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. \Sexpr[results=rd]{tools:::Rd_expr_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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