plot.ss: Plot method for Smoothing Spline Fit and Bootstrap

In npreg: Nonparametric Regression via Smoothing Splines

Plot method for Smoothing Spline Fit and Bootstrap

Description

Default plotting methods for ss and boot.ss objects.

Usage

## S3 method for class 'ss'
plot(x, n = 201, ci = TRUE, xseq = NULL, ...)

## S3 method for class 'boot.ss'
plot(x, n = 201, ci = TRUE, xseq = NULL, ...)

Arguments

x	an object of class 'ss' or 'boot.ss'
n	number of points used to plot smoothing spline estimate
ci	logical indicating whether to include a confidence interval
xseq	ordered sequence of points at which to plot smoothing spline estimate
...	optional additional argument for the plotci function, e.g., level, col, etc.

Details

Unless a sequence of points is provided via the xseq arugment, the plots are created by evaluating the smoothing spline fit at an equidistant sequence of n values that span the range of the training data.

Value

Plot of the function estimate and confidence interval with the title displaying the effective degrees of freedom.

Note

The plot.ss and plot.boot.ss functions produce plots that only differ in terms of their confidence intervals: plot.ss uses the Bayesian CIs, whereas plot.boot.ss uses the bootstrap CIs.

Author(s)

Nathaniel E. Helwig <[e-mail]>

See Also

ss and boot.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)

# fit smoothing spline
ssfit <- ss(x, y, nknots = 10)

# plot smoothing spline fit
plot(ssfit)

## Not run: 

# bootstrap smoothing spline
ssfitboot <- boot(ssfit)

# plot smoothing spline bootstrap
plot(ssfitboot)

## End(Not run)

npreg documentation built on July 21, 2022, 1:06 a.m.