smooth.demogdata: Create smooth demogdata functions

Description

Smooth demogdata data using one of four methods depending on the value of method

Usage

smooth.demogdata(data, method = switch(data$type, mortality = "mspline",
  fertility = "cspline", migration = "loess"), age.grid,
  power = switch(data$type, mortality = 0.4, fertility = 1, migration =
  1), b = 65, k = 30, span = 0.2, lambda = 1e-10,
  interpolate = FALSE, weight = data$type != "migration",
  obs.var = "empirical")

Value

Demogdata object identical to data except all rate matrices are replaced with smooth versions and pop matrices are replaced with disaggregated population estimates obtained using monotonic spline interpolation applied to the cumulative population data. Weight matrices are also added to the object showing the inverse variances of the estimated smooth curves.

Arguments

data: Demogdata object such as created using read.demogdata.
method: Method of smoothing. Possibilities: "mspline" (monotonic regression splines), "cspline" (concave regression splines), "spline" (unconstrained regression splines), "loess" (local quadratic using loess).
age.grid: Ages to use for smoothed curves. Default is single years over a slightly greater range than the unsmoothed data.
power: Power transformation for age variable before smoothing. Default is 0.4 for mortality data and 1 (no transformation) for fertility or migration data.
b: Lower age for monotonicity if method=="mspline". Above this, the smooth curve is assumed to be monotonically increasing.
k: Number of knots to use for penalized regression spline estimate. Ignored if method=="loess".
span: Span for loess smooth if method=="loess".
lambda: Penalty for constrained regression spline if method=="cspline".
interpolate: If interpolate==TRUE, a linear interpolation is used instead of smoothing.
weight: If TRUE, uses weighted smoothing.
obs.var: Method for computing observational variance. Possible values: “empirical” or “theoretical”.

Author

Rob J Hyndman

Details

The value of method determines the type of smoothing used.

method="mspline": Weighted penalized regression splines with a monotonicity constraint. The curves are monotonically increasing for age greater than b. Smoothness controlled by k. Methodology based on Wood (1994). Code calls gam for the basic computations.
method="cspline": Weighted regression B-splines with a concavity constraint. Smoothness controlled by lambda. Methodology based on He and Ng (1999). Code calls cobs for the basic computations.
method="spline": Unconstrained weighted penalized regression splines. Equivalent to "mspline" but with b=Inf.
method="loess": Weighted locally quadratic regression. Smoothness controlled by span. Code calls loess for the basic computations.

Examples

Run this code

france.sm <- smooth.demogdata(extract.years(fr.mort,1980:1997))
plot(france.sm)
plot(fr.mort,years=1980,type="p",pch=1)
lines(france.sm,years=1980,col=2)

Run the code above in your browser using DataCamp Workspace