me.EEE {mclust} | R Documentation |
EM iteration (M-step followed by E-step) for estimating parameters in an MVN mixture model having constant variance and possibly one Poisson noise term.
me.EEE(data, z, eps, tol, itmax, equal = F, noise = F, Vinv)
data |
matrix of observations. |
z |
matrix of conditional probabilities. z should have a row for each observation
in data , and a column for each component of the mixture.
|
eps |
Lower bound on the reciprocal condition estimate for the covariance estimate.
Default : .Machine$double.eps .
|
tol |
The iteration is terminated if the relative error in the loglikelihood value
falls below tol . Default : sqrt(.Machine$double.eps) .
|
itmax |
Upper limit on the number of iterations. Default : Inf (no upper limit).
|
equal |
Logical variable indicating whether or not to assume equal proportions in the
mixture. Default : F .
|
noise |
Logical variable indicating whether or not to include a Poisson noise term in
the model. Default : F .
|
Vinv |
An estimate of the inverse hypervolume of the data region (needed only if
noise = T ). Default : determined by function hypvol
|
the conditional probablilities at the final iteration (information about the iteration is included as attributes).
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley (1997).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "EEE"),3) z <- me.EEE( iris[,1:4], ctoz(cl)) Mstep <- mstep.EEE(iris[,1:4], z) estep.EEE( iris[,1:4], Mstep$mu, Mstep$sigma, Mstep$prob)