PFCS: Poisson mortality model fitting

In demofit: Parametric Mortality Curve Fitting and Mortality Forecasting Tools

Poisson mortality model fitting

Description

Fits and forecasts mortality rates using different stochastic mortality models with Poisson assumption.

Usage

PFCS(
  x,
  D,
  E,
  model = c("LC", "RH", "APC", "CBD", "CBDC", "CBDQC"),
  curve = NULL,
  h = 10,
  jumpoff = 1
)

Arguments

x	vector of ages.
D	matrix of death counts (rows as years and columns as ages).
E	matrix of mid-year exposures (rows as years and columns as ages).
model	name of stochastic mortality model (including LC, RH, APC, CBD, CBDC, and CBDQC).
curve	name of mortality curve for smoothing forecasted mortality rates (including gompertz, makeham, oppermann, thiele, wittsteinbumsted, perks, weibull, vandermaen, beard, heligmanpollard, rogersplanck, siler, martinelle, thatcher, gompertz2, makeham2, oppermann2, thiele2, wittsteinbumsted2, perks2, weibull2, vandermaen2, beard2, heligmanpollard2, rogersplanck2, siler2, martinelle2, thatcher2, where first 14 curves' parameters are unconstrained and last 14 curves' parameters are generally restricted to be positive) (optional; if missing, no smoothing is performed).
h	forecast horizon (default = 10).
jumpoff	if 1, forecasts are based on estimated parameters only; if 2, forecasts are anchored to observed mortality rates in final year (default = 1).

Details

See PLCS(), PRHS(), PAPCS(), PCBDS(), PCBDCS(), and PCBDQCS() for more details of different stochastic mortality models with Poisson assumption.

Value

An object of class based on selected stochastic mortality model with associated S3 methods coef, forecast, plot, and residuals.

Examples

x <- 60:89
a <- c(-4.8499,-4.7676,-4.6719,-4.5722,-4.4847,-4.3841,-4.2813,-4.1863,-4.0861,-3.9962,
-3.8885,-3.7896,-3.6853,-3.5737,-3.4728,-3.3718,-3.2586,-3.1474,-3.0371,-2.9206,
-2.7998,-2.6845,-2.5653,-2.4581,-2.3367,-2.2159,-2.1017,-1.9941,-1.8821, -1.7697)
b <- c(0.0283,0.0321,0.0335,0.0336,0.0341,0.0358,0.0368,0.0403,0.0392,0.0395,
0.0396,0.0399,0.0397,0.0386,0.039,0.0375,0.0367,0.0368,0.035,0.0354,
0.0336,0.0323,0.0313,0.0295,0.0282,0.0265,0.024,0.0226,0.0219,0.0183)
k <- c(12.11,10.69,11.18,9.64,9.35,8.21,6.89,5.74,4.56,3.6,
3.27,2.04,1.11,-0.44,-1.05,-1.03,-1.84,-2.9,-4.03,-4.12,
-5.18,-5.64,-6,-6.51,-6.91,-6.9,-8.32,-8.53,-9.69,-9.31)
set.seed(123)
M <- exp(outer(k,b)+matrix(a,nrow=30,ncol=30,byrow=TRUE)+rnorm(900,0,0.035))
E <- matrix(c(107788,108036,107481,106552,104608,100104,95803,91345,84980,79557,
75146,70559,65972,60898,55623,50522,47430,45895,41443,34774,
30531,27754,25105,22271,19437,16888,14458,12146,10038,7994),30,30,byrow=TRUE)
D <- round(E*M)
fit <- PFCS(x=x,D=D,E=E,model="LC",curve="makeham",h=30,jumpoff=2)
coef(fit)
forecast::forecast(fit)
plot(fit)
residuals(fit)

demofit documentation built on Aug. 20, 2026, 1:07 a.m.