R/liue.r

In lrmest: Different Types of Estimators to Deal with Multicollinearity

liu<-function (formula, d, data = NULL, na.action, ...) 
{
    d <- as.matrix(d)
    d1 <- d[1L]
    liues <- function(formula, d1, data = NULL, 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)
        bb <- xin %*% t(x) %*% y
        I <- diag(NCOL(x))
        fd <- solve(s + I) %*% (s + d1 * I)
        bcd <- fd %*% bb
        colnames(bcd) <- c("Estimate")
        ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) - 
            NCOL(x))
        ev <- diag(ev)
        dbd <- ev * fd %*% xin %*% t(fd)
        Standard_error <- sqrt(diag(abs(dbd)))
        dbt <- t(bcd)
        dbd <- ev * fd %*% solve(s) %*% t(fd)
        sdbd_inv <- (sqrt(diag(abs(dbd))))^-1
        sdbd_inv_mat <- diag(sdbd_inv)
        if (NCOL(dbt) == 1L) 
            tbd <- dbt * sdbd_inv
        else tbd <- dbt %*% sdbd_inv_mat
        hggh <- t(tbd)
        bibet <- (d1 - 1) * fd %*% solve(s + d1 * I) %*% bb
        bibets <- bibet %*% t(bibet)
        mse <- dbd + bibets
        mse1 <- sum(diag(mse))
        mse1 <- round(mse1, digits = 4L)
        names(mse1) <- c("MSE")
        tst <- t(2L * pt(-abs(tbd), df <- (NROW(x) - NCOL(x))))
        colnames(tst) <- c("p_value")
        colnames(hggh) <- c("t_statistic")
        ans1 <- cbind(bcd, Standard_error, hggh, tst)
        ans <- round(ans1, digits = 4L)
        anw <- list(`*****Liu Estimator*****` = ans, `*****Mean square error value*****` = mse1)
        return(anw)
    }
    npt <- liues(formula, d1, data, na.action)
    plotliu <- function(formula, d, data = NULL, na.action, ...) {
        i <- 0
        arr <- 0
        for (i in 1:NROW(d)) {
            if (d[i] < 0L) 
                d[i] <- 0L
            else d[i] <- d[i]
            if (d[i] > 1L) 
                d[i] <- 1L
            else d[i] <- d[i]
            liums <- function(formula, d, 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)
                bb <- xin %*% t(x) %*% y
                I <- diag(NCOL(x))
                fd <- solve(s + I) %*% (s + d * I)
                bcd <- fd %*% bb
                ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) - 
                  NCOL(x))
                ev <- diag(ev)
                dbd <- ev * fd %*% xin %*% t(fd)
                bibet <- (d - 1) * fd %*% solve(s + d * I) %*% 
                  bb
                bibets <- bibet %*% t(bibet)
                mse <- dbd + bibets
                mses <- sum(diag(mse))
                return(mses)
            }
            arr[i] <- liums(formula, d[i], data, na.action)
        }
        MSE <- arr
        parameter <- d
        pvl <- cbind(parameter, MSE)
        colnames(pvl) <- c("Parameter", "MSE")
        sval <- pvl
        return(sval)
    }
    pliu <- plotliu(formula, d, data, na.action)
    if (nrow(d) > 1L) 
        val <- pliu
    else val <- npt
    val
}