loess.as: Fit a local polynomial regression with automatic smoothing...

In davidaarmstrong/damisc: Dave Armstrong's Miscellaneous Functions

Fit a local polynomial regression with automatic smoothing parameter selection

Description

Fit a local polynomial regression with automatic smoothing parameter selection. Two methods are available for the selection of the smoothing parameter: bias-corrected Akaike information criterion (aicc); and generalized cross-validation (gcv).

Usage

loess.as(
  x,
  y,
  degree = 1,
  criterion = c("aicc", "gcv"),
  family = c("gaussian", "symmetric"),
  user.span = NULL,
  plot = FALSE,
  ...
)

as.crit(x)

opt.span(model, criterion = c("aicc", "gcv"), span.range = c(0.05, 0.95))

Arguments

x	An object of class loess.
y	a vector of response values
degree	the degree of the local polynomials to be used. It can ben 0, 1 or 2.
criterion	The criterion used to find the optimal span
family	if "gaussian" fitting is by least-squares, and if "symmetric" a re-descending M estimator is used with Tukey's biweight function.
user.span	the user-defined parameter which controls the degree of smoothing.
plot	if TRUE, the fitted curve or surface will be generated.
...	control parameters. Fit a local polynomial regression with automatic smoothing parameter selection. The predictor x can either one-dimensional or two-dimensional. This function was taken directly from 'fANCOVA' version 0.5-1 and is wholly attributed to its author Xiao-Feng Wang.
model	An object of class loess.
span.range	The range in which to look for the optimal span

Value

An object of class loess.

Author(s)

X.F. Wang

Examples

## Fit Local Polynomial Regression with Automatic Smoothing Parameter Selection
n1 <- 100
x1 <- runif(n1,min=0, max=3)
sd1 <- 0.2
e1 <- rnorm(n1,sd=sd1)
y1 <- sin(2*x1) + e1
(y1.fit <- loess.as(x1, y1, plot=TRUE))

davidaarmstrong/damisc documentation built on Oct. 1, 2023, 3:05 p.m.