Conjugate Prior for Gaussian Mixtures.

Specify a conjugate prior for Gaussian mixtures.

Usage

priorControl(functionName = "defaultPrior", ...)

Arguments

functionName

The name of the function specifying the conjugate prior. By default the function defaultPrior is used, and this can also be used as a template for alternative specification.

...

Optional named arguments to the function specified in functionName together with their values.

Value

A list with the function name as the first component. The remaining components (if any) consist of a list of arguments to the function with assigned values.

Details

The function priorControl is used to specify a conjugate prior for EM within MCLUST.
Note that, as described in defaultPrior, in the multivariate case only 10 out of 14 models may be used in conjunction with a prior, i.e. those available in MCLUST up to version 4.4.

References

C. Fraley and A. E. Raftery (2007). Bayesian regularization for normal mixture estimation and model-based clustering. Journal of Classification 24:155-181.

See also

mclustBIC, me, mstep, defaultPrior

Examples

# default prior
irisBIC <- mclustBIC(iris[,-5], prior = priorControl())
#> Warning: The presence of BIC values equal to NA is likely due to one or more of the mixture proportions being estimated as zero, so that the model estimated reduces to one with a smaller number of components.
summary(irisBIC, iris[,-5])
#> Best BIC values:
#>              VEV,2       VEV,3      VVV,2
#> BIC      -580.8136 -587.403843 -592.51283
#> BIC diff    0.0000   -6.590289  -11.69928
#> 
#> Classification table for model (VEV,2): 
#> 
#>   1   2 
#>  50 100 

# no prior on the mean; default prior on variance
irisBIC <- mclustBIC(iris[,-5], prior = priorControl(shrinkage = 0))
#> Warning: The presence of BIC values equal to NA is likely due to one or more of the mixture proportions being estimated as zero, so that the model estimated reduces to one with a smaller number of components.
summary(irisBIC, iris[,-5])
#> Best BIC values:
#>              VEV,2       VEV,3      VVV,2
#> BIC      -580.2861 -586.792195 -592.07132
#> BIC diff    0.0000   -6.506112  -11.78523
#> 
#> Classification table for model (VEV,2): 
#> 
#>   1   2 
#>  50 100