weights: Extract Smooth Model Weights

In npreg: Nonparametric Regression via Smoothing Splines

Extract Smooth Model Weights

Description

Extracts prior weights from a fit smoothing spline (fit by ss), smooth model (fit by sm), or generalized smooth model (fit by gsm).

Usage

## S3 method for class 'ss'
weights(object, ...)

## S3 method for class 'sm'
weights(object, ...)

## S3 method for class 'gsm'
weights(object, ...)

Arguments

object	an object of class "gsm" output by the gsm function, "sm" output by the sm function, or "ss" output by the ss function
...	other arugments (currently ignored)

Details

Returns the "prior weights", which are user-specified via the w argument (of the ss function) or the weights argument (of the sm and gsm functions). If no prior weights were supplied, returns the (default) unit weights, i.e., rep(1, nobs).

Value

Prior weights extracted from object

Author(s)

Nathaniel E. Helwig <[e-mail]>

References

Chambers, J. M. and Hastie, T. J. (1992) Statistical Models in S. Wadsworth & Brooks/Cole.

Helwig, N. E. (2020). Multiple and Generalized Nonparametric Regression. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), SAGE Research Methods Foundations. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.4135/9781526421036885885")}

See Also

ss, sm, gsm

Examples

# generate weighted data
set.seed(1)
n <- 100
x <- seq(0, 1, length.out = n)
w <- rep(5:15, length.out = n)
fx <- 2 + 3 * x + sin(2 * pi * x)
y <- fx + rnorm(n, sd = 0.5 / sqrt(w))

# smoothing spline
mod.ss <- ss(x, y, w, nknots = 10)
w.ss <- weights(mod.ss)

# smooth model
mod.sm <- sm(y ~ x, weights = w, knots = 10)
w.sm <- weights(mod.sm)

# generalized smooth model (family = gaussian)
mod.gsm <- gsm(y ~ x, weights = w, knots = 10)
w.gsm <- weights(mod.gsm)

# note: weights are internally rescaled such as 
w0 <- w / mean(w)
max(abs(w0 - w.ss))
max(abs(w0 - w.sm))
max(abs(w0 - w.gsm))

npreg documentation built on May 29, 2024, 4:17 a.m.