"summary.Krig" <-
function(x, digits = 4)
{
	summary <- list(call = x$call, num.observation = length(x$residuals),
		enp = x$trace, nt = x$nt, 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$cost,
		gcvmin = min(x$gcv.grid[, 3]), rho = x$rho, sigma2 = x$sigma2,
		num.uniq = length(x$yM), knot.model = x$knot.model, np = x$
		np, method = x$method, lambda.est = x$lambda.est[!is.na(x$
		lambda.est[, 1]),  ], shat.pure.error = x$shat.pure.error)
	class(summary) <- "summary.Krig"
	summary$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$cov.function <- as.character(x$call.name)
	summary$correlation.model <- x$correlation.model
	summary$sum.gcv.lambda <- summary.gcv.Krig(x, x$lambda)
	summary
}