lpsvariance: compute the variance of the local polynomial regression...

lps.varianceR Documentation

compute the variance of the local polynomial regression function

Description

To compute the variance of the local polynomial regression function

Usage

  lps.variance(y,x,bw, method="Rice")

Arguments

y, x

Two numerical vectors: y is the response and x is the predictor.

bw

Smoothing parameter. Is used only when method='Wasserman' or method='heteroscedastic'.

method

We use four method to compute the variance of r(x): Method 1) Larry Wasserman–nearly unbiased. This method based on an lps object; Method 2) Rice 1984 Method 3) Gasser et al (1986) – a variation of method 3. Method 4) For heteroscedastic errors. Need to estimate based on an lpr object. Yu and Jones (2004). Defaulty method: Rice.

Value

the variance of r(x).

Examples


n = 100
x=rnorm(n)
y=x^2+rnorm(n)
bw = lps.variance
par(mfrow=c(1,1))
out=lpsmooth(y,x)
#plot(out, scb=TRUE, type='l')
vrx = lps.variance(y,x)
out=lpsmooth(y,x,sd.y=sqrt(vrx), bw=0.5)
plot(y~x, pch='.')
lines(out, col=2)

x0 = seq(min(x),  max(x), length=100)
y0 = x0^2
lines(y0~x0, col=4)

 

bda documentation built on May 29, 2024, 2:56 a.m.

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