pmle.norm0: compute the PMLE or MLE of the parameters under a mixture of...
In MixtureInf: Inference for Finite Mixture Models

Description Usage Arguments Value Author(s) See Also Examples

Compute the PMLE or MLE of the parameters under a mixture of normals with equal and known variance. When the level of penalty is 0, PMLE reduces to MLE.

1 2	pmle.norm0(x, var, m0 = 1, lambda = 0, inival = NULL, len = 10, niter = 50, tol = 1e-06, rformat = FALSE)

`x`	data, can be either a vector or a matrix with the 1st column being the observed values and the 2nd column being the corresponding frequencies.
`var`	known component variance.
`m0`	order of the finite mixture model, default value: m0 = 1.
`lambda`	level of penalty, default value: lambda = 0.
`inival`	initial values for the EM-algorithm, a 2m0-dimension vector including m0 mixing proportions and m0 component parameters, or a matrix with 2m0 columns, default value: inival = NULL. (if not provided, random initial values are used.)
`len`	number of random initial values for the EM-algorithm, default value: len = 10.
`niter`	number of iterations for all initial values in the EM-algorithm. The algorithm runs EM-iteration niter times from each initial value. The iteration will restart from the parameter value with the highest likelihood value at the point and run until convergence. default value: niter = 50.
`tol`	tolerance level for the convergence of the EM-algorithm, default value: tol = 1e-6.
`rformat`	form of the digital output: default of R package is used when rformat = T; If rformat = T, the digital output is rounded to the 3rd dicimal place if it is larger than 0.001, keeps 3 significant digits otherwise. The default value of rformat is F.

Return the PMLE or MLE of the parameters with order = m0 (mixing proportions and component parameters), log-likelihood value at the PMLE or MLE and the penalized log-likelihood value at the PMLE.

Shaoting Li, Jiahua Chen and Pengfei Li

emtest.norm0, plotmix.norm0, rmix.norm

#generate a random sample from a 2 component normal mixture,
#compute the PMLE of the parameters under the 2 component normal mixture model with 
#known variance 1.
x <- rmix.norm(200,c(0.3,0.7),c(-1,2))
pmle.norm0(x,var=1,2)