LC.MLE: LC.MLE function
In pzwiernik/LatentClass: Estimation of the latent class model for discrete data

Description Usage Arguments Details Examples

View source: R/LC.MLE.R

This function estimates parameters of the latent class model using the maximum likelihood method. As a subroutine it uses constrOptim().

1	LC.MLE(counts, k, tries = 3, theta = NULL, tol = 1e-08, mode = "optim")

`counts`	The array of counts of format `(r[1],...,r[m])`
`k`	The number of latent classes fitted
`tries`	The number of times the algorithm reruns from different random starting points. The default value is tries=3
`theta`	The vector of parameters from which the algorithm starts. If not specified, the algorithm starts from a random point.
`tol`	The convergence criterion for the EM algorithm. The maximal decrease of the log-likelihood function that will terminate the algorithm.
`mode`	The algorithm used to compute the MLE. Write `'EM'` to use the EM algorithm and `'optim'` to use the standard R constrained optimization routine `constrOptim()`.

This function works as an alternative to LC.EM() that uses the EM algorithm.

theta0 <- list()
theta0[[1]] <- matrix(c(0.9,0.2,0.1,0.8),2,2)
theta0[[2]] <- matrix(c(0.7,0.2,0.3,0.8),2,2)
theta0[[3]] <- matrix(c(0.9,0.1,0.1,0.9),2,2)
theta0[[4]] <- matrix(c(0.7,0.1,0.3,0.9),2,2)
theta0[[5]] <- matrix(c(0.7,0.1,0.3,0.9),2,2)
theta0[[6]] <- c(0.3,0.7)
data <- sample.counts(500,theta0)
LC.MLE(data,2,tries=3,tol=1e-8,margin.size=3)