mixtools (version 1.0.1)

spEM: Semiparametric EM-like Algorithm for Mixtures of Independent Repeated Measurements

Description

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.

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 $n\times r$ 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 i

mu0

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

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 ass

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

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

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 ab

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

verb

If TRUE, print updates for every iteration of the algorithm as it runs

Value

• spEM returns a list of class spEM with the following items:
• data
  The raw data (an $n\times r$ matrix).
• posteriors
  An $n\times m$ matrix of posterior probabilities for observation. If stochastic = TRUE, this matrix is computed from an average over the maxiter iterations.
• bandwidth
  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.
• blockid
  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.
• lambda
  The sequence of mixing proportions over iterations.
• lambdahat
  The final mixing proportions if stochastic = FALSE, or the average mixing proportions if stochastic = TRUE.
• mu
  The sequence of location parameters over iterations.
• muhat
  The final location parameters if stochastic = FALSE, or the average location parameters if stochastic = TRUE.
• sigma
  The sequence of scale parameters over iterations.
• sigmahat
  The final scale parameters if stochastic = FALSE, or the average scale parameters if stochastic = TRUE.
• loglik
  The sequence of log-likelihoods over iterations.

References

• 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.

See Also

plot.spEM, normmixrm.sim, spEMsymloc, npEM, plotseq.npEM

Examples

## 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)