mstep.EI(mclust) | R Documentation |
mstep.EI(data, z, eps, 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 estimated value of sigma-squared.
Default : .Machine$double.eps
|
equal |
Logical variable indicating whether or not to assume equal proportions in the
mixture. Default : F .
|
noise |
Logical variable indicating whether or not to include a Poisson noise term in
the model. Default : F .
|
Vinv |
An estimate of the inverse hypervolume of the data region (needed only if
noise = T ). Default : determined by function hypvol
|
z
:
mu |
matrix whose columns are the Gaussian group means. |
sigma |
group variance matrix. |
prob |
probabilities (mixing proportions) for each group (present only when
equal = T ).
The loglikelihood and reciprocal condition estimate are returned as attributes.
|
M-step for estimating parameters given conditional probabilities in an MVN mixture model having equal, spherical variances and possibly one Poisson noise term.
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley,
(1997).
mstep
, me.EI
, estep.EI
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "EI"),3) z <- me.EI( iris[,1:4], ctoz(cl)) Mstep <- mstep.EI(iris[,1:4], z) estep.EI( iris[,1:4], Mstep$mu, Mstep$sigma, Mstep$prob)