bda (version 10.1.9)

lpsmooth: non-parametric regression

Description

To fit nonparametric regression model.

Usage

lpsmooth(y,x, bw, sd.y,lscv=FALSE, adaptive=FALSE,
 	  from, to, gridsize,conf.level=0.95)

Arguments

y,x

Two numerical vectors.

from,to,gridsize

start point, end point and size of a fine grid where the EDF will be evaluated.

bw

Smoothing parameter. Numeric or character value is allowed. If missing, adaptive (LSCV) bandwidth selector will be used.

lscv,adaptive

If lscv = FALSE, use the given bandwidth to fit lpr directly. If lscv = TRUE and adaptive = FALSE, compute lscv bandwidth and fit lpr. Initial bandwidth should be given. If lscv = TRUE and adaptive = TURE, compute lscv bandwidth, then compute varying smoothing parameter, then fit lpr. This algorithm could be extremeely slow when the sample size is very large.

sd.y

Standard deviation of y.

conf.level

Confidence level.

Examples

# NOT RUN {
 x <- rnorm(100,34.5,1.5)
 e <- rnorm(100,0,2)
 y <- (x-32)^2 + e
 out <- lpsmooth(y,x)
 out
 plot(out, type='l', scb=TRUE)
 x0 <- seq(min(x),max(x),length=100)
 y0 <- (x0-32)^2
 lines(x0, y0, col=2)
 points(x, y, pch="*", col=4)


 
# }