Doug,


Anova test for fixed effects assumes that estimated random effects are the parametric values (unknown). Pinheiro & Bates (2000) comment the procedure in section 2.4. Anova.lme are based on Wald hipotesis test approach. I think something more especific is made for more complex models.

#----------------------------------------------------------
# Toy data ------------------------------------------------
da <- expand.grid(trat=factor(1:3),
                  bloc=factor(1:8))
da$y <- as.numeric(da$trat)+
  rep(rnorm(8,0,2),e=3)+
  rnorm(da$trat,0,1)

# Mixed model ---------------------------------------------
m0 <- lme(y~trat, data=da, random=~1|bloc)
anova(m0)

# test for fixed effects (Wald) ---------------------------
t(fixef(m0)[2:3])%*%           # vector of estimatives
  solve(vcov(m0)[2:3,2:3])%*%  # vcov estimated
  matrix(fixef(m0)[2:3])/2     # number of hipotesis tested

#----------------------------------------------------------

At your disposal.
Walmes Zeviani, Lavras - MG - Brasil.




________________________________
De: Doug Adams <fog0@gmx.com>
Para: R sig-mixed-models <r-sig-mixed-models@r-project.org>
Enviadas: Terça-feira, 9 de Fevereiro de 2010 5:21:52
Assunto: [R-sig-ME] anova on lmer object

Maybe this has been asked many times before, but I couldn't find it
exactly:  When you use anova() on an lmer object -- like "
anova(lmer(...)) " -- what is being calculated?  I assume it's a test
for the significance of fixed effects, but just wanted to know how
it's being done.   :)

Thanks,

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