Usage
spEM(x, mu0, blockid = 1:ncol(x), bw = bw.nrd0(as.vector(as.matrix(x))), constbw = TRUE, h = bw, eps = 1e-8, maxiter = 500, stochastic = FALSE, verb = TRUE)
Arguments
x
An $n x r$ matrix of data. Each of the $n$ rows is a case,
and each case has $r$ repeated measurements. These measurements are assumed
to be conditionally independent, conditional on the mixture component (subpopulation)
from which the case is drawn.
mu0
Either an $m x r$ matrix specifying the initial
centers for the kmeans function, or an integer $m$ specifying the
number of initial centers, which are then choosen randomly in
kmeans blockid
A vector of length $r$ identifying coordinates
(columns of x) that are
assumed to be identically distributed (i.e., in the same block). For instance,
the default has all distinct elements, indicating that no two coordinates
are assumed identically distributed and thus a separate set of $m$
density estimates is produced for each column of $x$. On the other hand,
if blockid=rep(1,ncol(x)), then the coordinates in each row
are assumed conditionally i.i.d.
bw
Bandwidth for density estimation, equal to the standard deviation
of the kernel density. By default, a simplistic application of the
default bw.nrd0
bandwidth used by density to the entire dataset. constbw
Logical: If TRUE, use the same bandwidth for
each iteration and for each component and block. If FALSE,
use a separate bandwidth for each component and block, and update
this bandwidth at each iteration of the algorithm using a suitably
modified bw.nrd0 method as described in
Benaglia et al (2011). h
Alternative way to specify the bandwidth, to provide backward
compatibility.
eps
Tolerance limit for declaring algorithm convergence. Convergence
is declared whenever the maximum change in any coordinate of the
lambda vector (of mixing proportion estimates) does not exceed
eps.
maxiter
The maximum number of iterations allowed, for both
stochastic and non-stochastic versions;
for non-stochastic algorithms (stochastic = FALSE), convergence
may be declared before maxiter iterations (see eps above).
stochastic
Flag, if FALSE (the default), runs the non-stochastic version
of the npEM algorithm, as in Benaglia et al (2009). Set to TRUE to
run a stochastic version which simulates the posteriors at each
iteration, and runs for maxiter iterations.
verb
If TRUE, print updates for every iteration of the algorithm as
it runs