Nothing
mixe<-function (formula, r, R, dpn, 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)
RC <- matrix(R, NCOL(s))
RR <- t(RC)
if (is.matrix(R))
RR <- R
else RR <- RR
if (length(dpn) == 1L)
shi <- dpn
else if (is.matrix(dpn))
shi <- dpn
else shi <- diag(dpn)
de1 <- as.matrix(delt)
bb <- xin %*% t(x) %*% y
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) - NCOL(x))
ev <- diag(ev)
w1 <- solve(s/ev + t(RR) %*% solve(shi) %*% RR)
w2 <- (t(x) %*% y)/ev + t(RR) %*% solve(shi) %*% r
bm <- w1 %*% w2
colnames(bm) <- c("Estimate")
dbd <- w1
Standard_error <- sqrt(diag(abs(dbd)))
dbd <- w1
rdel <- matrix(delt, 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 %*% bm
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))))
bibet <- xin %*% t(RR) %*% solve((shi/ev) + RR %*% xin %*%
t(RR)) %*% de1
bibets <- bibet %*% t(bibet)
mse <- dbd + bibets
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(bm, Standard_error, t_statistic, pvalue)
ans <- round(ans1, digits <- 4L)
anw <- list(`*****Mixed Regression Estimator*****` = ans,
`*****Mean square error value*****` = mse1)
anw
}
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