Description Usage Arguments Details Value References See Also Examples
Generate random sample from a specified mixture of multivariate canonical fundamental skew t distribution
1 2 |
g |
a scalar specifying the number of components in the mixture model |
n |
either a positive integer specifying the total number of points to be generated
or a vector (of length |
mu |
for |
sigma |
for |
delta |
for |
dof |
for |
pro |
the mixing proportions; for |
known |
a list containing the parameters of the model.
If specified, it overwrites the values of |
rcfust
generates a sample n
multivariate CFUST observations.
rfmcfust
generates a mixture of CFUST observation.
Note that model parameters can be passed to rcfust
and rfmcfust
through the argument known
or listed as individual arguments.
If both methods of input were used, the parameters specified in known
will be used.
rcfust
returns an n
by p
numeric matrix of generated data.
rfmcfust
returns an n
by p+1
numeric matrix of genertaed data.
The first p
gives the coordinates of the generated data.
The last column specifies which component each data point is generated from.
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.
1 2 3 4 5 6 7 8 9 10 11 12 | ##---- Should be DIRECTLY executable !! ----
##-- ==> Define data, use random,
##-- or do help(data=index) for the standard data sets.
rcfust(10,c(1,2),diag(2),matrix(c(2,1,1,2),2,2),4)
obj <- list()
obj$mu <- list(c(17,19), c(5,22), c(6,10))
obj$sigma <- list(diag(2), matrix(c(2,0,0,1),2), matrix(c(3,7,7,24),2))
obj$delta <- list(matrix(c(3,0,2,1.5),2,2), matrix(c(5,0,0,10),2,2), matrix(c(2,0,5,0),2,2))
obj$dof <- c(1, 2, 3)
obj$pro <- c(0.25, 0.25, 0.5)
rfmcfust(3, 100, known=obj)
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