# R/segTraj_EM.algo_simultanee.R In segclust2d: Bivariate Segmentation/Clustering Methods and Tools

#### Documented in EM.algo_simultanee

```# EM.algo_simultanee
#' EM.algo_simultanee calculates the MLE of phi for given change-point instants
# and for a fixed number of clusters
#' @param rupt the sequence of change points
#' @param P   number of clusters
#' @param phi starting value for the  parameter
#' @param x bivariate signal
#' @param eps eps
#' @param sameSigma TRUE if segments have the same variance
#' @return a list with  phi, the MLE, tau =(taukj) the probability for segment k
#'   to belong to class,lvinc = lvinc,empty = empty,dv = dv

EM.algo_simultanee <- function(x, rupt, P, phi, eps = 1e-6, sameSigma = FALSE) {
K <- nrow(rupt)
delta <- 1
empty <- 0
dv <- 0
tau <- matrix(1, nrow = K, ncol = P)
iter <- 0
np <- apply(tau, 2, sum)

while ((delta >= 1e-4) & (min(np) > eps) & (iter <= 500)) {
iter <- iter + 1
phi_temp <- phi
logdensity <- t(
apply(rupt, 1,
FUN = function(y) logdens_simultanee(x[, y[1]:y[2]], phi)
)
)

Estepout <- Estep_simultanee(logdensity, phi)
tau <- Estepout[[1]]

lvinc <- Estepout[[2]]

phi <- Mstep_simultanee(x, rupt, tau, phi, sameSigma)
np <- apply(tau, 2, sum)

delta <- max(unlist(lapply(names(phi), function(d) {
max(abs(phi_temp[[d]] - phi[[d]]) / phi[[d]])
})))
}

if (min(np) < eps) {
empty <- 1
lvinc <- -Inf
}

if (iter > 5000) {
dv <- 2
lvinc <- -Inf
}

rm(delta, logdensity)

invisible(list(phi = phi, tau = tau, lvinc = lvinc, empty = empty, dv = dv))
}
```

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segclust2d documentation built on Oct. 11, 2021, 9:10 a.m.