Nothing
ogrls<-function (formula, r, R, 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)
I <- diag(NCOL(x))
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)
rbve<-as.vector(rb)
j<-0
sumsq<-0
for (j in 1:NROW(rbve))
{
sumsq=(rbve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) - NCOL(x))
ev <- diag(ev)
ahat<-rb%*%t(rb)%*%solve(ev*xin+rb%*%t(rb))
ogrlse<-ahat%*%bb
colnames(ogrlse) <- c("Estimate")
dbd <- ev*(ahat%*%xin%*%t(ahat))
Standard_error <- sqrt(diag(abs(dbd)))
dbd <- ev*(ahat%*%xin%*%t(ahat))
rval<-(1/cval)*rb%*%t(rb)
mse1 <-cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(rb)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%rb
mse1<-as.vector(mse1)
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 %*% ogrlse
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(ogrlse, Standard_error, t_statistic, pvalue)
ans <- round(ans1, digits <- 4L)
anw <- list(`*****Ordinary Generalized Restricted Least Square Estimator*****` = ans,
`*****Mean square error value******` = mse1)
anw
}
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