predict.MEM: Forecast the age-at-death distribution using the...

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

Description

Forecast the age-at-death distribution using the Maximum-Entropy Mortality Model

Usage

## S3 method for class 'MEM'
predict(object, h, x.h = NULL, level = 95,
  jumpchoice = c("actual", "fit"), verbose = FALSE, ...)

Arguments

object	An object of class MEM;
h	Number of years to be forecast in the future.
x.h	Numerical vector indicating the ages to be considered in reconstruction of the density over the forecast horizon. If NULL, the number of estimated data points is equal to the number of fitted data points. This argument can be used for example to estimate a density between 0 and 130 given the fact that the model was fitted on a dataset containing values for 0-100 only.
level	Significance level of the confidence interval.
jumpchoice	Method used for computation of jumpchoice. Possibilities: "actual" (use actual rates from final year) and "fit" (use fitted rates).
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.

Details

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pascariu_phd2018MortalityForecast

Value

The output is a list with the components:

call	An unevaluated function call, that is, an unevaluated expression which consists of the named function applied to the given arguments;
info	Short details about the model;
predicted.values	A list containing the predicted d[x] values given by the estimated model over the forecast horizon h;
conf.intervals	Confidence intervals for the predicted values;
predicted.raw.moments	Predicted raw moments over the forecast horizon h;
random.walk.model	The estimated multivariate Random Walk model used in the extrapolation of the moments;
x	Vector of ages used in prediction;
y	Vector of years used in prediction.

References

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See Also

model.MEM

Examples

# For examples go to ?model.MEM

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