Nothing
ogols=function (formula, 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)
bb <- xin %*% t(x) %*% y
bbve<-as.vector(bb)
j<-0
sumsq<-0
for (j in 1:NROW(bbve))
{
sumsq=(bbve[j])^2+sumsq
}
cval<-sumsq
ev <- (t(y) %*% y - t(bb) %*% t(x) %*% y)/(NROW(x) - NCOL(x))
ev <- diag(ev)
ahat<-bb%*%t(bb)%*%solve(ev*xin+bb%*%t(bb))
goeol<-ahat%*%bb
colnames(goeol) <- c("Estimate")
dbb <- ev*(ahat%*%xin%*%t(ahat))
Standard_error <- sqrt(diag(abs(dbb)))
dbt <- t(goeol)
sdbd_inv <- (sqrt(diag(abs(dbb))))^-1
sdbd_inv_mat <- diag(sdbd_inv)
if (NCOL(dbt) == 1L)
tbb <- dbt * sdbd_inv
else tbb <- dbt %*% sdbd_inv_mat
tst <- t(tbb)
pval <- t(2 * pt(-abs(tbb), df <- (NROW(x) - NCOL(x))))
colnames(pval) <- c("p_value")
colnames(tst) <- c("t_statistic")
I <- diag(NCOL(x))
rval<-(1/cval)*bb%*%t(bb)
mse1 <-cval^2*ev*tr(ev*rval*solve(ev*xin+cval*rval)%*%xin%*%solve(ev*xin+cval*rval)%*%rval)+ev^2*t(bb)%*%solve(ev*I+cval*rval%*%s)%*%solve(ev*I+cval*rval%*%s)%*%bb
mse1<-as.vector(mse1)
names(mse1) <- c("MSE")
mse1 <- round(mse1, digits <- 4L)
ans <- cbind(goeol, Standard_error, tst, pval)
ans1 <- round(ans, digits <- 4L)
adw <- list('*****Ordinary Generalized Least Square Estimator******' = ans1,'*****Mean square error value*****' = mse1)
adw
}
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