predict.ss | R Documentation |
predict
method for class "ss".
## S3 method for class 'ss' predict(object, x, deriv = 0, se.fit = TRUE, ...)
object |
a fit from |
x |
the new values of x. |
deriv |
integer; the order of the derivative required. |
se.fit |
a switch indicating if standard errors are required. |
... |
additional arguments affecting the prediction produced (currently ignored). |
Inspired by the predict.smooth.spline
function in R's stats package.
A list with components
x |
The input |
y |
The fitted values or derivatives at x. |
se |
The standard errors of the fitted values or derivatives (if requested). |
Nathaniel E. Helwig <helwig@umn.edu>
https://stat.ethz.ch/R-manual/R-devel/library/stats/html/predict.smooth.spline.html
Craven, P. and Wahba, G. (1979). Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik, 31, 377-403. doi: 10.1007/BF01404567
Gu, C. (2013). Smoothing spline ANOVA models, 2nd edition. New York: Springer. doi: 10.1007/978-1-4614-5369-7
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
# 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) # GCV selection (default) ss.GCV <- ss(x, y, nknots = 10) # get predictions and SEs (at design points) fit <- predict(ss.GCV, x = x) head(fit) # compare to original fit mean((fit$y - ss.GCV$y)^2) # plot result (with default 95% CI) plotci(fit) # estimate first derivative d1 <- 3 + 2 * pi * cos(2 * pi * x) fit <- predict(ss.GCV, x = x, deriv = 1) head(fit) # plot result (with default 95% CI) plotci(fit) lines(x, d1, lty = 2) # truth
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