Description Usage Arguments Details Value Author(s) See Also Examples
Compute Life Tables from Parameters of a Mortality Law
1 
x 
Vector of ages at the beginning of the age interval. 
par 
The parameters of the mortality model. 
law 
The name of the mortality law/model to be used. e.g.

sex 
Sex of the population considered here. Default: 
lx0 
Radix. Default: 100 000. 
ax 
Numeric scalar. Subjecttime alive in ageinterval for those who
die in the same interval. If 
The "life table" is also called "mortality table" or "actuarial table". This shows, for each age, what the probability is that a person of that age will die before his or her next birthday, the expectation of life across different age ranges or the survivorship of people from a certain population.
The output is of the "LifeTable"
class with the components:
lt 
Computed life table; 
call 

process_date 
Time stamp. 
Marius D. Pascariu
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62  # Example 1  Makeham  4 tables 
x1 = 45:100
L1 = "makeham"
C1 = matrix(c(0.00717, 0.07789, 0.00363,
0.01018, 0.07229, 0.00001,
0.00298, 0.09585, 0.00002,
0.00067, 0.11572, 0.00078),
nrow = 4, dimnames = list(1:4, c("A", "B", "C")))
LawTable(x = 45:100, par = C1, law = L1)
# WARNING!!!
# It is important to know how the coefficients have been estimated. If the
# fitting of the model was done over the [x, x+) agerange, the LawTable
# function can be used to create a life table only for age x onward.
# What can go wrong?
# ** Example 1B  is OK.
LawTable(x = 45:100, par = c(0.00717, 0.07789, 0.00363), law = L1)
# ** Example 1C  Not OK, because the life expectancy at age 25 is
# equal with life expectancy at age 45 in the previous example.
LawTable(x = 25:100, par = c(0.00717, 0.07789, 0.00363), law = L1)
# Why is this happening?
# If we have a model that covers only a part of the human mortality curve
# (e.g. adult mortality), in fitting the x vector is scaled down, meaning
# age (x) becomes (x  min(x) + 1). And, the coefficients are estimated on
# a scaled x in ordered to obtain meaningful estimates. Otherwise the
# optimization process might not converge.
# What can we do about it?
# a). Know which mortality laws are rescaling the x vector in the fitting
# process. If these models are fitted with the MortalityLaw() function, you
# can find out like so:
A < availableLaws()$table
A[, c("CODE", "SCALE_X")]
# b). If you are using one of the models that are applying scaling,
# be aware over what agerange the coefficients have been estimated. If they
# have been estimated using, say, ages 50 to 80, you can use the
# LawTable() to build a life tables from age 50 onwards.
# Example 2  HeligmanPollard  1 table 
x2 = 0:110
L2 = "HP"
C2 = c(0.00223, 0.01461, 0.12292, 0.00091,
2.75201, 29.01877, 0.00002, 1.11411)
LawTable(x = x2, par = C2, law = L2)
# Because "HP" is not scaling down the x vector, the output is not affected
# by the problem described above.
# Check
LawTable(x = 3:110, par = C2, law = L2)
# Note the e3 = 70.31 in both tables

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