1d:T76b,# Extract Smooth Model Weights Extracts prior weights from a fit smoothing spline (fit by `ss`), smooth model (fit by `sm`), or generalized smooth model (fit by `gsm`). ```r ## 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)`. ## Returns Prior weights extracted from `object` ## 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") ## Author(s) Nathaniel E. Helwig ## See Also `ss`, `sm`, `gsm` ## Examples ```r # 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)) ```

weights function