The function generates a fan plot representing the forecast death rates based on the ggplot2 and
ggfan R packages.
forecasting_plot(stan_fit, ages, years, death, exposure, ages.plot)
Arguments
| stan_fit | stanfit object |
|---|---|
| ages | range of ages |
| years | range of years |
| death | matrix of observed deaths |
| exposure | matrix of observed exposures |
| ages.plot | ages to be plotted |
Value
A ggplot object that can be further customized using the ggplot2 package.
Examples
# \donttest{ years <- 1980:2017 ages <- 50:90 death <- FRMaleData$Dxt[formatC(ages),formatC(years)] exposure <- FRMaleData$Ext[formatC(ages),formatC(years)] iterations<-1000 # Toy example, consider at least 2000 iterations fitLC<-lc_stan(death = death,exposure=exposure, forecast = 10, family = "poisson",chains=1,cores=1,iter=iterations) #> #> SAMPLING FOR MODEL 'leecarter' NOW (CHAIN 1). #> Chain 1: #> Chain 1: Gradient evaluation took 0 seconds #> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds. #> Chain 1: Adjust your expectations accordingly! #> Chain 1: #> Chain 1: #> Chain 1: Iteration: 1 / 1000 [ 0%] (Warmup) #> Chain 1: Iteration: 100 / 1000 [ 10%] (Warmup) #> Chain 1: Iteration: 200 / 1000 [ 20%] (Warmup) #> Chain 1: Iteration: 300 / 1000 [ 30%] (Warmup) #> Chain 1: Iteration: 400 / 1000 [ 40%] (Warmup) #> Chain 1: Iteration: 500 / 1000 [ 50%] (Warmup) #> Chain 1: Iteration: 501 / 1000 [ 50%] (Sampling) #> Chain 1: Iteration: 600 / 1000 [ 60%] (Sampling) #> Chain 1: Iteration: 700 / 1000 [ 70%] (Sampling) #> Chain 1: Iteration: 800 / 1000 [ 80%] (Sampling) #> Chain 1: Iteration: 900 / 1000 [ 90%] (Sampling) #> Chain 1: Iteration: 1000 / 1000 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 18.603 seconds (Warm-up) #> Chain 1: 4.938 seconds (Sampling) #> Chain 1: 23.541 seconds (Total) #> Chain 1: forecasting_plot(fitLC,ages,years,death,exposure,c(65,75,85))# }