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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