bda (version 14.3.19)

lpsmooth: non-parametric regression

Description

To fit nonparametric regression model.

Usage

lpsmooth(y,x, bw, sd.y,sd.x,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.

sd.x

Standard deviation of the measurement error x.

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

Examples

Run this code
# 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)


 
# }

Run the code above in your browser using DataCamp Workspace