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.
Usage
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)Arguments
- .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.
Details
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.
References
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/
Examples
library(dplyr)
#>
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#>
#> filter, lag
#> The following objects are masked from ‘package:base’:
#>
#> intersect, setdiff, setequal, union
norway_mortality |>
filter(Sex == "Female", Year > 2000) |>
smooth_mortality(Mortality)
#> # A vital: 2,553 x 9 [1Y]
#> # Key: Age x Sex [111 x 1]
#> Year Age OpenInterval Sex Population Deaths Mortality .smooth .smooth_se
#> <int> <dbl> <lgl> <chr> <dbl> <dbl> <dbl> <dbl[1> <dbl[1d]>
#> 1 2001 0 FALSE Fema… 28805 97 0.00343 3.42e-3 0.000311
#> 2 2001 1 FALSE Fema… 29090 8 0.000274 2.71e-4 0.0000717
#> 3 2001 2 FALSE Fema… 28891 2 0.000069 1.64e-4 0.0000393
#> 4 2001 3 FALSE Fema… 29589 5 0.000172 1.32e-4 0.0000296
#> 5 2001 4 FALSE Fema… 30155 3 0.0001 1.16e-4 0.0000250
#> 6 2001 5 FALSE Fema… 29874 3 0.0001 1.07e-4 0.0000224
#> 7 2001 6 FALSE Fema… 29751 5 0.000167 1.01e-4 0.0000210
#> 8 2001 7 FALSE Fema… 29812 1 0.000034 9.64e-5 0.0000202
#> 9 2001 8 FALSE Fema… 29832 4 0.000134 9.45e-5 0.0000200
#> 10 2001 9 FALSE Fema… 30410 1 0.000033 9.58e-5 0.0000203
#> # ℹ 2,543 more rows
norway_fertility |>
filter(Year > 2000) |>
smooth_fertility(Fertility)
#> # A vital: 968 x 6 [1Y]
#> # Key: Age [44 x 1]
#> Year Age Fertility OpenInterval .smooth .smooth_se
#> <int> <dbl> <dbl> <lgl> <dbl> <dbl>
#> 1 2001 12 0 TRUE 0.00000601 0.0000299
#> 2 2001 13 0.00004 FALSE 0.0000262 0.000119
#> 3 2001 14 0.00019 FALSE 0.000114 0.000469
#> 4 2001 15 0.00046 FALSE 0.000498 0.00184
#> 5 2001 16 0.00217 FALSE 0.00217 0.00717
#> 6 2001 17 0.00752 FALSE 0.00751 0.0221
#> 7 2001 18 0.0158 FALSE 0.0164 0.0426
#> 8 2001 19 0.0291 FALSE 0.0269 0.0613
#> 9 2001 20 0.0404 FALSE 0.0391 0.0776
#> 10 2001 21 0.0502 FALSE 0.0502 0.0864
#> # ℹ 958 more rows