R/mix.r

In lrmest: Different Types of Estimators to Deal with Multicollinearity

mixe<-function (formula, r, R, dpn, delt, data, na.action, ...) 
{
    cal <- match.call(expand.dots = FALSE)
    mat <- match(c("formula", "data", "na.action"), names(cal))
    cal <- cal[c(1L, mat)]
    cal[[1L]] <- as.name("model.frame")
    cal <- eval(cal)
    y <- model.response(cal)
    md <- attr(cal, "terms")
    x <- model.matrix(md, cal, contrasts)
    s <- t(x) %*% x
    xin <- solve(s)
    r <- as.matrix(r)
    RC <- matrix(R, NCOL(s))
    RR <- t(RC)
    if (is.matrix(R)) 
        RR <- R
    else RR <- RR
    if (length(dpn) == 1L) 
        shi <- dpn
    else if (is.matrix(dpn)) 
        shi <- dpn
    else shi <- diag(dpn)
    de1 <- as.matrix(delt)
    bb <- xin %*% t(x) %*% y
    ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) - NCOL(x))
    ev <- diag(ev)
    w1 <- solve(s/ev + t(RR) %*% solve(shi) %*% RR)
    w2 <- (t(x) %*% y)/ev + t(RR) %*% solve(shi) %*% r
    bm <- w1 %*% w2
    colnames(bm) <- c("Estimate")
    dbd <- w1
    Standard_error <- sqrt(diag(abs(dbd)))
    dbd <- w1
    rdel <- matrix(delt, NROW(RR))
    lenr <- length(RR)
    dlpt <- diag(RR %*% xin %*% t(RR))
    if (lenr == ncol(RR)) 
        ilpt <- sqrt(solve(abs(dlpt)))
    else ilpt <- sqrt(solve(diag(abs(dlpt))))
    upt <- RR %*% bm
    tb <- t(upt)
    t_statistic <- ((tb - t(rdel)) %*% ilpt)/sqrt(ev)
    tst <- t(2L * pt(-abs(t_statistic), df = (NROW(x) - NCOL(x))))
    pvalue <- c(tst, rep(NA, (NCOL(x) - NROW(RR))))
    bibet <- xin %*% t(RR) %*% solve((shi/ev) + RR %*% xin %*% 
        t(RR)) %*% de1
    bibets <- bibet %*% t(bibet)
    mse <- dbd + bibets
    mse1 <- sum(diag(mse))
    mse1 <- round(mse1, digits <- 4L)
    names(mse1) <- c("MSE")
    t_statistic <- c(t_statistic, rep(NA, (NCOL(x) - NROW(RR))))
    ans1 <- cbind(bm, Standard_error, t_statistic, pvalue)
    ans <- round(ans1, digits <- 4L)
    anw <- list(`*****Mixed  Regression Estimator*****` = ans, 
        `*****Mean square error value*****` = mse1)
    anw
}