mstep.VEV {mclust} | R Documentation |
M-step for estimating parameters given conditional probabilities in an MVN mixture model having constant shape, constant volume and possibly one Poisson noise term.
mstep.VEV(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 |
A 2-vector specifying lower bounds on the pth root of the volume of the
ellipsoids defining the clusters, where p is the data dimension, and on the
reciprocal condition number for the estimated shape of the covariance
estimates. Default: c(.Machine$double.eps, .Machine$double.eps)
|
tol |
The iteration for volume/shape estimates is terminated if their relative
error is less than tol .
|
itmax |
The iteration for volume/shape estimates is terminated if the number of
iterations exceeds itmax . Default: Inf (termination is determined by
tol ).
|
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
|
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 = T ).
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]),3) z <- me.VEV( iris[,1:4], ctoz(cl)) mstep.VEV(iris[,1:4], z)