model.OeppenC: The Coherent Oeppen Mortality Model (Oeppen-C)

In mpascariu/MortalityForecast: Standard Tools to Compare and Evaluate Various Mortality Forecasting Methods

Description

The Coherent Oeppen Mortality Model (Oeppen-C)

Usage

model.OeppenC(data, data.B, x = NULL, y = NULL, verbose = TRUE, ...)

Arguments

data	A data.frame or a matrix containing mortality data with ages x as row and time y as column.
data.B	A data.frame or a matrix containing mortality data for the benchmark population. Must be the same format as in data;
x	Numerical vector indicating the ages in input data. Optional. Default: NULL.
y	Numerical vector indicating the years in input data. Optional. Default: NULL.
verbose	A logical value. Set verbose = FALSE to silent the process that take place inside the function and avoid progress messages.
...	Arguments to be passed to or from other methods.

Value

The output is a list with the components:

input	List with arguments provided in input. Saved for convenience;
info	Short details about the model;
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 model;
observed.values	The observed values used in fitting arranged in the same format as the fitted.values;
residuals	Deviance residuals;
x	Vector of ages used in the fitting;
y	Vector of years used in the fitting;
benchmark	An object of class LeeCarter containing the fitted model for the benchmark population.

See Also

predict.Oeppen plot.Oeppen

Examples

# Example 1 ----------------------
# Data
x  <- 0:110
y  <- 1980:2016
dx_M <- HMD_male$dx$USA[paste(x), paste(y)]
dx_F <- HMD_female$dx$USA[paste(x), paste(y)]

# Replace zeros 
dx_M <- replace.zeros(dx_M)
dx_F <- replace.zeros(dx_F)

# Fit model for US males using US females as benchmark
M <- model.OeppenC(data = dx_M,
                   data.B = dx_F,
                   x = x, y = y)
M

summary(M)
coef(M)
coef(M$benchmark)

# Plot observed and fitted values
plot(M, plotType = "observed")
plot(M, plotType = "fitted")

# Plot residuals
R <- residuals(M)
plot(R, plotType = "scatter")
plot(R, plotType = "colourmap")
plot(R, plotType = "signplot")

# Perform forecasts
P  <- predict(M, h = 16)
P

plot(P, plotType = "mean")
plot(P, plotType = "lower")
plot(P, plotType = "upper")

mpascariu/MortalityForecast documentation built on Sept. 28, 2020, 2:40 p.m.