R/EMpoisson.R

In isglobal-brge/CNVassoc: Association analysis of CNVs and imputed SNPs incorporating uncertainty

EMpoisson <- function(y, X, w, beta, tol = 10^-6, 
        max.iter = 1000, verbose = FALSE) {
        variant <- !apply(is.na(beta), 1, any)
        beta <- t(apply(beta, 1, function(x) if (any(is.na(x))) 
            rep(x[1], length(x)) else x))
        P <- ncol(X)
        J <- ncol(w)
        N <- length(y)
        error <- Inf
        iter <- 1
        latent <- rep(seq_len(J), each = N)
        X.expand <- NULL
        for (p in seq_len(P)) {
            xp <- cbind(as.vector(X[, p, ]))
            colnames(xp) <- paste("V", p, sep = "")
            if (variant[p]) {
                xp <- model.matrix(~xp:as.factor(latent) - 
                    1)
                colnames(xp) <- paste(rep("V", 
                    J), p, sep = "")
            }
            X.expand <- cbind(X.expand, xp)
        }
        y.expand <- rep(y, J)
        while (error > tol & iter < max.iter) {
            beta.old <- beta
            eta <- vapply(seq(1,J), function(j) {
                Xj <- cbind(X[, , j])
                betaj <- beta[, j, drop = FALSE]
                Xj %*% betaj
            }, rep(0, N))
            lambda <- exp(eta)  ##
            dY <- vapply(seq(1,J), function(j) dpois(y, 
                lambda[, j]), rep(0, N))  ##
            wp <- dY * w
            wp <- wp/rowSums(wp)
            coeffic <- poisson.fit(x = X.expand, 
                y = y.expand, weights = as.vector(wp))
            beta <- vector2matrix(betav = coeffic, 
                variant = variant, J = J)
            error <- max(abs(beta - beta.old))
            beta.old <- beta
            colnames(beta) <- paste("clust", seq_len(J), sep = "")
            rownames(beta) <- dimnames(X)[2][[1]]
            if (verbose) {
                if (iter == 1) 
                    cat("--- EM procedure ---\n")
                cat("Iter", iter, "Error", error, 
                    "\n")
                print(beta)
                cat("\n")
            }
            iter <- iter + 1
        }
        return(list(beta = beta, variant = variant))
}