R/NRWeibull.R

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

NRWeibull <- function(y, cens, X, w, beta, 
        alpha, variant, tol = 10^-6, max.iter = 1000, 
        verbose = FALSE) {
        if (missing(variant)) 
            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))
        J <- NCOL(w)
        n <- NROW(y)
        K <- NROW(beta)
        numbetes <- sum(ifelse(variant, J, 1))
        if (max.iter <= 0) {
            derivatives <- hessianWeibull(beta = beta, 
                alpha = alpha, y = y, cens = cens, 
                w = w, X = X, variant = variant)  #
            S <- derivatives$S
            H <- derivatives$H
        }
        iter <- 1
        error <- Inf
        while (error > tol & iter < max.iter) {
            derivatives <- hessianWeibull(beta = beta, 
                alpha = alpha, y = y, cens = cens, 
                w = w, X = X, variant = variant)  #
            S <- derivatives$S
            H <- derivatives$H
            beta.old <- beta
            alpha.old <- alpha
            beta.oldv <- matrix2vector(beta.old, 
                variant)
            theta.oldv <- c(beta.oldv, alpha)
            thetav <- as.vector(theta.oldv - 
                qr.solve(H) %*% S)
            betav <- thetav[seq_len(numbetes)]
            beta <- vector2matrix(betav, variant, 
                J)
            alpha <- thetav[(numbetes + 1):length(thetav)]
            error <- max(abs(thetav - theta.oldv))
            colnames(beta) <- paste("clust", seq_len(J), sep = "")
            rownames(beta) <- dimnames(X)[2][[1]]
            if (verbose) {
                if (iter == 1) 
                    cat("---- Newton-Raphson procedure ----\n")
                cat("Iter", iter, "Error", error, 
                    "\n")
                print(beta)
                cat("\n")
                cat("alpha:\n")
                print(alpha)
            }
            iter <- iter + 1
        }
        if (max.iter > 0 & iter > max.iter) 
            warning("Maximum iterations reached")
        return(list(beta = beta, alpha = alpha, 
            variant = variant, score = S, hessian = H))
}