On Wed, November 9, 2005 14:38, Peter Dalgaard wrote:
> Prof Brian Ripley <[EMAIL PROTECTED]> writes:
>
>> A multistratum aov() fit is just a list of aov() fits, so you can apply
>> functions such as Anova to the individual strata.
>>
>> However, why do you want types II and III sums of squares? I
While my original design was balanced, I lost several replicates due to
a storm, making the whole thing unbalanced. Ah, the realities of
ecology.
So, how does one look at individual strata, and then how would one
report an aggregate test of the effect in general?
On Nov 9, 2005, at 4:38 AM, P
Prof Brian Ripley <[EMAIL PROTECTED]> writes:
> A multistratum aov() fit is just a list of aov() fits, so you can apply
> functions such as Anova to the individual strata.
>
> However, why do you want types II and III sums of squares? It is usual
> to do this type of analysis only with balance
A multistratum aov() fit is just a list of aov() fits, so you can apply
functions such as Anova to the individual strata.
However, why do you want types II and III sums of squares? It is usual
to do this type of analysis only with balanced designs. In the cases I
can envisage that these make
I've recently run into the problem of using aov with nested factors,
and wanting to get the type II and III sums of squares. Normally Anova
from the car package would do fine, but it doesn't like having an Error
included, so
my.aov <-aov(Response ~ Treatment + Error(Treatment:Replicate))
Anova
