Description Usage Arguments Details Value References See Also Examples
Bootstrap or jackknife estimation of standard errors and percentile bootstrap confidence intervals for the parameters of a Gaussian mixture model.
1 2 3  MclustBootstrap(object, nboot = 999, type = c("bs", "wlbs", "pb", "jk"),
max.nonfit = 10*nboot, verbose = interactive(),
...)

object 
An object of class  
nboot 
The number of bootstrap replications.  
type 
A character string specifying the type of resampling to use:
 
max.nonfit 
The maximum number of nonestimable models allowed.  
verbose 
A logical controlling if a text progress bar is displayed during the resampling procedure. By default is  
... 
Further arguments passed to or from other methods. 
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.
An object of class 'MclustBootstrap'
with the following components:
n 
The number of observations in the data. 
d 
The dimension of the data. 
G 
A value specifying the number of mixture components. 
modelName 
A character string specifying the mixture model covariances
parameterisation (see 
parameters 
A list of estimated parameters for the mixture components with the following components:

nboot 
The number of bootstrap replications if 
type 
The type of resampling approach used. 
nonfit 
The number of resamples that did not convergence during the procedure. 
pro 
A matrix of dimension ( 
mean 
An array of dimension ( 
variance 
An array of dimension ( 
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 ModelBased Clustering. Submitted to Computational Statistics.
summary.MclustBootstrap
, plot.MclustBootstrap
, Mclust
, densityMclust
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  ## 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)

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