locpol {locpol} | R Documentation |
Formula interface for the local polynomial estimation.
locpol(formula,data,weig=rep(1,nrow(data)),bw=NULL,kernel=EpaK,deg=1, xeval=NULL,xevalLen=100) confInterval(x) ## S3 method for class 'locpol': residuals(object,...) ## S3 method for class 'locpol': fitted(object,deg=0,...) ## S3 method for class 'locpol': summary(object,...) ## S3 method for class 'locpol': print(x,...) ## S3 method for class 'locpol': plot(x,...)
formula |
formula as in lm , only first covariate is used. |
data |
data frame with data. |
weig |
Vector of weigths for each observations. |
bw |
Smoothing parameter, bandwidth. |
kernel |
Kernel used to perform the estimation, see
Kernels |
deg |
Local polynomial estimation degree($p$). |
xeval |
Vector of evaluation points. |
xevalLen |
Length of xeval if it is NULL |
x |
A locpol object. |
object |
A locpol object. |
... |
Any other required argument. |
This is an interface to the local polynomial estimation function
that provides basic lm
functionality. summary
and
print
methods shows very basic information about the fit,
fitted
return the estimation of the derivatives if code{deg}
is larger than 0, and plot
provides a plot of data, local
polynomial estimation and the variance estimation.
Variance estimation is carried out by means of the local constant regression estimation of the squared residuals.
confInterval
provides confidence intervals for all points
in x$lpFit$[,x$X]
, say those in xeval
.
A list containing among other components:
mf |
Model frame for data and formula . |
data |
data frame with data. |
weig |
Vector of weight for each observations. |
xeval |
Vector of evaluation points. |
bw |
Smoothing parameter, bandwidth. |
kernel |
Kernel used, see Kernels |
KName |
Kernel name, a string with the name of kernel. |
deg |
Local polynomial estimation degree($p$). |
X,Y |
Names in data of the response and covariate. They
are also used in lpFit to name the fitted data. |
residuals |
|
lpFit |
{ Data frame with the local polynomial fit. It contains covariate, response, derivatives estimation, $X$ density estimation, and variance estimation.}
Jorge Luis Ojeda Cabrera.
Fan, J. and Gijbels, I. Local polynomial modelling and its applications/. Chapman & Hall, London (1996).
Wand, M.~P. and Jones, M.~C. Kernel smoothing/. Chapman and Hall Ltd., London (1995).
Crist'obal, J. A. and Alcal'a, J. T. (2000). Nonparametric regression estimators for length biased data/. J. Statist. Plann. Inference, 89, pp. 145-168.
Ahmad, Ibrahim A. (1995) On multivariate kernel estimation for samples from weighted distributions/. Statistics & Probability Letters, 22, num. 2, pp. 121-129
locpoly
from package KernSmooth,
ksmooth
and loess
from package modreg.
N <- 250 xeval <- 0:100/100 ## ex1 d <- data.frame(x = runif(N)) d$y <- d$x^2 - d$x + 1 + rnorm(N, sd = 0.1) r <- locpol(y~x,d) plot(r) ## ex2 d <- data.frame(x = runif(N)) d$y <- d$x^2 - d$x + 1 + (1+d$x)*rnorm(N, sd = 0.1) r <- locpol(y~x,d) plot(r) ## length biased data !! d <- data.frame(x = runif(10*N)) d$y <- d$x^2 - d$x + 1 + (rexp(10*N,rate=4)-.25) posy <- d$y[ whichYPos <- which(d$y>0) ]; d <- d[sample(whichYPos, N,prob=posy,replace=FALSE),] rBiased <- locpol(y~x,d) r <- locpol(y~x,d,weig=1/d$y) plot(d) points(r$lpFit[,r$X],r$lpFit[,r$Y],type="l",col="blue") points(rBiased$lpFit[,rBiased$X],rBiased$lpFit[,rBiased$Y],type="l") curve(x^2 - x + 1,add=TRUE,col="red") ## add example with real data !!