bic {mclust} | R Documentation |
Bayesian Information Criterion for MVN mixture models with possibly one Poisson noise term.
bic(data, modelid, z, eps, tol, itmax, equal = F, noise = F, Vinv)
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, and the following:
|
z |
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 = T ).
|
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 the mixing proportions are equal in the model. The default is to assume they are unequal. |
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 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)