| Title: | Hybrid Mortality Estimation |
| Version: | 1.3 |
| Date: | 2017-07-25 |
| Author: | Titus K Rotich <rktitus@uoeld.ac.ke> |
| Maintainer: | Titus K Rotich <rktitus@uoeld.ac.ke> |
| Description: | Hybrid Mortality Modelling (HMM) provides a framework in which mortality around "the accident hump" and at very old ages can be modelled under a single model. The graphics' codes necessary for visualization of the models' output are included here. Specifically, the graphics are based on the assumption that, the mortality rates can be expressed as a function of the area under the curve between the crude mortality rates plots and the tangential transform of the force of mortality. |
| Depends: | R (≥ 3.4.0) |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| NeedsCompilation: | no |
| Packaged: | 2017-07-28 23:25:39 UTC; rktit |
| Repository: | CRAN |
| Date/Publication: | 2017-07-29 12:43:55 UTC |
Switzerland Mortality Data
Description
Exposed to Risk and number of deaths data.
Usage
Mortality
Format
A data frame with 6 columns corresponding to:
- Year
Corresponding year of data collected
- Age
Age of the individual
- E.Male
Male Exposed-to-Risk Population
- E.Female
Female Exposed-to-Risk Population
- D.Male
Number of male death counts, for the given year and age
- D.Female
Number of female death counts, for the given year and age
Details
Mortality data for both Males and Females in Switzerland, from 1981 to 2014.
These data are freely available at the Human Mortality Database
Source
http://www.mortality.org/cgi-bin/hmd/country.php?cntr=CHE&level=1
References
Glei, D. and Andreeva, M. (2016). About mortality data for switzerland.
A Plotting Function
Description
Produces a plot of the area-under-the-curve for the mortality data, but lplot() inverted
Usage
iplot(n, x, add = F)
Arguments
n |
the length of the vector Defaults to TRUE. |
x |
the vector arguement. |
add |
whether to add lines. Default is FALSE |
Examples
m1 <- Mortality$D.Male[which(Mortality$Year == 2008)]
m2 <- Mortality$E.Male[which(Mortality$Year == 2008)]
male.1 <- m1/m2
male.2 <- log(male.1[!is.na(male.1)])
iplot(1:length(male.2),male.2)
iplot(1:length(male.2),male.2,add=TRUE)
A Plotting Function
Description
Produces a plot of the area-under-the-curve for the mortality data
Usage
lplot(n, x, add = F)
Arguments
n |
the length of the vector Defaults to TRUE. |
x |
the vector arguement. |
add |
whether to add lines. Default is FALSE |
Examples
m1 <- Mortality$D.Male[which(Mortality$Year == 2008)]
m2 <- Mortality$E.Male[which(Mortality$Year == 2008)]
male.1 <- m1/m2
male.2 <- log(male.1[!is.na(male.1)])
lplot(1:length(male.2),male.2)
lplot(1:length(male.2),male.2,add=TRUE)
A Plotting Function
Description
Produces a plot of the difference between the area-under-the-curve for the mortality data and the extended mortality boundaries
Usage
mmplot(n, x, young, old)
Arguments
n |
the length of the vector Defaults to TRUE. |
x |
the vector arguement. |
young |
the age at which the accident hump begins. Must be entered |
old |
age at which, either mortality experience between males and females converge, or rapid acceleration of mortality. This is typically over 80 years. |
Examples
#Examples
m1 <- Mortality$D.Male[which(Mortality$Year == 2008)]
m2 <- Mortality$E.Male[which(Mortality$Year == 2008)]
male.1 <- m1/m2
male.2 <- log(male.1[!is.na(male.1)])
lplot(1:length(male.2),male.2)
mmplot(1:length(male.2),male.2,young=17,old=80)
A Plotting Function
Description
Produces a plot of a copula, which can be used to assess the dependency between two sexes bounded by the actual and the expanded mortality estimates
Usage
pccopula(theta, pl = 1, z)
Arguments
theta |
gives the order. |
pl |
gives the association. |
z |
the length of the z axis Defaults to 10. |
Examples
#Examples
pccopula(theta=3,pl=.5,z=10)
A Plotting Function
Description
Similar to pccopula(), but suitable when the dependence is stronger at the older ages
Usage
pgcopula(theta, pl = 1, z)
Arguments
theta |
gives the order. |
pl |
gives the association, with a correction for the direction of dependence |
z |
the length of the z axis Defaults to 10. |
Examples
#Examples
pgcopula(theta=1.3,pl=2,z=10)
A Plotting Function
Description
Produces a similar plot as lplot(), only a transposition of ages is made
Usage
vplot(n, x, add = F)
Arguments
n |
the length of the vector Defaults to TRUE. |
x |
the vector arguement. |
add |
whether to add lines. Default is FALSE |
Examples
m1 <- Mortality$D.Male[which(Mortality$Year == 2008)]
m2 <- Mortality$E.Male[which(Mortality$Year == 2008)]
male.1 <- m1/m2
male.2 <- log(male.1[!is.na(male.1)])
vplot(1:length(male.2),male.2)