Nothing
alte1<-function (formula, k, d, aa, press = FALSE, data = NULL, na.action,
...)
{
k <- as.matrix(k)
d <- as.matrix(d)
k1 <- k[1L]
d1 <- d[1L]
if (length(aa) == 1L)
A <- as.matrix(aa)
else A <- diag(aa)
altes <- function(formula, k1, d1, aa, 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, "numeric")
md <- attr(cal, "terms")
x <- model.matrix(md, cal, contrasts)
s <- t(x) %*% x
I <- diag(NCOL(x))
bb1 <- solve(s) %*% t(x) %*% y
bk1 <- (solve(s + k1 * I) - d1 * solve(s + k1 * I) %*%
solve(s)) %*% t(x) %*% y
bk <- A %*% bk1
colnames(bk) <- c("Estimate")
ev <- (t(y) %*% y - t(bb1) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
dbd <- ev * A %*% (solve(s + k1 * I) - d1 * solve(s +
k1 * I) %*% solve(s)) %*% s %*% (solve(s + k1 * I) -
d1 * solve(s) %*% solve(s + k1 * I)) %*% t(A)
Standard_error <- sqrt(diag(abs(dbd)))
dbt <- t(bk)
dbd <- ev * A %*% (solve(s + k1 * I) - d1 * solve(s +
k1 * I) %*% solve(s)) %*% s %*% (solve(s + k1 * I) -
d1 * solve(s) %*% solve(s + k1 * I)) %*% t(A)
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)
tst <- t(2L * pt(-abs(tbd), df <- (NROW(x) - NCOL(x))))
colnames(tst) <- c("p_value")
colnames(hggh) <- c("t_statistic")
bibet <- A %*% (solve(s + k1 * I) %*% s - d1 * solve(s +
k1 * I) - I) %*% bb1
bibets <- bibet %*% t(bibet)
mse <- dbd + bibets
mse1 <- sum(diag(mse))
mse1 <- round(mse1, digits <- 4L)
names(mse1) <- c("MSE")
ab <- matrix(aa, NCOL(x))
a <- t(ab)
pr <- 0
i <- 1
m <- 1
for (i in 1:nrow(x)) {
subsum <- 0
bb <- c(x[i, ] %*% t(x[i, ]))
z <- (solve(s + k1 * I - bb) - d1 * solve(s + k1 *
I - bb) %*% solve(s - bb)) %*% (t(x) %*% y -
x[i, ] * y[i])
for (m in 1:ncol(x)) subsum <- subsum + (a[m] %*%
x[i, m] %*% z[m])
pr <- pr + (y[i] - subsum)^2
}
pre <- t(pr)
colnames(pre) <- c("PRESS")
ans1 <- cbind(bk, Standard_error, hggh, tst)
ans1 <- round(ans1, digits = 4L)
pre <- round(pre, digits = 4L)
rownames(ans1) <- rownames(bb1)
anw <- list(`*****Type (1) Adjusted Liu Estimator*****` = ans1,
`****Mean Square Error value*****` = mse1, `*****Prediction Sum of Squares value*****` = pre)
return(anw)
}
npt <- altes(formula, k1, d1, aa, data, na.action)
plotalt1 <- function(formula, k, d, aa, data = NULL, na.action,
...) {
i <- 0
j <- 0
arr <- 0
for (j in 1:nrow(k)) {
for (i in 1:nrow(d)) {
altem1 <- function(formula, k, d, aa, 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, "numeric")
md <- attr(cal, "terms")
x <- model.matrix(md, cal, contrasts)
s <- t(x) %*% x
I <- diag(NCOL(x))
bb <- solve(t(x) %*% x) %*% t(x) %*% y
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
dbd <- ev * A %*% (solve(s + k * I) - d * solve(s +
k * I) %*% solve(s)) %*% s %*% (solve(s +
k * I) - d * solve(s) %*% solve(s + k * I)) %*%
t(A)
bibet <- A %*% (solve(s + k * I) %*% s - d *
solve(s + k * I) - I) %*% bb
bibets <- bibet %*% t(bibet)
mse <- dbd + bibets
mse1 <- sum(diag(mse))
return(mse1)
}
arr[i * j] <- altem1(formula, k[j], d[i], aa,
data, na.action)
falt1 = file("alt1.data", "a+")
cat(k[j], d[i], arr[i * j], "\n", file = falt1,
append = TRUE)
close(falt1)
}
}
mat <- read.table("alt1.data")
unlink("alt1.data")
rmat <- matrix(mat[, 3L], nrow = NROW(d), dimnames = list(c(paste0("d=",
d)), c(paste0("k=", k))))
return(rmat)
}
pl1 <- plotalt1(formula, k, d, aa, data, na.action)
plotpralt1 <- function(formula, k, d, aa, data = NULL, na.action,
...) {
j <- 0
i <- 0
arr <- 0
for (j in 1:nrow(k)) {
for (i in 1:nrow(d)) {
apre <- function(formula, k, d, aa, 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
I <- diag(NCOL(x))
ab <- matrix(aa, NCOL(x))
a <- t(ab)
pr <- 0
i <- 1
m <- 1
for (i in 1:nrow(x)) {
subsum <- 0
bb <- c(x[i, ] %*% t(x[i, ]))
z <- (solve(s + k * I - bb) - d * solve(s +
k * I - bb) %*% solve(s - bb)) %*% (t(x) %*%
y - x[i, ] * y[i])
for (m in 1:ncol(x)) subsum <- subsum + (a[m] %*%
x[i, m] %*% z[m])
pr <- pr + (y[i] - subsum)^2
}
pre <- t(pr)
return(pre)
}
arr[j * i] <- apre(formula, k[j], d[i], aa, data,
na.action)
fpralte1 = file("praltev1.data", "a+")
cat(k[j], d[i], arr[j * i], "\n", file = fpralte1,
append = TRUE)
close(fpralte1)
}
}
pmat <- read.table("praltev1.data")
unlink("praltev1.data")
rpmat <- matrix(pmat[, 3L], nrow = NROW(d), dimnames = list(c(paste0("d=",
d)), c(paste0("k=", k))))
return(rpmat)
}
pralt1 <- plotpralt1(formula, k, d, aa, data, na.action)
if (!press)
prmse <- pl1
else prmse <- pralt1
if (nrow(k) > 1L | nrow(d) > 1L)
val <- prmse
else val <- npt
val
}
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