lpsmooth

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non-parametric regression

To fit nonparametric regression model.

Keywords
smooth
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.

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.

Value

y

Estimated values of the smooth function over a fine grid.

x

grid points where the smoothed function are evaluated.

x0,y0

cleaned data of x and y.

conf.level

confidence level of the simultaneous confidence bands.

pars

estimate parameters including smoothing bandwidth, and parameters for the tube formula.

ucb,lcb

upper and lower confidence bands.

call

function called

• lpsmooth
• print.scb
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')
x0 <- seq(min(x),max(x),length=100)
y0 <- (x0-32)^2
lines(x0, y0, col=2)
points(x, y, pch="*", col=4)

# }

Documentation reproduced from package bda, version 14.1.4, License: Unlimited

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