R: Summarizing Gaussian Finite Mixture Model Fits

summary.Mclust {mclust}

R Documentation

Summarizing Gaussian Finite Mixture Model Fits

Description

Summary method for class "Mclust".

Usage

## S3 method for class 'Mclust'
summary(object, parameters = FALSE, classification = FALSE, ...)
## 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.

Author(s)

Luca Scrucca

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.

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)

[Package mclust version 5.3 Index]