LifeTables (version 0.1)

LifeTables-package: HMD Model Life Tables

Description

Provides functions for using a model life table system generated from the Human Mortality Database

Details

ll{ Package: LifeTables Type: Package Version: 1.0 Date: 2011-06-01 License: LazyLoad: yes } mortmod is the mortality model that is the basis for the system. The user can supply the two input parameters - family and level - and a complete mortality rate schedule can be generated. If the user has some measure of child mortality (or a partial schedule) and optionally 45q15, the function hmd.DA can be used to find the appropriate family. Then alpha.e0 can calculate an alpha value to generate a life table with a desired life expectancy. The function mod.lt combines the steps outlined above. It will find the family and level based on the child (and possibly adult) mortality indicator and a desired life expectancy and return a complete life table generated with the appropriate input parameter values.

There is a graphical user interface implemented in the package that allows for generating model life tables, exporting them and plotting various measures of the life table. It can be invoked using the function modelLT.gui().

References

S. Clark and D. Sharrow (2011) "Contemporary Model Life Tables for Developed Countries: An Application of Model-Based Clustering." Working Paper No. 107, Center for Statistics and the Social Sciences, University of Washington, www.csss.washington.edu/Papers/

C. Fraley and A. E. Raftery (2002) "Model-based clustering, discriminant analysis, and density estimation." Journal of the American Statistical Association 97:611-631.

C. Fraley and A. E. Raftery (2006) "MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering", Technical Report no. 504, Department of Statistics, University of Washington

Human Mortality Database University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). Available at www.mortality.org or www.humanmortality.de (data downloaded on [November 2009]).

Examples

# first get the appropriate family #
hmd.DA(x=.09, sex="male", child.mort=4, adult.mort=.28)$classification

# then get the appropriate level (alpha) #
alpha.e0(pattern=1, e0.target=59, sex="male")

# put in the family and alpha to model to produce complete schedule #
mortmod(pattern=1, alpha=.034, sex="male")