* checking for file 'SpeCond/DESCRIPTION' ... OK
* preparing 'SpeCond':
* checking DESCRIPTION meta-information ... OK
* installing the package to re-build vignettes
* creating vignettes ... ERROR
Loading required package: mclust
Loading required package: Biobase
Welcome to Bioconductor
Vignettes contain introductory material. To view, type
'browseVignettes()'. To cite Bioconductor, see
'citation("Biobase")' and for packages 'citation("pkgname")'.
Loading required package: fields
Loading required package: spam
Package 'spam' is loaded. Spam version 0.23-0 (2010-09-01).
Type demo( spam) for some demos, help( Spam) for an overview
of this package.
Help for individual functions is optained by adding the
suffix '.spam' to the function name, e.g. 'help(chol.spam)'.
Attaching package: 'spam'
The following object(s) are masked from 'package:base':
backsolve, forwardsolve, norm
Use help(fields) for an overview of this library
library( fields, keep.source=TRUE) retains comments in the source code.
Loading required package: hwriter
Loading required package: RColorBrewer
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, modelNames = "V") :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at max choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Warning in summary.mclustBIC(Bic, data, G = G, modelNames = modelNames) :
best model occurs at the min or max # of components considered
Warning in Mclust(expressionMatrix[i, ], G = 1:3, prior = priorControl(shrinkage = 0, :
optimal number of clusters occurs at min choice
Error: processing vignette 'SpeCond.Rnw' failed with diagnostics:
chunk 3
Error in `rownames<-`(x, value) :
attempt to set rownames on object with no dimensions
Execution halted