smooth.lf: Local Regression, Likelihood and Density Estimation.

In locfit: Local Regression, Likelihood and Density Estimation

Local Regression, Likelihood and Density Estimation.

Description

smooth.lf is a simple interface to the Locfit library. The input consists of a predictor vector (or matrix) and response. The output is a list with vectors of fitting points and fitted values. Most locfit.raw options are valid.

Usage

smooth.lf(x, y, xev=x, direct=FALSE, ...)

Arguments

x	Vector (or matrix) of the independent variable(s).
y	Response variable. If omitted, x is treated as the response and the predictor variable is 1:n.
xev	Fitting Points. Default is the data vector x.
direct	Logical variable. If T, local regression is performed directly at each fitting point. If F, the standard Locfit method combining fitting and interpolation is used.
...	Other arguments to locfit.raw().

Value

A list with components x (fitting points) and y (fitted values). Also has a call component, so update() will work.

See Also

locfit(), locfit.raw(), density.lf().

Examples

# using smooth.lf() to fit a local likelihood model.
data(morths)
fit <- smooth.lf(morths$age, morths$deaths, weights=morths$n,
                 family="binomial")
plot(fit,type="l")

# update with the direct fit
fit1 <- update(fit, direct=TRUE)
lines(fit1,col=2)
print(max(abs(fit$y-fit1$y)))

locfit documentation built on July 9, 2023, 5:58 p.m.