Description Usage Arguments Details Value References See Also Examples
Computes maximum likelihood estimators (MLE) for finite mixtures of canonical fundamental multivariate skew t (FM-CFUST) model via the EM algorithm.
1 2 3 4 5 6 7 | fmcfust(g=1, dat, q, initial=NULL, known=NULL, clust=NULL, itmax=100, eps=1e-6,
nkmeans=20, verbose=T, method=c("moments","transformation","EMMIXskew","EMMIXuskew"),
convergence=c("Aitken","likelihood","parameters"))
## S3 method for class 'fmcfust'
summary(object, ...)
## S3 method for class 'fmcfust'
print(x, ...)
|
object, x |
an object class of class |
g |
a scalar specifying the number of components in the mixture model |
dat |
the data matrix giving the coordinates of the point(s) where the density is evaluated.
This is either a vector of length |
q |
a scalar specifying how many number of columns the skewness matrix |
initial |
(optional) a list containing the initial parameters of the mixture model.
See the 'Details' section. The default is |
known |
(optional) a list containing parameters of the mixture model that are known
and not required to be estimated. See the 'Details' section. The default is |
itmax |
(optional) a positive integer specifying the maximum number of EM iterations
to perform. The default is |
eps |
(optional) a numeric value used to control the termination criteria for the EM loops.
It is the maximum tolerance for the absolute difference between the log-likelihood value
and the asymptotic log likelihood value. The default is |
clust |
(optional) a numeric value of length |
nkmeans |
(optional) a numeric value indicating how many k-means trials to be used
when searching for initial values. The default is |
verbose |
(optional) a logical value. If |
method |
(optional) a string indicating which method to use to generate initial values.
See |
convergence |
(optional) a string indicating which convergence criterion to use to terminate the iterations.
The default |
... |
not used. |
The arguments init
and known
, if specified, is a list structure containing
at least one of mu
, sigma
, delta
, dof
, pro
(See dfmcfust
for the structure of each of these elements).
If init=FALSE
(default), the program uses an automatic approach based on
moments estimate and k-means clustering to generate an initial value for the model parameters.
Note that this may not provide the best results.
As the EM algorithm is sensitive to the starting value,
it is highly recommended to apply a wide range different initializations.
Some strategies are implemented in init.cfust
.
mu |
a list of |
sigma |
a list of |
delta |
a list of |
dof |
a numeric vector of length |
pro |
a vector of length of |
tau |
an |
clusters |
a vector of length n of final partition. |
loglik |
the final log likelihood value. |
lk |
a vector of log likelihood values at each EM iteration. |
iter |
number of iterations performed. |
eps |
the final absolute difference between the log likelihood value and the asymptotic log likelihood value. |
aic, bic |
Akaike Information Criterion (AIC), Bayes Information Criterion (BIC) |
Lee S.X. and McLachlan, G.J. (2016). Finite mixtures of canonical fundamental skew t-distributions: the unification of the restricted and unrestricted skew t-mixture models. Statistics and Computing 26, 573-589
Lee S.X. and McLachlan, G.J. (2017). EMMIXcskew: An R Package for the Fitting of a Mixture of Canonical Fundamental Skew t-Distributions. Journal of Statistical Software 83(3), 1-32. URL 10.18637/jss.v083.i03.
init.cfust
, rfmcfust
, dfmcfust
, fmcfust.contour.2d
1 2 3 4 5 6 7 |
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