R4GoodPersonalFinances (version 1.2.0)

plot_scenarios: Plot scenarios metrics

Description

The plot allows to compare metrics for multiple scenarios.

If scenarios are simulated without Monte Carlo samples, so they are based only on expected returns of portfolio, two metrics are available for each scenario:

• constant discretionary spending - certainty equivalent constant level of consumption that would result in the same lifetime utility as a given series of future consumption in a given scenario (the higher, the better).

• utility of discretionary spending - normalized to minimum and maximum values of constant discretionary spending (the higher, the better).

If scenarios are simulated with additional Monte Carlo samples, there are four more metrics available per scenario:

• constant discretionary spending (for Monte Carlo samples),

• normalized median utility of discretionary spending (for Monte Carlo samples),

• median of missing funds that need additional income or additional savings at the expense of non-discretionary spending, (of yearly averages of Monte Carlo samples),

• median of discretionary spending (of yearly averages of Monte Carlo samples).

Usage

plot_scenarios(scenarios, period = c("yearly", "monthly"))

Value

A ggplot2::ggplot() object.

Arguments

scenarios

A tibble with nested columns - the result of simulate_scenarios().

period

A character. The amounts can be shown as yearly values (default) or averaged per month values.

Examples

if (FALSE) { # interactive()
older_member <- HouseholdMember$new(
  name       = "older",  
  birth_date = "1980-02-15",
  mode       = 80,
  dispersion = 10
)  
household <- Household$new()
household$add_member(older_member)  

household$expected_income <- list(
  "income" = c(
    "is_not_on('older', 'retirement') ~ 7000 * 12"
  )
)
household$expected_spending <- list(
  "spending" = c(
    "TRUE ~ 4000 * 12"
  )
)

portfolio <- create_portfolio_template() 
portfolio$accounts$taxable <- c(100000, 300000)
portfolio <- 
  portfolio |> 
  calc_effective_tax_rate(
    tax_rate_ltcg = 0.20, 
    tax_rate_ordinary_income = 0.40
  )

start_ages <- c(60, 65, 75)
scenarios_parameters <- 
  tibble::tibble(
    member    = "older",
    event      = "retirement",
    start_age = start_ages,
    years     = Inf,
    end_age   = Inf
   ) |> 
  dplyr::mutate(scenario_id = start_age) |> 
  tidyr::nest(events = -scenario_id)

scenarios <- 
  simulate_scenarios(
    scenarios_parameters = scenarios_parameters,
    household            = household,
    portfolio            = portfolio,
    maxeval              = 100,
    current_date         = "2020-07-15"
  )

plot_scenarios(scenarios, "monthly")
}