lca {demography} | R Documentation |
Model mortality or fertility data using Lee-Carter approach
Description
Lee-Carter model of mortality or fertility rates. lca
produces a
standard Lee-Carter model by default, although many other options are
available. bms
is a wrapper for lca
and returns a model based
on the Booth-Maindonald-Smith methodology.
Usage
lca(
data,
series = names(data$rate)[1],
years = data$year,
ages = data$age,
max.age = 100,
adjust = c("dt", "dxt", "e0", "none"),
chooseperiod = FALSE,
minperiod = 20,
breakmethod = c("bai", "bms"),
scale = FALSE,
restype = c("logrates", "rates", "deaths"),
interpolate = FALSE
)
bms(
data,
series = names(data$rate)[1],
years = data$year,
ages = data$age,
max.age = 100,
minperiod = 20,
breakmethod = c("bms", "bai"),
scale = FALSE,
restype = c("logrates", "rates", "deaths"),
interpolate = FALSE
)
Arguments
data |
demogdata object of type “mortality” or “fertility”. Output from read.demogdata. |
series |
name of series within data containing mortality or fertility values (1x1) |
years |
years to include in fit. Default: all available years. |
ages |
ages to include in fit. Default: all available ages up to
|
max.age |
upper age to include in fit. Ages beyond this are collapsed into the upper age group. |
adjust |
method to use for adjustment of coefficients |
chooseperiod |
If TRUE, it will choose the best fitting period. |
minperiod |
Minimum number of years to include in fitting period if chooseperiod=TRUE. |
breakmethod |
method to use for identifying breakpoints if
chooseperiod=TRUE. Possibilities are “bai” (Bai's method computed
using |
scale |
If TRUE, it will rescale bx and kt so that kt has drift parameter = 1. |
restype |
method to use for calculating residuals. Possibilities are “logrates”, “rates” and “deaths”. |
interpolate |
If TRUE, it will estimate any zero mortality or fertility rates using the same age group from nearby years. |
Details
All mortality or fertility data are assumed to be in matrices of
mortality or fertility rates within data$rate
. Each row is one age group
(assumed to be single years). Each column is one year. The
function produces a model for the series
mortality or fertility rate matrix
within data$rate
. Forecasts from this model can be obtained using forecast.lca
.
Value
Object of class “lca” with the following components:
label |
Name of region |
age |
Ages from |
year |
Years from |
<series> |
Matrix of mortality or fertility data as contained in |
ax |
Average deathrates across fitting period |
bx |
First principal component in Lee-Carter model |
kt |
Coefficient of first principal component |
residuals |
Functional time series of residuals. |
fitted |
Functional time series containing estimated mortality or fertility rates from model |
varprop |
Proportion of variance explained by model. |
y |
The data stored as a functional time series object. |
mdev |
Mean deviance of total and base lack of fit, as described in Booth, Maindonald and Smith. |
Author(s)
Heather Booth, Leonie Tickle, John Maindonald and Rob J Hyndman.
References
Booth, H., Maindonald, J., and Smith, L. (2002) Applying Lee-Carter under conditions of variable mortality decline. Population Studies, 56, 325-336.
Lee, R.D., and Carter, L.R. (1992) Modeling and forecasting US mortality. Journal of the American Statistical Association, 87, 659-671.
See Also
forecast.lca
, plot.lca
, summary.lca
, fdm
Examples
## Not run:
france.LC1 <- lca(fr.mort, adjust="e0")
plot(france.LC1)
par(mfrow=c(1,2))
plot(fr.mort,years=1953:2002,ylim=c(-11,1))
plot(forecast(france.LC1,jumpchoice="actual"),ylim=c(-11,1))
france.bms <- bms(fr.mort, breakmethod="bai")
fcast.bms <- forecast(france.bms)
par(mfrow=c(1,1))
plot(fcast.bms$kt)
## End(Not run)