logquad {MortCast} | R Documentation |
Log-Quadratic Mortality Model
Description
Predict age-specific mortality rates using the Log-Quadratic Mortality Model (Wilmoth et al. 2012).
Usage
logquad(
e0,
sex = c("male", "female", "total"),
my.coefs = NULL,
q5ranges = c(1e-04, 0.9),
k = 0,
keep.lt = FALSE,
...
)
logquadj(e0m, e0f, ...)
Arguments
e0 |
Vector of target life expectancies. |
sex |
Which sex does the give |
my.coefs |
Data frame with columns “sex”, “age”, “ax”, “bx”, “cx”, “vx”.
The “sex” column should contain values “female”, “male” and/or “total”.
The “age” column must be sorted so that it assures that rows correspond to ages in increasing order.
Any |
q5ranges |
A vector of size two, giving the min and max of 5q0 used in the bisection method. |
k |
Value of the |
keep.lt |
Logical. If |
... |
Additional arguments passed to the underlying function. |
e0m |
A time series of target male life expectancy. |
e0f |
A time series of target female life expectancy. |
Details
The LogQuad method in this implementation projects mortality rates using the equation
\log(m_x) = a_x + b_x h + c_x h^2 + v_x k
where a_x
, b_x
, c_x
and v_x
are age-specific coefficients, h = \log( 5q0 )
(i.e. reflects child mortality),
and k
should be chosen to match 45q15 (adult mortality) or set to 0 (default). The coefficients
can be passed as inputs, or taken from the package default dataset LQcoef
which
are taken from https://u.demog.berkeley.edu/~jrw/LogQuad/.
For the given inputs and values of life expectancy e0, the function finds values of h
that
best match e0, using life tables and the bisection method. It returns the corresponding mortality schedule
for each value of e0.
Function logquad
is for one sex, while logquadj
can be used for both sexes.
Value
Function logquad
returns a list with the following elements: a matrix mx
with the predicted mortality rates. If keep.lt
is TRUE
, it also
contains matrices sr
(survival rates), and life table quantities Lx
and lx
.
Function logquadj
returns a list of objects, one for each sex.
References
Wilmoth, J., Zureick, S., Canudas-Romo, V., Inoue, M., Sawyer, C. (2012). A Flexible Two-Dimensional Mortality Model for Use in Indirect Estimation. Population studies, 66(1), 1-28. doi: 10.1080/00324728.2011.611411
See Also
LQcoef
, mortcast.blend
, mortcast
, pmd
, mlt
Examples
data(e0Mproj, package = "wpp2017")
country <- "Brazil"
# get target e0
e0m <- as.numeric(subset(e0Mproj, name == country)[-(1:2)])
# project into future
pred <- logquad(e0m, sex = "male")
# plot first projection in black and the remaining ones in heat colors
plot(pred$mx[,1], type = "l", log = "y", ylim = range(pred$mx),
ylab = "male mx", xlab = "Age", main = country)
for(i in 2:ncol(pred$mx)) lines(pred$mx[,i],
col = heat.colors(20)[i])