death probabilities in percentage of the latest census plot(mort.AT.census.1951.male, mort.AT.census.1991.male, mort.AT.census.2001.male, reference = mort.AT.census.2011.male, legend.position = c(1,0.75), ylim = c(0,4)) #> Warning in normalize_deathProbabilities(if (is.data.frame(t@data$dim)
#> || : Reference mortality table does not contain ages
#> 101102103104105106107108109110111112 required for normalization. These ages will
#> not be normalized!

For cohort life tables, the plot functions also take either the YOB or the Period parameter to plot either the cohort death probabilities for the given birth year or the period death probabilities for the given observation year.

# Comparison of two Austrian annuity tables for birth year 1977
plot(AVOe1996R.male, AVOe2005R.male, YOB = 1977, title = "Comparison for YOB=1977")


# Comparison of two Austrian annuity tables for observation year 2020
plot(AVOe1996R.male, AVOe2005R.male, Period = 2020, title = "Comparison for observation year 2020")

## 4.2 Obtaining period and cohort death probabilities

To obtain death probabilities from all the different types of tables, there are two functions:

• deathProbabilities: Returns the (cohort) death probabilities of the life table given the birth year
• periodDeathProbabilities: Returns the (period) death probabilities of the life table for a given observation year
mortalityTables.load("Austria_Annuities")
# Get the cohort death probabilities for Austrian Annuitants born in 1977:
qx.coh1977 = deathProbabilities(AVOe2005R.male, YOB = 1977)

# Get the period death probabilities for Austrian Annuitants observed in the year 2020:
qx.per2020 = periodDeathProbabilities(AVOe2005R.male, Period = 2020)

These functions return the death probabilities as a simple, numeric R vector.

There are two similar functions that return the death probabilities as a period life table object that can be used with all other functions provided by this package:

• getCohortTable: Get a mortalityTable object describing the death probabilities for people born in the given year
• getPeriodTable: Get a mortalityTable object describing the death probabilities observed in the given year
# Get the cohort death probabilities for Austrian Annuitants born in 1977 as a mortalityTable.period object:
table.coh1977 = getCohortTable(AVOe2005R.male, YOB = 1977)

# Get the period death probabilities for Austrian Annuitants observed in the year 2020:
table.per2020 = getPeriodTable(AVOe2005R.male, Period = 2020)

# Compare those two in a plot:
plot(table.coh1977, table.per2020, title = "Comparison of cohort 1977 with Period 2020", legend.position = c(1,0))

Not surprisingly, at 43 years the two death probabilities cross, because in 2020 the person born 1977 is 43 years old, so the $$q_x$$ refer to the same person. However, for younger ages, the period 2020 probabilities are lower, because the mortality improvement for those younger ages has much less time in the cohort 1977 table. For ages above 43 the cohort table describes the mortality further into the future than 2020, so there is more improvement and thus lower death probabilities for the cohort life table.

## 4.3 Other data extraction functions from life tables

function description
ages(table) Returns the vector of ages, for which the life table can provide death probabilities
getOmega(table) Returns the maximum age, for which the life table can provide dath probabilities
ageShift(table, YOB) Returns the age shift for the given year of birth
baseTable(table) Returns the base table, from which the table projects (for cohort tables)
baseYear(table) Returns the year of the base table
lifetable(table, YOB) Returns the cohort death probabilities as a lifetable object for use with the lifecontingencies package

## 4.4 Dimensional information

Mortality tables are always created for special purposes, particular collectives, types of risk, sex, year, etc. So, each MortalityTable object provides for a list of such factors that describe the underlying target of the mortality table and that can be used e.g. when plotting mortality Tables (just like any other factor variable in a ggplot):

plotMortalityTables(
mort.AT.census[c("m", "w"), c("1951", "1991", "2001", "2011")]) +
aes(color = as.factor(year), linetype = sex) + labs(color = "Period", linetype = "Sex")

The dimensional information is stored inside the @data$dim field of the MortalityTable: mort.AT.census.2011.male@data$dim
#> $sex #> [1] "m" #> #>$collar
#> [1] "Gesamtbevölkerung"
#>
#> $type #> [1] "Volkssterbetafel Österreich" #> #>$data
#> [1] "official"
#>
#> $year #> [1] 2011 #> #>$table
#> [1] "ÖVSt 2010/12"

There are no hard and enforced rules for these names and the potential values of the dimensional information. There are, however, some conventions that are obeyed by most of the tables provided by this package:

Key Potential values Description
sex “m”, “w”, “u” Sex
collar “Rententafel”, “Gruppenrententafel”, “Einzel”, “Gruppe”, “Gesamtbevölkerung”, “Raucher”, “Nichtraucher”, “Arbeiter”, “Angestellte”, “Mischtafel” Collective, to which the mortality table applies
type “Rententafel”, “Volkssterbetafel”, “Pensionstafel”, “Bevölkerungsprognose”, “Beobachtung”, “Risikotafel” The type of table
data “official”, “raw”, “loaded”, “loaded, group”, “unloaded”, “age-shifted”, “geglättet” The type of data
year numeric year, “2014-2080”, “1980-2017”, “1947-2017” The year (or range) described by the table
tablename “AVÖ 1996-R”, “AVÖ 2005-R”, “EROM 85”, “EROF 85”, “EROM G1950”, “EROF G1950”, “EROM G1950 AV”, “EROF G1950 AV”, “RR67”, “DAV 1994R”, “DAV 2004R”, “DAV 1994T”, “DAV 2008T”, “1971 IAM”, “1971 IAM projected”, “1983a”, “1983 GAM”, “1994 GAM”, “1994 GAR”, “2012 IAM”, “Annuity 2000”, “AVÖ 1999-P”, “AVÖ 2008-P”, “Ettl-Pagler 1989”, “DAV 2005-G” The formal name of the table
risk “Tod”, “sonst. Ausscheiden”, “Invalidisierung”, “Partnerwahrscheinlichkeit im Tod”, “mittl. Hinterbliebenenalter” The type of risk described by the table
probability “qx”, “sx”, “ix”, “qgx”, “qix”, “qpx”, “hx”, “qwy”, “yx” The probability described by the table (corresponds with “risk”)
country “Österreich”, “Deutschland”, “USA”, … The geographic region of the table (not neccessarily only countries)
source “AVÖ”, “Statistik Austria”, “DAV”, … Source of the data / table

Some of the provided datasets (mortality tables) have not yet fully implemented these conventions, so pleasy be vary when using them.

# 5 Creating a life table object

## 5.1 Period life tables

Period death probabilities are the simplest type of life table, giving the probabilities of death observed during the corresponding year (the “period”). The death probabilities of different ages refer to different persons, being of the corresponding ages in the observation year. All that is needed to create a period life table are the death probabilities and the corresponding ages:

lt = mortalityTable.period(name = "Sample period lifetable", ages = 1:99, deathProbs = exp(-(99:1)/10))
plot(lt, title = "Simple log-linear period mortality table")

deathProbabilities(lt)
#>  [1] 5.017468e-05 5.545160e-05 6.128350e-05 6.772874e-05 7.485183e-05
#>  [6] 8.272407e-05 9.142423e-05 1.010394e-04 1.116658e-04 1.234098e-04
#> [11] 1.363889e-04 1.507331e-04 1.665858e-04 1.841058e-04 2.034684e-04
#> [16] 2.248673e-04 2.485168e-04 2.746536e-04 3.035391e-04 3.354626e-04
#> [21] 3.707435e-04 4.097350e-04 4.528272e-04 5.004514e-04 5.530844e-04
#> [26] 6.112528e-04 6.755388e-04 7.465858e-04 8.251049e-04 9.118820e-04
#> [31] 1.007785e-03 1.113775e-03 1.230912e-03 1.360368e-03 1.503439e-03
#> [36] 1.661557e-03 1.836305e-03 2.029431e-03 2.242868e-03 2.478752e-03
#> [41] 2.739445e-03 3.027555e-03 3.345965e-03 3.697864e-03 4.086771e-03
#> [46] 4.516581e-03 4.991594e-03 5.516564e-03 6.096747e-03 6.737947e-03
#> [51] 7.446583e-03 8.229747e-03 9.095277e-03 1.005184e-02 1.110900e-02
#> [56] 1.227734e-02 1.356856e-02 1.499558e-02 1.657268e-02 1.831564e-02
#> [61] 2.024191e-02 2.237077e-02 2.472353e-02 2.732372e-02 3.019738e-02
#> [66] 3.337327e-02 3.688317e-02 4.076220e-02 4.504920e-02 4.978707e-02
#> [71] 5.502322e-02 6.081006e-02 6.720551e-02 7.427358e-02 8.208500e-02
#> [76] 9.071795e-02 1.002588e-01 1.108032e-01 1.224564e-01 1.353353e-01
#> [81] 1.495686e-01 1.652989e-01 1.826835e-01 2.018965e-01 2.231302e-01
#> [86] 2.465970e-01 2.725318e-01 3.011942e-01 3.328711e-01 3.678794e-01
#> [91] 4.065697e-01 4.493290e-01 4.965853e-01 5.488116e-01 6.065307e-01
#> [96] 6.703200e-01 7.408182e-01 8.187308e-01 9.048374e-01

## 5.2 Cohort life tables with trend projection

A cohort life table with trend projection needs the following parameters:

• The base table $$q_x^{(base)}$$ (death probabilities) for the given base period as a vector
• Age-specific trend factors $$\lambda_x$$ as a vector
• The base year (numeric)
atPlus2 = mortalityTable.trendProjection(
name = "Austrian Census Males 2011, 2% yearly trend",
baseYear = 2011,
deathProbs = deathProbabilities(mort.AT.census.2011.male),
ages = ages(mort.AT.census.2011.male),
trend = rep(0.02, length(ages(mort.AT.census.2011.male)))
)

Some life tables do not assume a constant age-specific trend over time, but rather assume that the currently observed high mortality improvements are just a temporary effect, so the current trend is in effect only for some time and then reduces to some kind of long-term trend.

There are two conceptual approaches: One is to use a trend dampening function that is simply applied to the starting trend. So, while the initial trend might be 3%, i.e. the projection will use (ObservationYear-BaseYear) * OriginalYear, over time it will assume the value dampeningFunction(ObservationYear-BaseYear) * OriginalTrend. The dampening function in this case gives the cumulated trend effect from the base year until the observation year. To implement this trend reduction with the MortalityTables package, simply pass a one-argument function as the dampingFunction slot to the class, the argument will be the number of years from the base year (NOT the calendar year!):

atPlus2.damp = mortalityTable.trendProjection(
name = "Austrian M '11, 2% yearly, damping until 2111",
baseYear = 2011,
deathProbs = deathProbabilities(mort.AT.census.2011.male),
ages = ages(mort.AT.census.2011.male),
trend = rep(0.02, length(ages(mort.AT.census.2011.male))),
# damping function: 2011: full effect, linear reduction until yearly trend=0 in 2111:
# 2011: 100%, 2012: 99%, 2013: 98% => For 2013 we have a cumulative trend
# of 297% instead of 300% for three full yearly trends!
dampingFunction = function(n) { n - n * (n + 1) / 2 / 100 }
)

plot(mort.AT.census.2011.male, atPlus2, atPlus2.damp, YOB = 2011, legend.position = c(0.8,0.75))

The other approach is to assume that instead of the initial trend, after some time a second trend (slot trend2) takes over. In this case, the dampingFunction slot is again a one-argument function that now gives the weight of the first trend, while 1-dampingFunction(year) will give the weight of the second trend. As the weights will be applied for the whole period from the base- to the observation year, the weights need to be cumulated and normalized.

The argument in this case is the actual calendar year (not the year since the base year like it was in the one-trend case above!)

atPlus2.damp2 = mortalityTable.trendProjection(
name = "Austrian M '11, 2% yearly, 1% long-term",
baseYear = 2011,
deathProbs = deathProbabilities(mort.AT.census.2011.male),
ages = ages(mort.AT.census.2011.male),
trend = rep(0.02, length(ages(mort.AT.census.2011.male))),
trend2 = rep(0.01, length(ages(mort.AT.census.2011.male))),
# damping function interpolates between the two trends:
# until 2021 trend 1, from 2031 trend 2, linearly beteen
dampingFunction = function(year) {
if (year <= 2021) 1
else if (year > 2031) 14.5/(year - 2011)
else 1 - (year - 2021)*(year - 2021 + 1) / 20 / (year - 2011)
}
)

plot(mort.AT.census.2011.male, atPlus2, atPlus2.damp, atPlus2.damp2, YOB = 2011, legend.position = c(0.02, 0.98), legend.justification = c(0, 1))

## 5.3 Cohort life tables with age-shift

Age-shifted cohort life tables are an approximation to full cohort life tables. Full cohort life tables apply a trend or improvment factors to the death probabilities of a base year to obtail death probabilities for a given birth year. Age-shifting rather modifies the age of the corresponding person and uses the same, unmodified base table for all cohorts. Basically, it works like this:

A 60-year old born in 1950 has the same death probability as a 50-year old born in 1900, so instead of looking at the cohort 1950, we can look at the cohort 1900 and for a person born 1950 we treat him as if he were 10 years younger.

So, an age-shifted cohort life table just needs the base table and for each birth year the amount the age is modified.

For those people, who think visually, age shifting works on the death probabilities as following: A normal trend moves the $$q_x$$ curve downwards. Age-shifting approximates this by shifting the $$q_x$$ curve to the right without modifying its values.

The following example clearly shows this, with the blue curve being the base table for YOB 2011. A full trend projection moves the curve down to the green line, while age-shifting moves the base curve to the right so that it coincides as much as possible with the exact (green) line.

baseTableShift = getCohortTable(atPlus2, YOB = 2011);
baseTableShift@name = "Base table of the shift (YOB 2011)"

atShifted = mortalityTable.ageShift(
name = "Approximation with age shift",
baseYear = 2011,
deathProbs = deathProbabilities(baseTableShift),
ages = ages(baseTableShift),
ageShifts = data.frame(
shifts = c(
rep( 0, 3),
rep(-1, 3),
rep(-2, 3),
rep(-3, 3),
rep(-4, 3),
rep(-5, 3),
rep(-6, 3)
),
row.names = 2011:2031
)
)

ageShift(atShifted, YOB = 2021)
#> [1] -3

plot(baseTableShift, atPlus2, atShifted, YOB = 2021, legend.position = c(0.8,0.75))

As one can see, for ages above 40 years, the table with 2% yearly trend and the corresponding age-shifted table have roughly the same mortalities. Below 40 years, the two are very different, so this approximation through age-shifting should really be used with extreme care!

# 6 Modifying life table objects

## 6.1 Copying life tables

Life tables are simple pass-by-value S4 objects, so copying works by simple assignment.

b = AVOe2005R.female
b@name = "Modified Copy"
# only b is modified, not the original table
b@modification = function(qx) pmax(qx, 0.01)
plot(AVOe2005R.female, b, YOB = 2000)

When calculating premiums for life insurance contracts, one often needs to add a certain security loading on the raw death probabilities (e.g. 10% increased death probabilities) to account for statistical fluctuations. This can be easily done with the setLoading function that returns a copy of the given table and adds the given security loading.

AVOe2005R.female.sec = setLoading(AVOe2005R.female, loading = 0.1);
# Make sure the modified table has a new name, otherwise plots might break
plot(AVOe2005R.female, AVOe2005R.female.sec, title = "Original and modified table")

## 6.3 Adding a modification to the raw probabilities

Some uses require post-processing of the death probabilities, like adding a lower bound for the death probabilities. To achive this, all mortalityTable-derived classes have a slot modification that takes a function that is passed the vector of death probabilities.

AVOe2005R.female.mod = setModification(AVOe2005R.female, modification = function(qx) pmax(0.03, qx));
# Make sure the modified table has a new name, otherwise plots might break
AVOe2005R.female.mod@name = "Modified table (lower bound of 3%)"
plot(AVOe2005R.female, AVOe2005R.female.mod, title = "Original and modified table")

# 7 Creating mortality tables from data and modifying them using various helper functions

The package MortalityTables not only provides the data structures and some examples of mortality tables, it also provides several functions to create mortality tables from raw data and modify them. The package provides several editing functions, which all begin with the prefix mT..

Let us take as an example the provided dataset PopulationData.AT2017 of Austrian population data (exposure and deaths counts for the year 2017).

For simplicity, we only look at the unisex data (i.e. male + female numbers, which are already provided as total exposure and total deaths). The raw mortality can then be calculated as

library(tidyverse)
data("PopulationData.AT2017", package = "MortalityTables")
PopulationData.AT2017.raw = PopulationData.AT2017 %>%
select(age, exposure.total, deaths.total) %>%
mutate(qraw = deaths.total / (exposure.total + deaths.total/2))

We now have all data needed to put it into a MortalityTable object (some fields like the exposre and the data list are not strictly needed, but can be useful later on):

PopulationTable.AT2017 = mortalityTable.period(
name = "Austrian Population Mortality 2017 (raw)",
baseYear = 2017,
deathProbs = PopulationData.AT2017.raw$qraw, ages = PopulationData.AT2017.raw$age,
exposures = PopulationData.AT2017.raw$exposure.total, data = list( deaths = PopulationData.AT2017.raw$deaths.total,
dim = list(sex = "u", collar = "Population", type = "raw", year = "2017")
)
)
plotMortalityTables(PopulationTable.AT2017, title = "Austrian population mortality (raw), 2017")

Of course, we sooner or later want to work with a smooth table rather than the raw death probabilities. The most common approach to smoothing mortality tables is the Whittaker-Henderson method of graduation, which is provided by the function whittaker.mortalityTable(). The parameters are the $$\lambda$$ smoothing parameter (determining how smooth the result shall be, which in turn means that the result might be quite distant from the raw probabilities in some ages) and the order of differences $$d$$ (the default 2 typically suffices). Since we have the exposures available and stored inside the table, the whittaker.mortalityTable() function will use the exposures as weight and so try to match age ranges with high exposure much better than e.g. old ages with hardly any living.

PopulationTable.AT2017.smooth = PopulationTable.AT2017 %>%
whittaker.mortalityTable(lambda = 1/10, d = 2, name.postfix = ", Whittaker") %>%
mT.setDimInfo(type = "smoothed")
plotMortalityTables(PopulationTable.AT2017, PopulationTable.AT2017.smooth, title = "Austrian population mortality (raw and smoothed), 2017")  +
aes(colour = type)