[R] Elegant way to express residual calculation in R?

[Gabor Grothendieck]

Try this:

Xm <- as.matrix(X)
X.lm <- lm(c(Xm) ~ factor(col(Xm)) + factor(row(Xm)))
sum(resid(X.lm)^2)

or if the idea was to do it without using lm try replacing
your calculation of X.predicted with this:

X.predicted <-
  outer(operator.adjustment, machine.adjustment, "+") + mean(mean(X))


On 2/27/06, John McHenry <john_d_mchenry at yahoo.com> wrote:
>    Hi All,
>
>    I am illustrating a simple, two-way ANOVA using the following data and I'm
>    having difficulty in expressing the predicted values succinctly in R.
>
>    X<- data.frame(read.table(textConnection("
>        Machine.1    Machine.2    Machine.3
>        53           61           51
>        47           55           51
>        46           52           49
>        50           58           54
>        49           54           50"
>    ), header=TRUE))
>    rownames(X)<- paste("Operator.", 1:nrow(X), sep="")
>    print(X)
>
>    # I'd like to know if there is a more elegant way to calculate the residuals
>    # than the following, which seems to be rather a kludge. If you care to read
>    # the code you'll see what I mean.
>
>    machine.adjustment<-  colMeans(X) - mean(mean(X))    # length(machine.adjustment)==3
>    operator.adjustment<- rowMeans(X) - mean(mean(X))    # length(operator.adjustment)==5
>    X.predicted<- numeric(0)
>    for (j in 1:ncol(X))
>    {
>        new.col<- mean(mean(X)) + operator.adjustment + machine.adjustment[j]
>        X.predicted<- cbind(X.predicted, new.col)
>    }
>    print(X.predicted)
>    X.residual<- X - X.predicted
>    SS.E<- sum( X.residual^2 )
>
> It seems like there ought to be some way of doing that a little bit cleaner ...
>
> Thanks,
>
> Jack.
>
>
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