eps allow computations to proceed nearer to singularity. The
default is the relative machine precision
.Machine$double.eps, which is approximately $2e-16$ on
IEEE-compliant machines.c(1.e-5,1.e-5).c(Inf,Inf) allowing termination
to be completely governed by tol.equalPro = FALSE.warnSingular = TRUE.EMclust
and mclustDAtrain that involve multiple models. The default
is all of the multivariate models available in MCLUST:
"EII": spherical, equal volume
"VII": spherical, unequal volume
"EEI": diagonal, equal volume and shape
"VEI": diagonal, varying volume, equal shape
"EVI": diagonal, equal volume, varying shape
"VVI": diagonal, varying volume and shape
"EEE": ellipsoidal, equal volume, shape, and orientation
"EEV": ellipsoidal, equal volume and equal shape
"VEV": ellipsoidal, equal shape
"VVV": ellipsoidal, varying volume, shape, and orientationEMclust and EMclustN. The default is
c("V","VVV"), giving the unconstrained model in each case.mclustOptions is supplied for assigning values to
the .Mclust list.mclustOptions,
EMclust,
mclustDAtrain,
em,
me,
estep,
mstepdata(iris)
irisMatrix <- iris[,1:4]
irisClass <- iris[,5]
.Mclust
.Mclust <- mclustOptions(tol = 1.e-6, emModelNames = c("VII", "VVI", "VVV"))
.Mclust
irisBic <- EMclust(irisMatrix)
summary(irisBic, irisMatrix)
.Mclust <- mclustOptions() # restore defaults
.MclustRun the code above in your browser using DataLab