Lack of Fit Test
I wrote a small function which would do Lack of fit tet for you.  First copy avrything in red area, and paste it in Splus (you have to do it only once.
    LF.test<-function(fit){
    my.paste<-function(x) return(paste(x,collapse=" "))
    X<-model.matrix(fit)
    Y<-X%*%fit$coef+fit$res
    x<-apply(X,1,my.paste)
    return(anova(lm(Y~X-1),lm(Y~x)))
    }
Now to test linear model, which you say named fit, you just need to type LF.test(fit).

Now let's do an example from the book. (Chapte 3, pages 115-124)

    > data<-read.table("/afs/umich.edu/user/k/u/kutsyy/Public/html/classes/ALSM/CH03TA04.DAT")
    > data
        V1  V2
     1 125 160
     2 100 112
     3 200 124
     4  75  28
     5 150 152
     6 175 156
     7  75  42
     8 175 124
     9 125 150
    10 200 104
    11 100 136
    > X<-data[,1]
    > Y<-data[,2]
    > fit<-lm(Y~X)
    > summary(fit)

    Call: lm(formula = Y ~ X)
    Residuals:
        Min     1Q Median    3Q   Max
     -59.23 -34.06  12.61 32.44 48.44

    Coefficients:
                  Value Std. Error t value Pr(>|t|)
    (Intercept) 50.7225 39.3979     1.2874  0.2301
              X  0.4867  0.2747     1.7717  0.1102

    Residual standard error: 40.47 on 9 degrees of freedom
    Multiple R-Squared: 0.2586
    F-statistic: 3.139 on 1 and 9 degrees of freedom, the p-value is 0.1102

    Correlation of Coefficients:
      (Intercept)
    X -0.9508
    > anova(fit)
    Analysis of Variance Table

    Response: Y

    Terms added sequentially (first to last)
              Df Sum of Sq  Mean Sq  F Value     Pr(F)
    X          1   5141.34 5141.338 3.138882 0.1102125
    Residuals  9  14741.57 1637.952
    > LF.test(fit)
    Analysis of Variance Table

    Response: Y

      Terms Resid. Df      RSS    Test Df Sum of Sq  F Value       Pr(F)
    1 X - 1         9 14741.57
    2     x         5  1148.00 1 vs. 2  4  13593.57 14.80136 0.005593812

Note that 1148.00 is Pure error sum of squares.  0.00559 is p-value for that sum of squares, and it is exactly the same as in the book (page 121).