normalmixMaxPhi: normalmixMaxPhi

In hkasahar/normalregMix: normalregMix: An R Package for Normal Mixture Regression Models

Description

Compute ordinary & penalized log-likelihood ratio resulting from MEM algorithm at k=1,2,3.

Usage

normalmixMaxPhi(y, parlist, z = NULL, an, tauset = c(0.1, 0.3, 0.5),
  ninits = 10, epsilon.short = 0.01, epsilon = 1e-08, maxit.short = 500,
  maxit = 2000, verb = FALSE, parallel = 0.75, cl = NULL)

Arguments

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
an	a term used for penalty function
tauset	A set of initial tau value candidates
ninits	The number of randomly drawn initial values.
epsilon.short	The convergence criterion in short EM. Convergence is declared when the penalized log-likelihood increases by less than epsilon.short.
epsilon	The convergence criterion. Convergence is declared when the penalized log-likelihood increases by less than epsilon.
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.
parallel	Determines what percentage of available cores are used, represented by a double in [0,1]. 0.75 is default.
cl	Cluster used for parallelization; if it is NULL, the system will automatically create a new one for computation accordingly.

Value

A list with items:

loglik	Log-likelihood resulting from MEM algorithm at k=1,2,3.
penloglik	Penalized log-likelihood resulting from MEM algorithm at k=1,2,3.

hkasahar/normalregMix documentation built on May 17, 2019, 4 p.m.