mclust (version 5.0.2)

MclustBootstrap: Bootstrap Inference for Gaussian finite mixture models

Description

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

Usage

MclustBootstrap(object, nboot = 999, type = c("nonpara", "wlb"), 
                verbose = TRUE, ...)

Arguments

object

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

nboot

The number of bootstrap replications.

type

A character string specifying the type of bootstrap to use: ll{ "nonpara" = nonparametric bootstrap "wlb" = weighted likelihood bootstrap }

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).
• parameters
  A list of estimated parameters for the mixture components with the following components: [object Object],[object Object],[object Object]
• nboot
  The number of bootstrap replications.
• type
  The type of bootstrap computation employed.
• pro
  A matrix of dimension (nboot x G) containing the bootstrap 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 bootstrap 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 bootstrap distribution for the component covariances.

Details

Nonparametric bootstrap is used to compute the bootstrap 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 bootstrap 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

summary.MclustBootstrap, plot.MclustBootstrap, 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")