defaultPrior {mclust} | R Documentation |
Default conjugate prior specification for Gaussian mixtures.
defaultPrior(data, G, modelName, ...)
data |
The name of the function specifying the conjgate prior.
The default function is defaultPrior , which can be
used a template for
|
G |
The number of mixture components. |
modelName |
A character string indicating the model: "E": equal variance (one-dimensional) "V": variable variance (one-dimensional) "EII": spherical, equal volume "VII": spherical, unequal volume "EEI": diagonal, equal volume and shape "VEI": diagonal, varying volume, equal shape "EVI": diagonal, equal volume, varying shape "VVI": diagonal, varying volume and shape "EEE": ellipsoidal, equal volume, shape, and orientation "EEV": ellipsoidal, equal volume and equal shape "VEV": ellipsoidal, equal shape "VVV": ellipsoidal, varying volume, shape, and orientation |
... |
One or more of the following:
|
defaultPrior
is as a default prior specification
for EM within MCLUST. It is usually not necessary
to invoke defaultPrior
explicitly (it does not appear in the examples
below because it is the default function name in priorControl
).
This function allows considerable flexibility in the prior specification,
and can be used as a template for further users that want to specify
their own conjugate prior beyond what the arguments will allow.
A list giving the prior degrees of freedom, scale, shrinkage, and mean.
C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611-631.
C. Fraley and A. E. Raftery (2005). Bayesian regularization for normal mixture estimation and model-based clustering. Technical Report, Department of Statistics, University of Washington.
C. Fraley and A. E. Raftery (2006). MCLUST Version 3 for R: Normal Mixture Modeling and Model-Based Clustering, Technical Report no. 504, Department of Statistics, University of Washington.
mclustBIC
,
me
,
mstep
,
priorControl
# default prior irisBIC <- mclustBIC(iris[,-5], prior = priorControl()) summary(irisBIC, iris[,-5]) # equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName = "defaultPrior")) summary(irisBIC, iris[,-5]) # no prior on the mean; default prior on variance irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0)) summary(irisBIC, iris[,-5]) # equivalent to previous example irisBIC <- mclustBIC(iris[,-5], prior = priorControl(functionName="defaultPrior", shrinkage=0)) summary(irisBIC, iris[,-5])