Wrapper function to fit and forecast mortality models
fit_mo_mo( mortality_model = "lc", death, exposure, ages = 50:90, validation = 0, forecast = 1, family = "nb", chains = 1, cores = 4, log_marg = FALSE, iter = 2000 )
Arguments
| mortality_model | name of the mortality model |
|---|---|
| death | death matrix |
| exposure | exposure matrix |
| ages | vector of ages |
| validation | size of the validation set |
| forecast | number of calendar years to be forecast |
| family | underlying count distribution |
| chains | number of Markov chains |
| cores | number of cores used |
| log_marg | Do we compute the marginal likelihood or not? |
| iter | Length of the Markov chain trajectory |
Value
a stanfit object
Examples
years <- 1990:2017 ages <- 50:90 cohorts <- sort(unique(as.vector(sapply(years, function(year) year - ages)))) death <- FRMaleData$Dxt[formatC(ages),formatC(years)] exposure <- FRMaleData$Ext[formatC(ages),formatC(years)] stan_fit <- fit_mo_mo("m6", death , exposure, ages, 0, 5, "nb", 1, 4, log_marg = FALSE,iter=50) #> #> SAMPLING FOR MODEL 'M6model' 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: WARNING: There aren't enough warmup iterations to fit the #> Chain 1: three stages of adaptation as currently configured. #> Chain 1: Reducing each adaptation stage to 15%/75%/10% of #> Chain 1: the given number of warmup iterations: #> Chain 1: init_buffer = 3 #> Chain 1: adapt_window = 20 #> Chain 1: term_buffer = 2 #> Chain 1: #> Chain 1: Iteration: 1 / 50 [ 2%] (Warmup) #> Chain 1: Iteration: 5 / 50 [ 10%] (Warmup) #> Chain 1: Iteration: 10 / 50 [ 20%] (Warmup) #> Chain 1: Iteration: 15 / 50 [ 30%] (Warmup) #> Chain 1: Iteration: 20 / 50 [ 40%] (Warmup) #> Chain 1: Iteration: 25 / 50 [ 50%] (Warmup) #> Chain 1: Iteration: 26 / 50 [ 52%] (Sampling) #> Chain 1: Iteration: 30 / 50 [ 60%] (Sampling) #> Chain 1: Iteration: 35 / 50 [ 70%] (Sampling) #> Chain 1: Iteration: 40 / 50 [ 80%] (Sampling) #> Chain 1: Iteration: 45 / 50 [ 90%] (Sampling) #> Chain 1: Iteration: 50 / 50 [100%] (Sampling) #> Chain 1: #> Chain 1: Elapsed Time: 0.812 seconds (Warm-up) #> Chain 1: 0.048 seconds (Sampling) #> Chain 1: 0.86 seconds (Total) #> Chain 1: boxplot_post_dist(stan_fit, "k", ages, years)boxplot_post_dist(stan_fit, "g", ages, years)
