estep.EI {mclust} | R Documentation |
E-step for estimating conditional probabilities from parameter estimates in an MVN mixture model having equal spherical variances and possibly one Poisson noise term.
estep.EI(data, mu, sigma, prob, eps, Vinv)
data |
matrix of observations. |
mu |
matrix whose columns are the Gaussian group means. |
sigma |
variance. |
prob |
mixing proportions (probabilities) for each group. If prob is missing,
the number of groups is assumed to be the number of columns in mu (no
noise). A Poisson noise term will appear in the conditional probabilities if
length(prob) is equal to ncol(mu)+1 .
|
eps |
Lower bound on the estimated values of sigma-squared.
Default : .Machine$double.eps
|
Vinv |
An estimate of the inverse hypervolume of the data region (needed only if
prob indicates a noise term). Default : determined by function hypvol
|
the conditional probablilities corresponding to the parameter estimates. The loglikelihood is returned as an attribute.
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley (1997).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "EI"),3) z <- me.EI( iris[,1:4], ctoz(cl)) Mstep <- mstep.EI(iris[,1:4], z) estep.EI( iris[,1:4], Mstep$mu, Mstep$sigma, Mstep$prob)