coef | R Documentation |
Extracts basis function coefficients from a fit smoothing spline (fit by ss
), smooth model (fit by sm
), or generalized smooth model (fit by gsm
).
## S3 method for class 'gsm' coef(object, ...) ## S3 method for class 'sm' coef(object, ...) ## S3 method for class 'ss' coef(object, ...)
object |
an object of class "gsm" output by the |
... |
other arugments (currently ignored) |
For "ss" objects, the coefficient vector will be of length m + q where m
is the dimension of the null space and q is the number of knots used for the fit.
For "sm" and "gsm" objects, the coefficient vector will be of length m + q if the tprk = TRUE
(default). Otherwise the length will depend on the model formula and marginal knot placements.
Coefficients extracted from the model 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
model.matrix
, fitted.values
, residuals
# generate data set.seed(1) n <- 100 x <- seq(0, 1, length.out = n) fx <- 2 + 3 * x + sin(2 * pi * x) y <- fx + rnorm(n, sd = 0.5) # smoothing spline mod.ss <- ss(x, y, nknots = 10) fit.ss <- fitted(mod.ss) coef.ss <- coef(mod.ss) X.ss <- model.matrix(mod.ss) mean((fit.ss - X.ss %*% coef.ss)^2) # smooth model mod.sm <- sm(y ~ x, knots = 10) fit.sm <- fitted(mod.sm) coef.sm <- coef(mod.sm) X.sm <- model.matrix(mod.sm) mean((fit.sm - X.sm %*% coef.sm)^2) # generalized smooth model (family = gaussian) mod.gsm <- gsm(y ~ x, knots = 10) fit.gsm <- fitted(mod.gsm) coef.gsm <- coef(mod.gsm) X.gsm <- model.matrix(mod.gsm) mean((fit.gsm - X.gsm %*% coef.gsm)^2)
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