Would someone be so kind as to explain in English what the ANOVA code
(anova.lm) is doing? I am having a hard time reconciling what the text books
have as a brute force regression and the formula algorithm in 'R'. Specifically
I see:
p <- object$rank
if (p > 0L) {
p1 <- 1L:p
comp <- object$effects[p1]
asgn <- object$assign[object$qr$pivot][p1]
nmeffects <- c("(Intercept)", attr(object$terms, "term.labels"))
tlabels <- nmeffects[1 + unique(asgn)]
ss <- c(unlist(lapply(split(comp^2, asgn), sum)), ssr)
df <- c(unlist(lapply(split(asgn, asgn), length)), dfr)
}
else {
ss <- ssr
df <- dfr
tlabels <- character(0L)
}
ms <- ss/df
f <- ms/(ssr/dfr)
P <- pf(f, df, dfr, lower.tail = FALSE)
I think I understand the check for 'p' being non-zero. 'p' is essentially the
number of terms in the model matrix (including the intercept term if it
exists). So in a mathematical description of a regression that included the
intercept and one term (like dist ~ speed) you would have a model matrix of a
column of '1's and then a column of data. The 'assign' would be a vector
containing [0,1]. So then in finding the degrees of freedom you split the
asssign matrix with itself. I am having a hard time seeing that this ever
produces degrees of freedom that are different. So I get that the vector 'df'
would always be something like [2,2,dfr]. But that is obviously wrong. Would
someone care to elighten me on what the code above is doing?
Thank you.
Kevin
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