# plot.ss: Plot method for Smoothing Spline Fit and Bootstrap In npreg: Nonparametric Regression via Smoothing Splines

 plot.ss R Documentation

## 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 <helwig@umn.edu>

`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.