mortalityTable.trendProjection-class | R Documentation |
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
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
.
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))
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