Message-ID: <51288685.90003@xtra.co.nz>
Date: 2013-02-23T09:06:13Z
From: Rolf Turner
Subject: anova comparisons
In-Reply-To: <CAGJyvhE38kYNsgVNYqoJK+GCq8+=s23nLRRWrx9AS-yrgXypKQ@mail.gmail.com>
On 02/23/2013 08:55 PM, Robert Zimbardo wrote:
> I have several linear models on the same data:
>
> m1 <- lm(y ~ poly(x,1))
> m2 <- lm(y ~ poly(x,2))
> m3 <- lm(y ~ poly(x,3))
>
> What I don't understand is why
>
> anova(m1, m2, m3, test="F")
>
> - yields the same RSS and SS values, but a different p-value from anova(m1,
> m2, test="F")
> - when it also yields the SAME as anova(m2, m3, test="F")
>
> What am I missing?
A basic understanding of the theory of linear models. This really has
little
to do with R. Go and read a good intro to linear modelling.
Insofar as your question has anything to do with R:
When you do
anova(m1, m2, m3, test="F")
the mean squared error from m3 is used as the denominator of the F
statistic.
When you do
anova(m1, m2, test="F")
the mean squared error from m2 is used as the denominator of the F
statistic.
cheers,
Rolf Turner