R/initSmallEM.R In valse: Variable Selection with Mixture of Models

Documented in initSmallEM

```#' initSmallEM
#'
#' initialization of the EM algorithm
#'
#' @param k number of components
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' @param fast boolean to enable or not the C function call
#'
#' @return a list with phiInit (the regression parameter reparametrized),
#' rhoInit (the covariance parameter reparametrized), piInit (the proportion parameter is the
#' mixture model), gamInit (the conditional expectation)
#'
#' @importFrom stats cutree dist hclust runif
#'
#' @export
initSmallEM <- function(k, X, Y, fast)
{
n <- nrow(X)
p <- ncol(X)
m <- ncol(Y)
nIte <- 20
Zinit1 <- array(0, dim = c(n, nIte))
betaInit1 <- array(0, dim = c(p, m, k, nIte))
sigmaInit1 <- array(0, dim = c(m, m, k, nIte))
phiInit1 <- array(0, dim = c(p, m, k, nIte))
rhoInit1 <- array(0, dim = c(m, m, k, nIte))
Gam <- matrix(0, n, k)
piInit1 <- matrix(0, nIte, k)
gamInit1 <- array(0, dim = c(n, k, nIte))
LLFinit1 <- list()

# require(MASS) #Moore-Penrose generalized inverse of matrix
for (repet in 1:nIte)
{
distance_clus <- dist(cbind(X, Y))
tree_hier <- hclust(distance_clus)
Zinit1[, repet] <- cutree(tree_hier, k)

for (r in 1:k)
{
Z <- Zinit1[, repet]
Z_indice <- seq_len(n)[Z == r]  #renvoit les indices ou Z==r
if (length(Z_indice) == 1) {
betaInit1[, , r, repet] <- MASS::ginv(crossprod(t(X[Z_indice, ]))) %*%
crossprod(t(X[Z_indice, ]), Y[Z_indice, ])
} else {
betaInit1[, , r, repet] <- MASS::ginv(crossprod(X[Z_indice, ])) %*%
crossprod(X[Z_indice, ], Y[Z_indice, ])
}
sigmaInit1[, , r, repet] <- diag(m)
phiInit1[, , r, repet] <- betaInit1[, , r, repet]  #/ sigmaInit1[,,r,repet]
rhoInit1[, , r, repet] <- solve(sigmaInit1[, , r, repet])
piInit1[repet, r] <- mean(Z == r)
}

for (i in 1:n)
{
for (r in 1:k)
{
dotProduct <- tcrossprod(Y[i, ] %*% rhoInit1[, , r, repet]
- X[i, ] %*% phiInit1[, , r, repet])
Gam[i, r] <- piInit1[repet, r] *
det(rhoInit1[, , r, repet]) * exp(-0.5 * dotProduct)
}
sumGamI <- sum(Gam[i, ])
# TODO: next line is a division by zero if dotProduct is big
gamInit1[i, , repet] <- Gam[i, ]/sumGamI
}

miniInit <- 10
maxiInit <- 11

init_EMG <- EMGLLF(phiInit1[, , , repet], rhoInit1[, , , repet], piInit1[repet, ],
gamInit1[, , repet], miniInit, maxiInit, gamma = 1, lambda = 0, X, Y,
eps = 1e-04, fast)
LLFinit1[[repet]] <- init_EMG\$llh
}
b <- which.min(LLFinit1)
phiInit <- phiInit1[, , , b]
rhoInit <- rhoInit1[, , , b]
piInit <- piInit1[b, ]
gamInit <- gamInit1[, , b]

list(phiInit = phiInit, rhoInit = rhoInit, piInit = piInit, gamInit = gamInit)
}
```

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valse documentation built on May 31, 2021, 9:10 a.m.