summary.Krig.r
"summary.Krig" <-
function (object, digits = 4, ...)
{
x <- object
# lambda est may not be available if lambda has been supplied by user.
if (!is.na(x$lambda.est[1])){
l.est<- x$lambda.est}
else{
l.est<- NA}
summary <- list(call = x$call, num.observation = length(x$residuals),
enp = x$eff.df, nt = x$nt, df.drift= sum(x$ind.drift),
res.quantile = quantile(x$residuals,
seq(0, 1, 0.25)), shat.MLE = x$shat.MLE, shat.GCV = x$shat.GCV,
rhohat = x$rhohat, m = x$m, lambda = x$lambda, cost = x$cost,
rho = x$rho, sigma2 = x$sigma2,
num.uniq = length(x$yM), knot.model = x$knot.model, np = x$np,
method = x$method, lambda.est = l.est,
shat.pure.error = x$shat.pure.error, args=x$args)
class(summary) <- "summary.Krig"
summary$covariance <- cor(x$fitted.values * sqrt(x$weights),
(x$y) * sqrt(x$weights))^2
hold <- (sum((x$y - mean(x$y))^2) - sum(x$residuals^2))/(sum((x$y -
mean(x$y))^2))
summary$adjr2 <- 1 - ((length(x$residuals) - 1)/(length(x$residuals) -
x$eff.df)) * (1 - hold)
summary$digits <- digits
summary$cov.function <- as.character(x$cov.function.name)
summary$correlation.model <- x$correlation.model
summary$sum.gcv.lambda <- summary.gcv.Krig(x, x$lambda)
summary
}