Forecast probabilities of smoking initiation, quitting and relapse

Forecasts trends in transition probabilities using a Lee-Carter style Singular Value Decomposition (SVD) model.

Usage

quit_forecast(
  data,
  forecast_var,
  forecast_type = c("continuing", "stationary"),
  cont_limit = NULL,
  oldest_year = 2003,
  youngest_age = 11,
  oldest_age = 88,
  age_cont_limit = 88,
  first_year = 2010,
  jump_off_year = 2015,
  time_horizon = 2050,
  smooth_rate_dim = c(3, 3),
  k_smooth_age = 3
)

Arguments

data

Data table with input probabilities.

forecast_var

Character - variable to forecast.

forecast_type

"continuing" (linear trend) or "stationary" (constant).

cont_limit

Integer - year where forecast becomes stationary.

oldest_year

Integer - start of historical data.

youngest_age

Integer - min age.

oldest_age

Integer - max age.

first_year

Integer - start year for trend fitting.

jump_off_year

Integer - end year of historical data.

time_horizon

Integer - end year of forecast.

smooth_rate_dim

Vector - dimensions for raster smoothing (c(3,3)).

k_smooth_age

Integer - knots for smoothing age component.

Details

The model assumes the logit of the probability can be decomposed into: Logit(P_xt) = Alpha_x + Beta_x * Kappa_t Where: - Alpha_x: Average age profile - Kappa_t: Time trend index - Beta_x: Sensitivity of each age to the time trend