Nothing
ogrliu<-function (formula, r, R, delt, d, data = NULL, na.action, ...)
{
d <- as.matrix(d)
d1 <- d[1L]
ogrliues <- 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
brsve<-as.vector(brs)
j<-0
sumsq<-0
for (j in 1:NROW(brsve))
{
sumsq=(brsve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
ahat<-brs%*%t(brs)%*%solve(ev*xin+brs%*%t(brs))
ogrle<-ahat%*%bb
colnames(ogrle) <- c("Estimate")
dbd <- ev*(ahat%*%xin%*%t(ahat))
rval<-(1/cval)*brs%*%t(brs)
mse1 <- cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(brs)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%brs
mse1<-as.vector(mse1)
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 %*% ogrle
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 <- round(mse1, digits <- 4L)
names(mse1) <- c("MSE")
t_statistic <- c(t_statistic, rep(NA, (NCOL(x) - NROW(RR))))
ans1 <- cbind(ogrle, Standard_error, t_statistic, pvalue)
ans <- round(ans1, digits <- 4L)
anw <- list(`*****Ordinary Generalized Restricted Liu Estimator*****` = ans,
`*****Mean square error value*****` = mse1)
return(anw)
}
npt <- ogrliues(formula, r, R, delt, d1, data, na.action)
plotogrliu <- 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]
ogrlm <- 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
brsve<-as.vector(brs)
j<-0
sumsq<-0
for (j in 1:NROW(brsve))
{
sumsq=(brsve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
ahat<-brs%*%t(brs)%*%solve(ev*xin+brs%*%t(brs))
dbd <- ev*(ahat%*%xin%*%t(ahat))
rval<-(1/cval)*brs%*%t(brs)
mse1 <-cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(brs)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%brs
mse1<-as.vector(mse1)
return(mse1)
}
arr[i] <- ogrlm(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 <- plotogrliu(formula, r, R, delt, d, data, na.action)
if (nrow(d) > 1L)
val <- prliu
else val <- npt
val
}
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.