Description Usage Arguments Value References See Also Examples
Fit Compositional Data (CoDa) model for forecasting the life table distribution of deaths. CoDa is a Lee-Carter type model. A key difference between the Lee-Carter (1992) method and the Compositional Data (CoDa) model is that the former fits and forecasts the death rates (mx) while the latter is based on the life table death distribution (dx). See Bergeron-Boucher et al. (2017) for a detail description and mathematical formulation.
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data |
Matrix containing mortality data (dx) with ages as row and time as column. |
x |
Vector of input ages (optional). Used to label the output objects and plots. |
y |
Vector of input years (optional). Used to label the output objects and plots. |
The output is an object of class "coda"
with the components:
input |
List with arguments provided in input. Saved for convenience. |
call |
An unevaluated function call, that is, an unevaluated expression which consists of the named function applied to the given arguments. |
coefficients |
Estimated coefficients. |
fitted.values |
Fitted values of the estimated CoDa model. |
residuals |
Deviance residuals. |
x |
Vector of ages used in the fitting. |
y |
Vector of years used in the fitting. |
Bergeron-Boucher M-P., Canudas-Romo V., Oeppen J. and Vaupel W.J. 2017. Coherent forecasts of mortality with compositional data analysis. Demographic Research, Volume 17, Article 17, Pages 527–566.
Oeppen, J. 2008. Coherent forecasting of multiple-decrement life tables: A test using Japanese cause of death data. Paper presented at the European Population Conference 2008, Barcelona, Spain, July 9-12, 2008.
Aitchison, J. 1986. The Statistical Analysis of Compositional Data. London: Chapman and Hall. 2015.
Ronald D. Lee and Lawrence R. Carter. 1992. Modeling and Forecasting U.S. Mortality, Journal of the American Statistical Association, 87:419, 659–671.
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