1. quantile, probs = 0.025) mxtPred97.5 <-apply(LCsim_NZ$rates, c(1, 2), quantile, probs = 0.975) #95% intervals with parameter uncertainty (in sample, and predictions) mxtHatPU2.5 <-apply(LCsimPU_NZ$fitted, c(1, 2), quantile, probs = 0.025) mxtHatPU97.5 <-apply(LCsimPU_NZ$fitted, c(1, 2), quantile, probs = 0.975) mxtPredPU2.5 <-apply(LCsimPU_NZ$rates, c(1, 2), quantile, probs = 0.025) mxtPredPU97.5 <-apply;central forecasts mxt <-LCfit_NZ$Dxt / LCfit_NZ$Ext mxtHat <-fitted(LCfit_NZ, type = "rates") mxtCentral <-LCfor_NZ$rates #95% Prediction intervals without parameter uncertainty mxtPred2.5 <-apply(LCsim_NZ$rates, c(1, 2)
2. lty = 5, col = "red") matlines(LCfit_NZ$years, t(mxtHatPU97.5[x, ]), lty = 5, col = "red") matlines(LCfor_NZ$years, t(mxtCentral[x, ]), lty = 4, col = "black") matlines(LCsim_NZ$years;matlines(LCfit_NZ$years, t(mxtHat[x, ]), lty = 1, col = "black") matlines(LCfit_NZ$years, t(mxtHatPU2.5
3. Rethinking age-period-cohort mortality trend models;D H Alai;Scandinavian Actuarial Journal,2014
4. A user-friendly approach to stochastic mortality modelling;H Aro;European Actuarial Journal,2011
5. Prospective life tables;H Booth;Computational Actuarial Science with R,2014