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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
,
mstep
n <- 250 ## create artificial data
set.seed(0)
x <- rbind(matrix(rnorm(n*2), n, 2) %*% diag(c(1,9)),
matrix(rnorm(n*2), n, 2) %*% diag(c(1,9))[,2:1])
xclass <- c(rep(1,n),rep(2,n))
odd <- seq(1, 2*n, 2)
train <- mclustDAtrain(x[odd, ], labels = xclass[odd]) ## training step
even <- odd + 1
test <- mclustDAtest(x[even, ], train) ## compute model densities
data(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
.Mclust
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