wkde {mixtools} | R Documentation |
Weighted Univariate (Normal) Kernel Density Estimate
Description
Evaluates a weighted kernel density estimate, using a Gaussian kernel, at a specified vector of points.
Usage
wkde(x, u=x, w=rep(1, length(x)), bw=bw.nrd0(as.vector(x)), sym=FALSE)
Arguments
x |
Data |
u |
Points at which density is to be estimated |
w |
Weights (same length as |
bw |
Bandwidth |
sym |
Logical: Symmetrize about zero? |
Value
A vector of the same length as u
References
Benaglia, T., Chauveau, D., and Hunter, D. R. (2009), An EM-like algorithm for semi- and non-parametric estimation in multivariate mixtures, Journal of Computational and Graphical Statistics, 18, 505-526.
Benaglia, T., Chauveau, D., Hunter, D. R., and Young, D. (2009), mixtools: An R package for analyzing finite mixture models. Journal of Statistical Software, 32(6):1-29.
See Also
Examples
# Mixture with mv gaussian model
set.seed(100)
m <- 2 # no. of components
r <- 3 # no. of repeated measures (coordinates)
lambda <- c(0.4, 0.6)
mu <- matrix(c(0, 0, 0, 4, 4, 6), m, r, byrow=TRUE) # means
sigma <- matrix(rep(1, 6), m, r, byrow=TRUE) # stdevs
centers <- matrix(c(0, 0, 0, 4, 4, 4), 2, 3, byrow=TRUE) # initial centers for est
blockid = c(1,1,2) # block structure of coordinates
n = 100
x <- rmvnormmix(n, lambda, mu, sigma) # simulated data
a <- npEM(x, centers, blockid, eps=1e-8, verb=FALSE)
par(mfrow=c(2,2))
u <- seq(min(x), max(x), len=200)
for(j in 1:2) {
for(b in 1:2) {
xx <- as.vector(x[,a$blockid==b])
wts <- rep(a$post[,j], length.out=length(xx))
bw <- a$bandwidth
title <- paste("j =", j, "and b =", b)
plot(u, wkde(xx, u, wts, bw), type="l", main=title)
}
}
[Package mixtools version 2.0.0 Index]