weights | R Documentation |
Extracts prior weights from a fit smoothing spline (fit by ss
), smooth model (fit by sm
), or generalized smooth model (fit by gsm
).
## S3 method for class 'ss' weights(object, ...) ## S3 method for class 'sm' weights(object, ...) ## S3 method for class 'gsm' weights(object, ...)
object |
an object of class "gsm" output by the |
... |
other arugments (currently ignored) |
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)
.
Prior weights extracted from object
Nathaniel E. Helwig <helwig@umn.edu>
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. doi: 10.4135/9781526421036885885
ss
, sm
, gsm
# 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))
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