MLE_sNiW: MLE for sNiW distributed observations

In borishejblum/NPflow: Bayesian Nonparametrics for Automatic Gating of Flow-Cytometry Data

MLE for sNiW distributed observations

Description

Maximum likelihood estimation of Normal inverse Wishart distributed observations

Usage

MLE_sNiW(xi_list, psi_list, S_list, doPlot = TRUE)

Arguments

xi_list	a list of length N whose elements are observed vectors of length d of the mean parameters xi.
psi_list	a list of length N whose elements are observed vectors of length d of the skew parameters psi.
S_list	a list of length N whose elements are observed variance-covariance matrices of dimension d x d.
doPlot	a logical flag indicating whether the algorithm progression should be plotted. Default is TRUE.

Author(s)

Boris Hejblum, Chariff Alkhassim

Examples

hyperG0 <- list()
hyperG0$b_xi <- c(0.3, -1.5)
hyperG0$b_psi <- c(0, 0)
hyperG0$kappa <- 0.001
hyperG0$D_xi <- 100
hyperG0$D_psi <- 100
hyperG0$nu <- 35
hyperG0$lambda <- diag(c(0.25,0.35))

xi_list <- list()
psi_list <- list()
S_list <- list()
for(k in 1:1000){
 NNiW <- rNNiW(hyperG0, diagVar=FALSE)
 xi_list[[k]] <- NNiW[["xi"]]
 psi_list[[k]] <- NNiW[["psi"]]
 S_list[[k]] <- NNiW[["S"]]
}

mle <- MLE_sNiW(xi_list, psi_list, S_list)
mle

borishejblum/NPflow documentation built on Feb. 2, 2024, 1:51 a.m.