


Hey
listers,

 



 

It&rsquo;s good to know that I still
have a lot of search to do&hellip;

 

According to the two procedures AOV
and LME, I got two different results and I didn&rsquo;t understand at all
the results of LME&hellip; There is a coefficient estimate of each level
and I just pretend to test if the effects of the factors and interaction
are significant or not&hellip;So I would like to learn more about this
function, because it's adequate for split-plot design...
test.anova=
aov(mes ~ nitro*thatch + Error(block/nitro), data=test)

 

summary(test.anova)

 

test.lme
<- lme(mes ~ nitro*thatch,random= ~ 1|block/nitro, data=test)

 

summary(test.lme)

 

Does anybody has any document or
reference that explain this results in details of the lme&hellip;
I&rsquo;ve looked for, but I didn&rsquo;t find any good
explication&hellip; I notice that there is a lot of information about the
lmer, but I am going step by step&hellip;

 



 

Thanks,

 



 

Ribeiro

 


 
> Thanks for your response John. I just have one quick comment
about 
> the Kenward-Roger degrees of freedom calculation. It has
been some 
> time since I looked at that paper but my impression
at the time was 
> that the equations would not easily translate
into the formulation 
> used in lmer. The approach used in lmer is
like that of Henderson's 
> mixed model equations (with many
modifications). That is, it is based 
> on a penalized least
squares problem, not a generalized least squares 
> problem. My
recollection is that Kenward and Roger wrote their 
> equations in
terms of the generalized least squares problem. 
> 
> You
are not the first person to suggest that incorporating the 
>
Kenward-Roger calculation would enhance lmer. The reason I haven't 
> done so is that I believe it is far from trivial to do so and I
have 
> many, many other enhancements I would prefer to spend my
time on. 
> However, this is open source software and any
enterprising person who 
> wants to implement it is more than
welcome to do so. It may be 
> sufficiently involved to be a
thesis topic - I don't know because I 
> haven't studied it
carefully enough. 
> 
> Any person considering doing that
should read or reread Bill Venables' 
> "Exegeses on Linear
Models" before embarking on it. As he points out 
> in his
discussion of modifications made in S-PLUS to emulate some 
>
calculations in SAS, the "brute force" approach of taking a set
of 
> equations and implementing them literally is rarely a good
approach. 
> So I am not talking about a "pidgin R"
implementation here where a 
> linear least squares calculation is
written 
> 
> XpX <- t(X) %*% X 
> XpXinv <-
solve(XpX) 
> Xpy <- t(X) %*% y 
> betahat <- XpXinv
%*% Xpy 
> 
> An mer object includes slots L, RZX and RX
that define the Cholesky 
> decomposition of the crossproduct
matrix that is more-or-less like 
> that in the Henderson mixed
model equations. The L slot itself has a 
> Perm slot that gives
the fill-reducing permutation P for the random 
> effects. That
may not be relevant - I'm not sure. The original model 
> matrices
are available as the X and Zt (transpose of Z) slots. The 
>
(transpose of the) derived model matrix for the orthogonal random 
> effects is available as A. The terms attribute of the model matrix
X 
> and the assign attribute of the model frame (in the slot
named 
> "frame") should be used to associate terms with
columns of the model 
> matrix. 
> 
> It's
possible that the calculation would be straightforward. As i 
>
said, I don't know. My gut feeling is that it is not, which is why I 
> haven't embarked on it. 
> 
>
_______________________________________________ 
>
R-sig-mixed-models@r-project.org mailing list 
>
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models 
> 


	[[alternative HTML version deleted]]


