Normal kernel density estimate for semiparametric EM output
Takes an object of class spEM and returns an object of class
density giving the kernel density estimate.
## S3 method for class 'spEM' density(x, u=NULL, component=1, block=1, scale=FALSE, ...)
x |
An object of class |
u |
Vector of points at which the density is to be evaluated |
component |
Mixture component number; should be an integer from 1 to the
number of columns of |
block |
Block of repeated measures. Only applicable in repeated measures
case, for which |
scale |
Logical: If TRUE, multiply the density values by the
corresponding mixing proportions found in |
... |
Additional arguments; not used by this method. |
The bandwidth is taken to be the same as that used to produce the npEM
object, which is given by x$bandwidth.
set.seed(100)
mu <- matrix(c(0, 15), 2, 3)
sigma <- matrix(c(1, 5), 2, 3)
x <- rmvnormmix(300, lambda = c(.4,.6), mu = mu, sigma = sigma)
d <- spEM(x, mu0 = 2, blockid = rep(1,3), constbw = TRUE)
plot(d, xlim=c(-10, 40), ylim = c(0, .16), xlab = "", breaks = 30,
cex.lab=1.5, cex.axis=1.5) # plot.spEM calls density.spEM herePlease choose more modern alternatives, such as Google Chrome or Mozilla Firefox.