estep.VI(mclust) | R Documentation |
estep.VI(data, mu, sigmasq, prob, eps, Vinv)
data |
matrix of observations. |
mu |
matrix whose columns are the Gaussian group means. |
sigma |
group variances. |
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$double.eps
|
Vinv |
An estimate of the inverse hypervolume of the data region (needed only if
prob indicates a noise term). Default : determined by function hypvol
|
the conditional probablilities corresponding to the parameter estimates. The loglikelihood is returned as an attribute.
E-step for estimating conditional probabilities from parameter estimates in an MVN mixture model having varying spherical variances and possibly one Poisson noise term.
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley (1997).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "VI"),3) z <- me.VI( iris[,1:4], ctoz(cl)) Mstep <- mstep.VI(iris[,1:4], z) estep.VI( iris[,1:4], Mstep$mu, Mstep$sigma, Mstep$prob)