npreg (version 1.1.0)

weights: 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 ss
weights(object, ...)
# S3 method for sm
weights(object, ...)
# S3 method for gsm
weights(object, ...)

Value

Prior weights extracted from 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)

Author

Nathaniel E. Helwig <[e-mail]>

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).

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. 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))