locfit: Local Regression, Likelihood and Density Estimation.

Description

locfit is the model formula-based interface to the Locfit library for fitting local regression and likelihood models.

locfit is implemented as a front-end to locfit.raw. See that function for options to control smoothing parameters, fitting family and other aspects of the fit.

Usage

locfit(formula, data=sys.frame(sys.parent()), weights=1, cens=0, base=0,
       subset, geth=FALSE, ..., lfproc=locfit.raw)

Value

An object with class "locfit". A standard set of methods for printing, ploting, etc. these objects is provided.

Arguments

formula: Model Formula; e.g. y~lp(x) for a regression model; ~lp(x) for a density estimation model. Use of lp() on the RHS is recommended, especially when non-default smoothing parameters are used.
data: Data Frame.
weights: Prior weights (or sample sizes) for individual observations. This is typically used where observations have unequal variance.
cens: Censoring indicator. 1 (or TRUE) denotes a censored observation. 0 (or FALSE) denotes uncensored.
base: Baseline for local fitting. For local regression models, specifying a base is equivalent to using y-base as the reponse. But base also works for local likelihood.
subset: Subset observations in the data frame.
geth: Don't use.
...: Other arguments to locfit.raw() (or the lfproc).
lfproc: A processing function to compute the local fit. Default is locfit.raw(). Other choices include locfit.robust(), locfit.censor() and locfit.quasi().

References

Loader, C. (1999). Local Regression and Likelihood. Springer, New York.

Examples

Run this code

# fit and plot a univariate local regression
data(ethanol, package="locfit")
fit <- locfit(NOx ~ E, data=ethanol)
plot(fit, get.data=TRUE)

# a bivariate local regression with smaller smoothing parameter
fit <- locfit(NOx~lp(E,C,nn=0.5,scale=0), data=ethanol)
plot(fit)

# density estimation
data(geyser, package="locfit")
fit <- locfit( ~ lp(geyser, nn=0.1, h=0.8))
plot(fit,get.data=TRUE)