Returns semiparametric EM algorithm output (Benaglia et al, 2009) for mixtures of multivariate (repeated measures) data where the coordinates of a row (case) in the data matrix are assumed to be independent, conditional on the mixture component (subpopulation) from which they are drawn. For now, this algorithm only implements model (4.7) in Benaglia et al, in which each component and block has exactly the same (nonparametric) shape and they differ only by location and scale.
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)spEM returns a list of class spEM with the following items:
The raw data (an \(n\times r\) matrix).
An \(n\times m\) matrix of posterior probabilities for
observation. If stochastic = TRUE, this matrix is computed
from an average over the maxiter iterations.
If constbw==TRUE,
same as the bw input argument; otherwise, value of bw matrix
at final iteration (since for now this algorithm only implements
model (4.7) in Benaglia et al, the bandwidth matrix is reduced to a single
bandwith scalar).
This
information is needed by any method that produces density estimates from the
output.
Same as the blockid input argument, but recoded to have
positive integer values. Also needed by any method that produces density
estimates from the output.
The sequence of mixing proportions over iterations.
The final mixing proportions if stochastic = FALSE,
or the average mixing proportions if stochastic = TRUE.
The sequence of location parameters over iterations.
The final location parameters if stochastic = FALSE,
or the average location parameters if stochastic = TRUE.
The sequence of scale parameters over iterations.
The final scale parameters if stochastic = FALSE,
or the average scale parameters if stochastic = TRUE.
The sequence of log-likelihoods over iterations.
An \(n\times r\) 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.
Either an \(m\times r\) 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
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.
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.
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).
Alternative way to specify the bandwidth, to provide backward compatibility.
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.
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).
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.
If TRUE, print updates for every iteration of the algorithm as it runs
Benaglia, T., Chauveau, D., and Hunter, D. R., An EM-like algorithm for semi- and non-parametric estimation in multivariate mixtures, Journal of Computational and Graphical Statistics, 18, 505-526, 2009.
Benaglia, T., Chauveau, D. and Hunter, D.R. Bandwidth Selection in an EM-like algorithm for nonparametric multivariate mixtures. Nonparametric Statistics and Mixture Models: A Festschrift in Honor of Thomas P. Hettmansperger. World Scientific Publishing Co., pages 15-27, 2011.
Bordes, L., Chauveau, D., and Vandekerkhove, P., An EM algorithm for a semiparametric mixture model, Computational Statistics and Data Analysis, 51: 5429-5443, 2007.
plot.spEM, normmixrm.sim, spEMsymloc,
npEM, plotseq.npEM
if (FALSE) {
## simulate a 2-component gaussian mixture with 3 iid repeated measures
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)
## apply spEM with or without an iterative bandwidth selection
d <- spEM(x, mu0 = 2, blockid = rep(1,3), constbw = FALSE)
d2 <- 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, addlegend=FALSE)
plot(d2, newplot=FALSE, addlegend=FALSE, lty=2)}
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