Nothing
ogre<-function (formula, k, data = NULL, na.action, ...)
{
k <- as.matrix(k)
k1 <- k[1L]
ogres <- function(formula, k1, 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)
I <- diag(NCOL(x))
bb <- solve(s) %*% t(x) %*% y
bk <- solve(s + k1 * I) %*% t(x) %*% y
bkve<-as.vector(bk)
j<-0
sumsq<-0
for (j in 1:NROW(bkve))
{
sumsq=(bkve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
ahat<-bk%*%t(bk)%*%solve(ev*xin+bk%*%t(bk))
ogrid<-ahat%*%bb
colnames(ogrid) <- c("Estimate")
dbd <- ev*(ahat%*%xin%*%t(ahat))
Standard_error <- sqrt(diag(abs(dbd)))
dbt <- t(ogrid)
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)*bk%*%t(bk)
mse1 <- cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(bk)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%bk
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(ogrid, Standard_error, hggh, tst)
ans <- round(ans1, digits <- 4L)
anw <- list(`*****Ordinary Generalized Ridge Regression Estimators*****` = ans,
`*****Mean square error value*****` = mse1)
return(anw)
}
npt <- ogres(formula, k1, data, na.action)
plotogre <- function(formula, k, data = NULL, na.action, ...) {
j <- 0
arr <- 0
for (j in 1:nrow(k)) {
ridm <- function(formula, k, 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)
I <- diag(NCOL(x))
bb <- solve(s) %*% t(x) %*% y
bk <- solve(s + k * I) %*% t(x) %*% y
bkve<-as.vector(bk)
j<-0
sumsq<-0
for (j in 1:NROW(bkve))
{
sumsq=(bkve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) -
NCOL(x))
ev <- diag(ev)
ahat<-bk%*%t(bk)%*%solve(ev*xin+bk%*%t(bk))
rval<-(1/cval)*bk%*%t(bk)
mse1 <-cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(bk)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%bk
mses<-as.vector(mse1)
return(mses)
}
arr[j] <- ridm(formula, k[j], data, na.action)
}
MSE <- arr
parameter <- k
pvl <- cbind(parameter, MSE)
colnames(pvl) <- c("Parameter", "MSE")
sval <- pvl
return(sval)
}
prdes <- plotogre(formula, k, data, na.action)
if (nrow(k) > 1L)
val <- prdes
else val <- npt
val
}
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