mstep.VVV {mclust1998} | R Documentation |
M-step for estimating parameters given conditional probabilities in an unconstrained MVN mixture model with possibly one Poisson noise term.
mstep.VVV(data, z, eps, equal = FALSE, noise = FALSE, 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 estimates.
Default : .Machine$double.eps .
|
equal |
Logical variable indicating whether or not to assume equal proportions in the
mixture. Default : FALSE .
|
noise |
Logical variable indicating whether or not to include a Poisson noise term in
the model. Default : FALSE .
|
Vinv |
An estimate of the inverse hypervolume of the data region (needed only if
noise = TRUE ). Default : determined by function hypvol
|
A list whose components are the parameter estimates corresponding to z
:
mu |
matrix whose columns are the Gaussian group means. |
sigma |
group variance matrix. |
prob |
probabilities (mixing proportions) for each group (present only when
equal = TRUE ).
The loglikelihood and reciprocal condition estimate are returned as attributes.
|
G. Celeux and G. Govaert, Gaussian parsimonious clustering models, Pattern Recognition, 28:781-793 (1995).
A. P. Dempster, N. M. Laird and D. B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B, 39:1-22 (1977).
G. J. MacLachlan and K. E. Basford, The EM Algorithm and Extensions, Wiley, (1997).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"),3) z <- me.VVV( iris[,1:4], ctoz(cl)) Mstep <- mstep.VVV(iris[,1:4], z) estep.VVV( iris[,1:4], Mstep$mu, Mstep$sigma, Mstep$prob)