predict.ss {npreg}

Predict method for Smoothing Spline Fits

Description

predict method for class "ss".

Usage

## S3 method for class 'ss'
predict(object, x, deriv = 0, se.fit = TRUE, ...)

Arguments

object

a fit from ss.

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).

Details

Inspired by the predict.smooth.spline function in R's stats package.

Value

A list with components

x

The input x.

y

The fitted values or derivatives at x.

se

The standard errors of the fitted values or derivatives (if requested).

Author(s)

Nathaniel E. Helwig <[e-mail]>

References

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

See Also

ss

Examples

# 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