demography: fdm – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

fdm

Functional demographic model

Description

Fits a basis function model to demographic data. The function uses optimal orthonormal basis functions obtained from a principal components decomposition.

Usage

fdm(data, series = names(data$rate)[1], order = 6, ages = data$age,
  max.age = max(ages), method = c("classical", "M", "rapca"),
  lambda = 3, mean = TRUE, level = FALSE, transform = TRUE, ...)

Arguments

`data`	demogdata object. Output from read.demogdata.
`series`	name of series within data holding rates (1x1).
`order`	Number of basis functions to fit.
`ages`	Ages to include in fit.
`max.age`	Maximum age to fit. Ages beyond this are collapsed into the upper age group.
`method`	Method to use for principal components decomposition. Possibilities are “M”, “rapca” and “classical”. See `ftsm` for details.
`lambda`	Tuning parameter for robustness when `method="M"`.
`mean`	If TRUE, will estimate mean term in the model before computing basis terms. If FALSE, the mean term is assumed to be zero.
`level`	If TRUE, will include an additional (intercept) term that depends on the year but not on ages.
`transform`	If TRUE, the data are transformed with a Box-Cox transformation before the model is fitted.
`...`	Extra arguments passed to `ftsm`.

Value

Object of class “fdm” with the following components:

`label`	Name of country
`age`	Ages from `data` object.
`year`	Years from `data` object.
`<series>`	Matrix of demographic data as contained in `data`. It takes the name given by the series argument.
`fitted`	Matrix of fitted values.
`residuals`	Residuals (difference between observed and fitted).
`basis`	Matrix of basis functions evaluated at each age level (one column for each basis function). The first column is the fitted mean.
`coeffs`	Matrix of coefficients (one column for each coefficient series). The first column are all ones.
`mean.se`	Standard errors for the estimated mean function.
`varprop`	Proportion of variation explained by each basis function.
`weights`	Weight associated with each time period.
`v`	Measure of variation for each time period.
`type`	Data type (mortality, fertility, etc.)
`y`	The data stored as a functional time series object.

Author(s)

Rob J Hyndman

References

Hyndman, R.J., and Ullah, S. (2007) Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51, 4942-4956. http://robjhyndman.com/papers/funcfor

Examples

france.fit <- fdm(fr.mort)
summary(france.fit)
plot(france.fit)
plot(residuals(france.fit))

demography

Forecasting Mortality, Fertility, Migration and Population Data

v1.22

GPL (>= 2)

Authors

Rob J Hyndman with contributions from Heather Booth, Leonie Tickle and John Maindonald.

Initial release

fdm