mclust (version 4.1)

summary.Mclust: Summarizing Gaussian Finite Mixture Model Fits

Description

Summary method for class "Mclust".

Usage

## S3 method for class 'Mclust':
summary(object, parameters = FALSE, classification = FALSE, \dots)
## S3 method for class 'summary.Mclust':
print(x, digits = getOption("digits"), ...)

Arguments

object

An object of class "Mclust" resulting of a call to Mclust or densityMclust.

x

An object of class "summary.Mclust", usually, a result of a call to summary.Mclust.

parameters

Logical; if TRUE, the parameters of mixture components are printed.

classification

Logical; if TRUE, the MAP classification/clustering of observations is printed.

digits

The number of significant digits to use when printing.

...

Further arguments passed to or from other methods.

References

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, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

See Also

Mclust, densityMclust.

Examples

mod1 = Mclust(iris[,1:4])
summary(mod1)
summary(mod1, parameters = TRUE, classification = TRUE)

mod2 = Mclust(iris[,1:4], G = 1)
summary(mod2, parameters = TRUE, classification = TRUE)

mod3 = Mclust(iris[,1:4], prior = priorControl())
summary(mod3)

mod4 = Mclust(iris[,1:4], prior = priorControl(functionName="defaultPrior", shrinkage=0.1))
summary(mod4, parameters = TRUE, classification = TRUE)