mstep {mclust} | R Documentation |
M-step for estimating parameters given conditional probabilities in a MVN mixture model with possibly one Poisson noise term.
mstep(data, modelid, z, ...)
data |
matrix of observations. |
modelid |
An integer specifying a parameterization of the MVN covariance matrix defined
by volume, shape and orientation charactertistics of the underlying clusters.
The allowed values for modelid and their interpretation are as follows:
"EI" : uniform spherical, "VI" : spherical, "EEE" : uniform variance,
"VVV" : unconstrained variance, "EEV" : uniform shape and volume,
"VEV" : uniform shape.
|
z |
matrix of conditional probabilities. z should have a row for each observation
in data , and a column for each component of the mixture.
|
... |
additional arguments, as follows: |
eps |
Tolerance for determining singularity in the covariance matrix. The precise
definition of eps varies the parameterization, each of which has a default.
|
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 is returned as an attribute.
|
The reciprocal condition estimate returned as an attribute ranges in value between 0 and 1. The closer this estimate is to zero, the more likely it is that the corresponding EM result (and BIC) are contaminated by roundoff error.
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( iris[,1:4], modelid = "VVV", ctoz(cl)) pars <- mstep(iris[,1:4], modelid="VVV", z)