Description Usage Arguments Details Value See Also
The routinnes to fit mixture models to the data and to obtain initial partition or initial values.
1 2 3 | wire.init.fit(dat,X,qe,n,m,g,nkmeans,nrandom=0)
wire.init.reg(dat,X,qe,n,m,g,cluster)
tau.estep.wire(dat,pro,mu,sigma,n,m,g)
|
dat |
The dataset, an n by m numeric matrix, where n is number of observations and m the dimension of data. |
X |
The design matrix |
n |
The number of observations |
m |
The number of variables |
g |
The number of components in the mixture model |
qe |
The number of columns of design matrix W |
cluster |
A vector of integers specifying the initial partitions of the data |
nkmeans |
An integer to specify the number of KMEANS partitions to be used to find the best initial values. |
nrandom |
An integer to specify the number of random partitions to be used to find the best initial values; the default value is 0. |
pro |
A vector of mixing proportions pi |
mu |
A numeric matrix with each column corresponding to the mean. |
sigma |
The covaraince m by m by g array. |
These functions are called internally.
pro |
A vector of mixing proportions pi |
beta |
A numeric matrix with each column corresponding to the mean. |
sigma.e |
The covaraince of error |
cluster |
A vector of final partition |
loglik |
The loglikelihood at convergence |
lk |
A vector of loglikelihood at each EM iteration |
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