mclust (version 5.0.0)

MclustBootstrap: Bootstrap Inference for Gaussian finite mixture models

Description

Boostrap estimation of standard errors and percentile boostrap confidence intervals for the parameters of a Gaussian mixture model.

Usage

MclustBootstrap(object, nboot = 999, verbose = TRUE, ...)

Arguments

object

An object of class 'Mclust' or 'densityMclust' providing an estimated Gaussian mixture model.

nboot

The number of bootstrap replications.

verbose

A logical, if TRUE and the session is interactive a text progress bar is displayed during the bootstrap procedure.

...

Further arguments passed to or from other methods.

Value

• An object of class 'MclustBootstrap' with the following components:
• G
  A value specifying the number of mixture components.
• modelName
  A character string specifying the mixture model covariances parameterisation (see mclustModelNames).
• nboot
  The number of bootstrap replications.
• pro
  A matrix of dimension nboot x G containing the boostrap distribution for the mixing proportion.
• mean
  An array of dimension nboot x d x G, where d is the dimension of the data, containing the boostrap distribution for the component means.
• variance
  An array of dimension nboot x d x d x G, where d is the dimension of the data, containing the boostrap distribution for the component covariances.

Details

Nonparametric boostrap is used to compute the boostrap distribution of mixture parameters. For a fitted mixture model given by the first argument of the function call with object$G mixture components and covariances parameterisation model object$modelName, the boostrap procedure generates nboot bootstrap samples of the same size as the original data by re-sampling with replacement from the observed data. The resulting bootstrap distribution can then be used to obtain standard errors and percentile confidence intervals by the use of summary.MclustBootstrap function.

References

Davison, A. and Hinkley, D. (1997) Bootstrap Methods and Their Applications. Cambridge University Press.

McLachlan, G.J. and Peel, D. (2000) Finite Mixture Models. Wiley.

See Also

Mclust, densityMclust.

Examples

data(diabetes)
X = diabetes[,-1]
modClust = Mclust(X) 
bootClust = MclustBootstrap(modClust)
summary(bootClust, what = "se")
summary(bootClust, what = "ci")

data(acidity)
modDens = densityMclust(acidity)
modDens = MclustBootstrap(modDens)
summary(modDens, what = "se")
summary(modDens, what = "ci")