Description Usage Arguments Value Note References See Also Examples
Computes maximumlikelihood estimates for the parameters of the unrestricted general location model from an incomplete mixed dataset.
1 
s 
summary list of an incomplete data matrix produced by the function

start 
optional starting value of the parameter. This is a parameter list in
packed storage, such as one returned by this function or by

prior 
Optional vector or array of hyperparameters for a Dirichlet prior distribution. By default, uses a uniform prior on the cell probabilities (all hyperparameters set to one). EM algorithm finds the posterior mode, which under a uniform prior is the same as a maximumlikelihood estimate. If structural zeros appear in the table, the corresponding hyperparameters should be set to NA. 
maxits 
maximum number of iterations performed. The algorithm will stop if the parameter still has not converged after this many iterations. 
showits 
if 
eps 
optional convergence criterion. The algorithm stops when the maximum relative difference in every parameter from one iteration to the next is less than or equal to this value. 
a list representing the maximumlikelihood estimates (or posterior
mode) of the normal parameters. This list contains cell probabilities,
cell means, and covariances. The parameter can be transformed back to
the original scale and put into a more understandable format by the
function getparam.mix
.
If zero cell counts occur in the completedata table, the maximum likelihood estimate may not be unique, and the algorithm may converge to different stationary values depending on the starting value. Also, if zero cell counts occur in the completedata table, the ML estimate may lie on the boundary of the parameter space.
Schafer, J. L. (1996) Analysis of Incomplete Multivariate Data. Chapman \& Hall, Chapter 9.
prelim.mix
, getparam.mix
,
and ecm.mix
.
1 2 3 4  data(stlouis)
s < prelim.mix(stlouis,3) # do preliminary manipulations
thetahat < em.mix(s) # compute ML estimate
getparam.mix(s,thetahat, corr=TRUE) # look at estimated parameters

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