Nothing
ogliu<-function (formula, d, data = NULL, na.action, ...)
{
d <- as.matrix(d)
d1 <- d[1L]
oglue <- 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
bcdve<-as.vector(bcd)
j<-0
sumsq<-0
for (j in 1:NROW(bcdve))
{
sumsq=(bcdve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
ahat<-bcd%*%t(bcd)%*%solve(ev*xin+bcd%*%t(bcd))
oglu<-ahat%*%bb
colnames(oglu) <- c("Estimate")
dbd <- ev*ahat%*%xin%*%t(ahat)
Standard_error <- sqrt(diag(abs(dbd)))
dbt <- t(oglu)
dbd <- ev*ahat%*%xin%*%t(ahat)
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)
rval<-(1/cval)*bcd%*%t(bcd)
mse1 <- cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(bcd)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%bcd
mse1<-as.vector(mse1)
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(oglu, Standard_error, hggh, tst)
ans <- round(ans1, digits = 4L)
anw <- list(`*****Ordinary Generalized Liu Estimator*****` = ans, `*****Mean square error value*****` = mse1)
return(anw)
}
npt <- oglue(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]
oglum <- 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
bcdve<-as.vector(bcd)
j<-0
sumsq<-0
for (j in 1:NROW(bcdve))
{
sumsq=(bcdve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
ahat<-bcd%*%t(bcd)%*%solve(ev*xin+bcd%*%t(bcd))
dbd <- ev*ahat%*%xin%*%t(ahat)
rval<-(1/cval)*bcd%*%t(bcd)
mse1 <- cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(bcd)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%bcd
mse<-as.vector(mse1)
return(mse)
}
arr[i] <- oglum(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
}
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