bic {mclust1998} | R Documentation |
Bayesian Information Criterion for MVN mixture models with possibly one Poisson noise term.
bic(data, modelid, ...)
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.
|
... |
other arguments, including a quantity eps for determining singularity
in the covariance. The precise
definition of eps varies the parameterization, each of which has
a default.
Furthermore z , a matrix of conditional
probabilities. z should have a row for each observation in
data , and a column for each component of the mixture. If z
is missing, a single cluster is assumed (all noise if noise = TRUE ).
Next argument: equal , a logical variable indicating whether or
not the mixing proportions are equal in the model. The default is to
assume they are unequal.
The noise logical variable indicates whether or not to include a
Poisson noise term in the model. Default : FALSE .
Finally, Vinv gives
an estimate of the inverse hypervolume of the data region (needed only if
noise = TRUE ). Default : determined by the function hypvol .
|
An object of class "bic"
which is the Bayesian Information Criterion for the
given mixture model and given conditional probabilites. The model parameters
and reciprocal condition estimate are returned as attributes.
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.
C. Fraley and A. E. Raftery, How many clusters? Which clustering method? Answers via model-based cluster analysis. Technical Report No. 329, Dept. of Statistics, U. of Washington (February 1998).
R. Kass and A. E. Raftery, Bayes Factors. Journal of the American Statistical Association90:773-795 (1995).
data(iris) cl <- mhclass(mhtree(iris[,1:4], modelid = "VVV"), 3) z <- me( iris[,1:4], ctoz(cl), modelid = "VVV") bic(iris[,1:4], modelid = "VVV", z = z)