Description Usage Arguments Value
Given a pair of h and tau and data, compute ordinary & penalized log-likelihood ratio resulting from MEM algorithm at k=1,2,3, tailored for parallelization.
1 2 | normalmixMaxPhiStep(htaupair, y, parlist, z = NULL, p, an, ninits,
ninits.short, epsilon.short, epsilon, maxit.short, maxit, verb)
|
htaupair |
A set of h and tau |
y |
n by 1 vector of data |
parlist |
The parameter estimates as a list containing alpha, mu, sigma, and gamma in the form of (alpha = (alpha_1, ..., alpha_m), mu = (mu_1, ..., mu_m), sigma = (sigma_1, ..., sigma_m), gam = (gamma_1, ..., gamma_m)) |
z |
n by p matrix of regressor associated with gamma |
p |
Dimension of z |
an |
a term used for penalty function |
ninits |
The number of randomly drawn initial values. |
ninits.short |
The number of candidates used to generate an initial phi, in short MEM |
epsilon.short |
The convergence criterion in short EM. Convergence is declared when the penalized log-likelihood increases by less than |
epsilon |
The convergence criterion. Convergence is declared when the penalized log-likelihood increases by less than |
maxit.short |
The maximum number of iterations in short EM. |
maxit |
The maximum number of iterations. |
verb |
Determines whether to print a message if an error occurs. |
A list of phi, log-likelihood, and penalized log-likelihood resulting from MEM algorithm.
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