mspline: Vector Smoothing Spline

In mda: Mixture and Flexible Discriminant Analysis

Vector Smoothing Spline

Description

Fit a smoothing spline to a matrix of responses, single x.

Usage

mspline(x, y, w, df = 5, lambda, thresh = 1e-04, ...)

Arguments

x	x variable (numeric vector).
y	response matrix.
w	optional weight vector, defaults to a vector of ones.
df	requested degrees of freedom, as in smooth.spline.
lambda	can provide penalty instead of df.
thresh	convergence threshold for df inversion (to lambda).
...	holdall for other arguments.

Details

This function is based on the ingredients of smooth.spline, and allows for simultaneous smoothing of multiple responses

Value

A list is returned, with a number of components, only some of which are of interest. These are

lambda	The value of lambda used (in case df was supplied)
df	The df used (in case lambda was supplied)
s	A matrix like y of smoothed responses
lev	Self influences (diagonal of smoother matrix)

Author(s)

Trevor Hastie

See Also

smooth.spline

Examples

x=rnorm(100)
y=matrix(rnorm(100*10),100,10)
fit=mspline(x,y,df=5)

mda documentation built on July 9, 2023, 7:14 p.m.