Description Usage Arguments Details Value References See Also Examples
Computes maximum likelihood estimators (MLE) for finite mixtures of unrestricted multivariate skew t (FM-MST) model via the EM algorithm.
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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 |
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 |
print |
(optional) a logical value. If |
... |
not used. |
The arguments init
and known
, if specified, is a list structure containing
at least one of mu
, sigma
, delta
, dof
, pro
.
If init=FALSE
(default), the program uses an automatic approach based on
k-means clustering to generate an initial value for the model parameters.
Note that this may not provide the best results.
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, McLachlan G (2011). On the fitting of mixtures of multivariate skew t-distributions via the EM algorithm. arXiv:1109.4706 [stat.ME]
Lee, S. and McLachlan, G.J. (2014) Finite mixtures of multivariate skew t-distributions: some recent and new results. Statistics and Computing, 24, 181-202.
Lee, S. and McLachlan, G.J. (2013) EMMIXuskew: An R
package for
fitting mixtures of multivariate skew t-distributions via the EM algorithm.
Journal of Statistical Software, 55(12), 1-22.
URL http://www.jstatsoft.org/v55/i12/.
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