Description Usage Arguments Details Value Author(s) See Also Examples

Functions for computing residuals from the observed life expectancy and MCMC estimation, and fitting a local polynomial regression.

1 2 3 | ```
compute.residuals(sim.dir, burnin = 1000)
compute.loess(sim.dir = NULL, burnin = 1000, residuals = NULL)
``` |

`sim.dir` |
Directory with the MCMC estimation. In |

`burnin` |
Number of (unthinned) iterations to be discarded. In |

`residuals` |
Residuals can be computed outside of the |

The Bayesian hierarchical model for life expectancy uses a lowess curve as a multiplier of the variance. The dataset is stored in the package as the `loess_sd`

dataset. These functions can be used to re-compute this `loess_sd`

dataset. In such a case, the simulation should be run with the argument `constant.variance = TRUE`

(in `run.e0.mcmc`

).

The residuals are computed for each country as the absolute differences between the observed life expectancy increases and the mean of the estimated double logistic function at the corresponding life expectancy level.

`compute.residuals`

returns a data frame with columns ‘x’ (life expectancy levels) and ‘y’ (absolute residuals).

`compute.loess`

also returns a data frame with columns ‘x’ and ‘y’, where ‘x’ is the same as before (with added a minimum and maximum) and ‘y’ is the local polynomial fit with constant tails.

Hana Sevcikova

1 2 3 4 5 6 7 | ```
sim.dir <- file.path(find.package("bayesLife"), "ex-data", "bayesLife.output")
resid <- compute.residuals(sim.dir, burnin = 30)
lws <- compute.loess(residuals = resid)
# plot residuals and loess
plot(resid$x, resid$y, ylim = c(0, 4))
lines(lws$x, lws$y, col = "red")
``` |

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