"print.summary.Krig" <-
function (x, ...) 
{
    digits <- x$digits
    c1 <- "Number of Observations:"
    c2 <- x$num.observation
    c1 <- c(c1, "Number of unique points:")
    c2 <- c(c2, x$num.uniq)
    c1 <- c(c1, "Degree of polynomial null space ( base model):")
    c2 <- c(c2, x$m - 1)
    c1 <- c(c1, "Number of parameters in the null space")
    c2 <- c(c2, x$nt)
    c1 <- c(c1, "Effective degrees of freedom:")
    c2 <- c(c2, format(round(x$enp, 1)))
    c1 <- c(c1, "Residual degrees of freedom:")
    c2 <- c(c2, format(round(x$num.observation - x$enp, 1)))
    c1 <- c(c1, "MLE sigma ")
    c2 <- c(c2, format(signif(x$shat.MLE, digits)))
    c1 <- c(c1, "GCV sigma ")
    c2 <- c(c2, format(signif(x$shat.GCV, digits)))
    if (!is.na(x$shat.pure.error)) {
        c1 <- c(c1, "Pure error sigma")
        c2 <- c(c2, format(signif(x$shat.pure.error, digits)))
    }
    c1 <- c(c1, "MLE rho ")
    c2 <- c(c2, format(signif(x$rhohat, digits)))
    c1 <- c(c1, "Scale passed for covariance (rho)")
    c2 <- c(c2, signif(x$rho, digits))
    c1 <- c(c1, "Scale passed for nugget (sigma^2)")
    c2 <- c(c2, signif(x$sigma2, digits))
    c1 <- c(c1, "Smoothing parameter lambda")
    c2 <- c(c2, signif(x$lambda, digits))
    sum <- cbind(c1, c2)
    dimnames(sum) <- list(rep("", dim(sum)[1]), rep("", dim(sum)[2]))
    res.quantile <- x$res.quantile
    names(res.quantile) <- c("min", "1st Q", "median", "3rd Q", 
        "max")

    cat("CALL:\n")
    dput(x$call)
    print(sum, quote = FALSE)
    cat("\n")
    cat("Residual Summary:", fill = TRUE)
    print(signif(res.quantile, digits))
    cat("\n")
    cat("Covariance Model:", x$cov.function, fill = TRUE)

    if( x$cov.function=="stationary.cov"){
         cat( "  Covariance function is ", x$args$Covariance, fill=TRUE)}

    if( !is.null(x$args)){ 
         cat("  Names of non-defaults covariance arguments: ", fill=TRUE)
         cat("      ",   
               paste( as.character(names( x$args)), collapse=", "),
                  fill=TRUE)}

    if ((x$correlation.model)) {
        cat(" A correlation model was fit: 
           Y is standardized before spatial estimate is found", 
                      fill = TRUE) }

    if (x$knot.model) {
        cat(" Knot model: ", x$np- x$nt, " knots supplied to define basis 
                functions", fill = TRUE)}

    cat("\n")
    cat("DETAILS ON SMOOTHING PARAMETER:", fill = TRUE)
    cat(" Method used:  ", x$method, "   Cost: ", x$cost, fill = TRUE)
    print(x$sum.gcv.lambda, digits = digits)
    cat("\n")
    cat(" Summary of all estimates found for lambda", fill = TRUE)

    if( !is.na( x$lambda.est[1])){
          print(x$lambda.est, digits = x$digits)}

    invisible(x)
}