mclust (version 5.2.2)

MclustBootstrap: Resampling-based Inference for Gaussian finite mixture models

Description

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

Usage

MclustBootstrap(object, nboot = 999, type = c("bs", "wlbs", "jk"), 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 resampling to use:

"bs"

= nonparametric bootstrap

"wlbs"

= weighted likelihood bootstrap

"jk"

= jackknife

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

'MclustBootstrap' with the following components:

Details

For a fitted Gaussian mixture model with object$G mixture components and covariances parameterisation object$modelName, this function returns either the boostrap distribution or the jackknife distribution of mixture parameters. In the former case, the nonparametric bootstrap or the weighted likelihood bootstrap approach could be used, so the the bootstrap procedure generates nboot bootstrap samples of the same size as the original data by resampling with replacement from the observed data. In the jackknife case, the procedure considers all the samples obtained by omitting one observation at time.

The resulting resampling 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.

O'Hagan A., Murphy T. B., Gormley I. C. and Scrucca L. (2015) On Estimation of Parameter Uncertainty in Model-Based Clustering. Submitted to Computational Statistics.

See Also

summary.MclustBootstrap, plot.MclustBootstrap, Mclust, densityMclust.

Examples

## Not run: 
# 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")
# ## End(Not run)