wkde {bda}  R Documentation 
Returns x and y coordinates of the binned kernel density estimate of the probability density of the weighted data.
wkde(x, w, bandwidth, freq=FALSE, gridsize = 401L, range.x, truncate = TRUE, na.rm = TRUE)
x 
vector of observations from the distribution whose density is to be estimated. Missing values are not allowed. 
w 
The weights of 
bandwidth 
the kernel bandwidth smoothing parameter. Larger values of

freq 
An indicator showing whether 
gridsize 
the number of equally spaced points at which to estimate the density. 
range.x 
vector containing the minimum and maximum values of 
truncate 
logical flag: if 
na.rm 
logical flag: if 
The default bandwidth, "wnrd0"
, is computed using a ruleofthumb for
choosing the bandwidth of a Gaussian kernel density estimator
based on weighted data. It defaults to 0.9 times the
minimum of the standard deviation and the interquartile range
divided by 1.34 times the sample size to the negative onefifth
power (= Silverman's ‘rule of thumb’, Silverman (1986, page 48,
eqn (3.31)) _unless_ the quartiles coincide when a positive result
will be guaranteed.
"wnrd"
is the more common variation given by Scott (1992), using
factor 1.06.
"wmise"
is a completely automatic optimal bandwidth selector
using the leastsquares crossvalidation (LSCV) method by minimizing the
integrated squared errors (ISE).
a list containing the following components:
x 
vector of sorted 
y 
vector of density estimates
at the corresponding 
bw 
optimal bandwidth. 
sp 
sensitivity parameter, none 
Wand, M. P. and Jones, M. C. (1995). Kernel Smoothing. Chapman and Hall, London.
mu = 34.5; s=1.5; n = 3000 x = round(rnorm(n, mu, s),1) x0 = seq(min(x)s,max(x)+s, length=100) f0 = dnorm(x0,mu, s) xt = table(x); n = length(x) x1 = as.numeric(names(xt)) w1 = as.numeric(xt) (h1 < bw.wnrd0(x1, w1)) (h2 < bw.wnrd0(x1,w1,n=n)) est1 < wkde(x1,w1, bandwidth=h1) est2 < wkde(x1,w1, bandwidth=h2) est3 < wkde(x1,w1, bandwidth='awmise') est4 < wkde(x1,w1, bandwidth='wmise') est5 < wkde(x1,w1, bandwidth='blscv') est0 = density(x1,bw="SJ",weights=w1/sum(w1)); plot(f0~x0, xlim=c(min(x),max(x)), ylim=c(0,.30), type="l") lines(est0, col=2, lty=2, lwd=2) lines(est1, col=2) lines(est2, col=3) lines(est3, col=4) lines(est4, col=5) lines(est5, col=6) legend(max(x),.3,xjust=1,yjust=1,cex=.8, legend=c("N(34.5,1.5)", "SJ", "wnrd0", "wnrd0(n)","awmise","wmise","blscv"), col = c(1,2,2,3,4,5,6), lty=c(1,2,1,1,1,1,1), lwd=c(1,2,1,1,1,1,1))