fitted | R Documentation |
Extracts the fitted values 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' fitted(object, ...) ## S3 method for class 'sm' fitted(object, ...) ## S3 method for class 'gsm' fitted(object, ...)
object |
an object of class "gsm" output by the |
... |
other arugments (currently ignored) |
For objects of class ss
, fitted values are predicted via predict(object, object$data$x)$y
For objects of class sm
, fitted values are extracted via object$fitted.values
For objects of class gsm
, fitted values are computed via ginv(object$linear.predictors)
where ginv = object$family$linkinv
Fitted values extracted (or predicted) 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 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) # smooth model mod.sm <- sm(y ~ x, knots = 10) fit.sm <- fitted(mod.sm) # generalized smooth model (family = gaussian) mod.gsm <- gsm(y ~ x, knots = 10) fit.gsm <- fitted(mod.gsm) # compare fitted values mean((fit.ss - fit.sm)^2) mean((fit.ss - fit.gsm)^2) mean((fit.sm - fit.gsm)^2)
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