loess.as {DAMisc} | R Documentation |
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).
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))
x |
An object of class |
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 |
... |
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 |
span.range |
The range in which to look for the optimal span |
An object of class loess
.
X.F. Wang
## 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))