| smooth_spline | R Documentation | 
These smoothing functions allow smoothing of a variable in a vital object.
The vital object is returned along with some additional columns containing
information about the smoothed variable: usually .smooth containing the
smoothed values, and .smooth_se containing the corresponding standard errors.
smooth_spline(.data, .var, age_spacing = 1, k = -1)
smooth_mortality(.data, .var, age_spacing = 1, b = 65, power = 0.4, k = 30)
smooth_fertility(.data, .var, age_spacing = 1, lambda = 1e-10)
smooth_loess(.data, .var, age_spacing = 1, span = 0.2)
| .data | A vital object | 
| .var | name of variable to smooth | 
| age_spacing | Spacing between ages for smoothed vital. Default is 1. | 
| k | Number of knots to use for penalized regression spline estimate. | 
| b | Lower age for monotonicity. Above this, the smooth curve is assumed to be monotonically increasing. | 
| power | Power transformation for age variable before smoothing. Default is 0.4 (for mortality data). | 
| lambda | Penalty for constrained regression spline. | 
| span | Span for loess smooth. | 
smooth_mortality() use penalized regression splines applied to log mortality
with a monotonicity constraint above age b. The methodology is based on Wood (1994).
smooth_fertility() uses weighted regression B-splines with a concavity constraint,
based on He and Ng (1999). The function smooth_loess() uses locally quadratic
regression, while smooth_spline() uses penalized regression splines.
vital with added columns containing smoothed values and their standard errors
Rob J Hyndman
Hyndman, R.J., and Ullah, S. (2007) Robust forecasting of mortality and fertility rates: a functional data approach. Computational Statistics & Data Analysis, 51, 4942-4956. https://robjhyndman.com/publications/funcfor/
library(dplyr)
norway_mortality |>
  filter(Sex == "Female", Year > 2000) |>
  smooth_mortality(Mortality)
norway_fertility |>
  filter(Year > 2000) |>
  smooth_fertility(Fertility)
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