nMm2par {norMmix}

Multivariate Normal Mixture Model to parameter for MLE

Description

From a "norMmix"(-like) object, return the numeric parameter vector in our MLE parametrization.

Usage

  nMm2par(obj,
        model = c("EII", "VII", "EEI", "VEI", "EVI",
                  "VVI", "EEE", "VEE", "EVV", "VVV"),
        meanFUN = mean.default,
        checkX = FALSE)

Arguments

obj

a list containing

sig:

covariance matrix array,

mu:

mean vector matrix,

w:

= weights,

k:

= number of components,

p:

= dimension

model

a character string specifying the (Sigma) model, one of those listed above.

meanFUN

a function to compute a mean (of variances typically).

checkX

a boolean. check for positive definiteness of covariance matrix.

Details

This transformation forms a vector from the parameters of a normal mixture. These consist of weights, means and covariance matrices.

Covariance matrices are given as D and L from the LDLt decomposition

Value

vector containing encoded parameters of the mixture. first, the centered log ratio of the weights, then the means, and then the model specific encoding of the covariances.

See Also

the inverse function of nMm2par() is par2nMm().

Examples

A <- MW24
nMm2par(A, model = A$model)
# [1] -0.3465736  0.0000000  0.0000000  0.0000000  0.0000000  0.0000000
# [7] -2.3025851

## All MW* models in {norMmix} pkg:
pkg <- "package:norMmix"
lMW <- mget(ls(pattern = "^MW", pkg), envir=as.environment(pkg))
lM.par <- lapply(lMW, nMm2par)
## but these *do* differ  ___ FIXME __ 
modMW <- vapply(lMW, `[[`, "model", FUN.VALUE = "XYZ")
cbind(modMW, lengths(lM.par),  npar = sapply(lMW, npar))[order(modMW),]