smoothfun: Smoothed Function

In obreschkow/cooltools: Practical Tools for Scientific Computations and Visualizations

Smoothed Function

Description

Generates a cubic smoothed spline function y=f(x) approximating supplied (x,y)-data with a custom number of degrees of freedom. The routines builds on smooth.spline, but directly returns the smoothed function rather than the fitted model.

Usage

smoothfun(x, y = NULL, w = NULL, df = NULL, ...)

Arguments

x	a vector giving the values of the predictor variable, or a list or a two-column matrix specifying x and y.
y	responses. If y is missing or NULL, the responses are assumed to be specified by x, with x the index vector.
w	optional vector of weights of the same length as x; defaults to all 1.
df	the desired equivalent number of degrees of freedom. Must be in [2,nx], where nx is the number of unique x values. If not given, nx is set to the square root of the number of unique x-values.
...	additional optional arguments used by smooth.spline.

Value

Returns a fast and vectorized smoothed function f(x).

Author(s)

Danail Obreschkow

See Also

smooth.spline

Examples

# make random data set
set.seed(1)
x = runif(100)
y = sin(2*pi*x)+rnorm(100, sd=0.5)
plot(x,y,pch=16)

# smoothed spline
f = smoothfun(x, y)
curve(f, add=TRUE, col='red')

# smoothed spline with custom degree of freedom
g = smoothfun(x, y, df=5)
curve(g, add=TRUE, col='blue')

obreschkow/cooltools documentation built on Nov. 16, 2024, 2:46 a.m.