Nothing
rliu<-function (formula, r, R, delt, d, data = NULL, na.action, ...)
{
d <- as.matrix(d)
d1 <- d[1L]
rliues <- function(formula, r, R, delt, 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)
r <- as.matrix(r)
RC <- matrix(R, NCOL(x))
RR <- t(RC)
if (is.matrix(R))
RR <- R
else RR <- RR
del <- as.matrix(delt)
bb <- xin %*% t(x) %*% y
rb <- bb + solve(s) %*% t(RR) %*% solve(RR %*% solve(s) %*%
t(RR)) %*% (r - RR %*% bb)
I <- diag(NCOL(x))
fd <- solve(s + I) %*% (s + d1 * I)
brs <- fd %*% rb
colnames(brs) <- c("Estimate")
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
dbd <- ev * fd %*% (solve(s) - solve(s) %*% t(RR) %*%
solve(RR %*% solve(s) %*% t(RR)) %*% RR %*% solve(s)) %*%
t(fd)
bibet <- (d1 - 1) * fd %*% solve(s + d1 * I) %*% bb +
fd %*% t(RR) %*% solve(RR %*% solve(s) %*% t(RR)) %*%
del
bibets <- bibet %*% t(bibet)
mse <- dbd + bibets
mse1 <- sum(diag(mse))
Standard_error <- sqrt(diag(abs(dbd)))
rdel <- matrix(del, 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 %*% brs
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))))
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(brs, Standard_error, t_statistic, pvalue)
ans <- round(ans1, digits <- 4L)
anw <- list(`*****Restricted Liu Estimator*****` = ans,
`*****Mean square error value*****` = mse1)
return(anw)
}
npt <- rliues(formula, r, R, delt, d1, data, na.action)
plotrliu <- function(formula, r, R, delt, 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]
rlim <- function(formula, r, R, delt, 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)
r <- as.matrix(r)
RC <- matrix(R, NCOL(x))
RR <- t(RC)
if (is.matrix(R))
RR <- R
else R <- RR
del <- as.matrix(delt)
bb <- xin %*% t(x) %*% y
I <- diag(NCOL(x))
fd <- solve(s + I) %*% (s + d * I)
rb <- bb + solve(s) %*% t(RR) %*% solve(RR %*%
solve(s) %*% t(RR)) %*% (r - RR %*% bb)
brs <- fd %*% rb
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
dbd <- ev * fd %*% (solve(s) - solve(s) %*% t(RR) %*%
solve(RR %*% solve(s) %*% t(RR)) %*% RR %*%
solve(s)) %*% t(fd)
bibet <- (d - 1) * fd %*% solve(s + d * I) %*%
bb + fd %*% t(RR) %*% solve(RR %*% solve(s) %*%
t(RR)) %*% del
bibets <- bibet %*% t(bibet)
mse1 <- dbd + bibets
mse <- sum(diag(mse1))
return(mse)
}
arr[i] <- rlim(formula, r, R, delt, d[i], data, na.action)
}
MSE <- arr
parameter <- d
pvl <- cbind(parameter, MSE)
colnames(pvl) <- c("Parameter", "MSE")
sval <- pvl
return(sval)
}
prliu <- plotrliu(formula, r, R, delt, d, data, na.action)
if (nrow(d) > 1L)
val <- prliu
else val <- npt
val
}
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