
   Sorry, actually it does not fail when I do it now, but I'm pretty
 sure it did at some point. But probably it's my fault, I might have 
forgotten to write REML=FALSE and I did not realize. Sorry about that. 
It's R 2.12.0.

>datecoltailmodel1<-lmer(jdate~sex*briventral*inslarge*weatherpc1*tlength-sex:briventral:inslarge:weatherpc1:tlength-sex:briventral:inslarge:weatherpc1-sex:briventral:inslarge:tlength-sex:briventral:weatherpc1:tlength-sex:inslarge:weatherpc1:tlength-briventral:inslarge:weatherpc1:tlength-sex:briventral:inslarge-sex:briventral:weatherpc1-sex:briventral:tlength-sex:inslarge:weatherpc1-sex:inslarge:tlength-sex:weatherpc1:tlength-briventral:inslarge:weatherpc1-briventral:inslarge:tlength-briventral:weatherpc1:tlength-inslarge:weatherpc1:tlength+(1|site/pair)+(1|year),REML=FALSE)
>
 summary(datecoltailmodel1)
Linear mixed model fit by maximum likelihood 
Formula:
 jdate ~ sex * briventral * inslarge * weatherpc1 * tlength -      
sex:briventral:inslarge:weatherpc1:tlength - 
sex:briventral:inslarge:weatherpc1 -      
sex:briventral:inslarge:tlength - sex:briventral:weatherpc1:tlength 
-      sex:inslarge:weatherpc1:tlength - 
briventral:inslarge:weatherpc1:tlength -      sex:briventral:inslarge - 
sex:briventral:weatherpc1 - sex:briventral:tlength -      
sex:inslarge:weatherpc1 - sex:inslarge:tlength - sex:weatherpc1:tlength 
-      briventral:inslarge:weatherpc1 - briventral:inslarge:tlength 
-      briventral:weatherpc1:tlength - inslarge:weatherpc1:tlength 
+      (1 | site/pair) + (1 | year)
 
   AIC BIC logLik deviance REMLdev
 532.1 579 -246.1    492.1   523.7
Random effects:
 Groups    Name        Variance   Std.Dev.  
 pair:site (Intercept) 1.4939e+02 1.2222e+01
 site      (Intercept) 1.3566e-16 1.1647e-08
 year      (Intercept) 0.0000e+00 0.0000e+00
 Residual              7.3350e+00 2.7083e+00
Number of obs: 77, groups: pair:site, 31; site, 10; year, 2

Fixed effects:
                        Estimate Std. Error t value
(Intercept)           170.164607  42.400016  
 4.013
sexM                  -10.024653  14.856227  -0.675
briventral             -0.111976   0.298515  -0.375
inslarge               -6.754924  14.830299  -0.455
weatherpc1             -6.804090   5.373973  -1.266
tlength                -0.367131   0.450065  -0.816
sexM:briventral        -0.012079   0.063567  -0.190
sexM:inslarge          -0.992277   2.899232  -0.342
briventral:inslarge    
 0.009167   0.048433   0.189
sexM:weatherpc1        -0.374227   0.839172  -0.446
briventral:weatherpc1   0.007392   0.019052   0.388
inslarge:weatherpc1     0.092689   1.093089   0.085
sexM:tlength            0.137842   0.140530   0.981
briventral:tlength      0.001274   0.003160   0.403
inslarge:tlength        0.085786   0.155209   0.553
weatherpc1:tlength      0.044950   0.047498   0.946

Correlation of Fixed Effects:
            (Intr) sexM   brvntr inslrg wthrp1 tlngth sxM:br sxM:ns
 brvntrl:n
sexM         0.083                                                           
briventral  -0.829 -0.105                                                    
inslarge    -0.762 -0.324 
 0.382                                             
weatherpc1  -0.166  0.195  0.295 -0.075                                      
tlength     -0.983 -0.122  0.796  0.742  0.155                               
sxM:brvntrl -0.687 -0.248  0.851  0.282  0.154 
 0.702                        
sexM:inslrg -0.537 -0.399  0.114  0.838 -0.234  0.579  0.089                 
brvntrl:nsl  0.247  0.039 -0.381 -0.314 -0.036 -0.106 -0.187  0.037          
sxM:wthrpc1  0.120  0.067  0.026 -0.268  0.621 -0.099  0.032 -0.284  0.087   
brvntrl:wt1  0.063 -0.371 -0.238  0.222 -0.678 -0.032 -0.099  0.346 -0.025   
inslrg:wth1  0.150  0.131 -0.033 -0.331 -0.106 -0.084  0.070 -0.235  0.291   
sexM:tlngth  0.379 -0.862 -0.270 -0.039 -0.196 -0.358 -0.171  0.069  0.012   
brvntrl:tln  0.811  0.144 -0.970 -0.331
 -0.299 -0.819 -0.888 -0.137  0.165   
inslrg:tlng  0.695  0.360 -0.251 -0.948  0.114 -0.725 -0.216 -0.920  0.014   
wthrpc1:tln  0.188 -0.117 -0.308  0.069 -0.919 -0.206 -0.217  0.195 -0.035   
            sxM:w1 brvn:1 insl:1 sxM:tl brvntrl:t inslr:
sexM                                                   
 
briventral                                              
inslarge                                                
weatherpc1                                             
 
tlength                                                 
sxM:brvntrl                                             
sexM:inslrg                                            
 
brvntrl:nsl                                             
sxM:wthrpc1                                             
brvntrl:wt1 -0.167                                      
inslrg:wth1  0.104
 -0.140                               
sexM:tlngth  0.012  0.324 -0.103                        
brvntrl:tln -0.061  0.237 -0.069  0.252                 
inslrg:tlng  0.250 -0.277  0.263  0.002  0.267          
wthrpc1:tln -0.760  0.475 -0.132  0.154  0.348    -0.078

> sessionInfo()
R version 2.12.0 (2010-10-15)
Platform: i386-pc-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=English_United Kingdom.1252 
[2] LC_CTYPE=English_United Kingdom.1252   
[3]
 LC_MONETARY=English_United Kingdom.1252
[4] LC_NUMERIC=C                           
[5] LC_TIME=English_United Kingdom.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] lme4_0.999375-36   Matrix_0.999375-44 lattice_0.19-13    nlme_3.1-97       
[5] MASS_7.3-8        

loaded via a namespace (and not attached):
[1] grid_2.12.0   stats4_2.12.0


   So, does analising the first model by REML or ML make any difference, as long as you switch to ML in the first update?

   Thank you
 very much.

   Iker 

--- El lun, 21/3/11, Douglas Bates <bates@stat.wisc.edu> escribió:

De: Douglas Bates <bates@stat.wisc.edu>
Asunto: Re: [R-sig-ME] Switch to ML during model simplification
Para: "Iker Vaquero Alba" <karraspito@yahoo.es>
CC: r-sig-mixed-models@r-project.org
Fecha: lunes, 21 de marzo, 2011 17:55

On Sat, Mar 19, 2011 at 11:56 AM, Iker Vaquero Alba <karraspito@yahoo.es> wrote:
>
>    Thank you very much!! With the first question, I mean if you have to change the method when fitting the model or when updating  it. I mean, if you have to write:
>
>    model1<-lmer(y~a*b*c+(1|random1/random2),REML=FALSE)
>
>    Or you write the previous without "REML=FALSE" but you write that command when simplifying the model (in the first "update"). Actually, I have tried both, and writing "REML=FALSE" when fitting the model does not seem to have any effect, as when typing "summary(model1)" it always says "model fit by REML method" no matter wether you wrote "REML=FALSE" or not, and the first simplified model is analysed by "REML" or "ML" depending on what you write.

Could you provide a reproducible example of REML=FALSE failing?  Also,
please include the output of

sessionInfo()

so we know what versions of the packages you are using.

>    Hope it makes sense now. Thank you.
>
> --- El sáb, 19/3/11, Douglas Bates <bates@stat.wisc.edu> escribió:
>
> De: Douglas Bates <bates@stat.wisc.edu>
> Asunto: Re: [R-sig-ME] Switch to ML during model simplification
> Para: "Iker Vaquero Alba" <karraspito@yahoo.es>
> CC: r-sig-mixed-models@r-project.org
> Fecha: sábado, 19 de marzo, 2011 10:10
>
> On Tue, Mar 15, 2011 at 3:29 PM, Iker Vaquero Alba <karraspito@yahoo.es> wrote:
> >
> >    Hello, list members:
> >
> >    I am fitting a mixed effects model with a split-plot analysis. I have read that in order to be able to use anova to compare models with a different fixed-effects structure, it's not correct to use the default REML method, so it must be changed to ML.
> >
> >    - When using "lme", that can be done just writing: method="ML", whereas when using "lmer" you achieve that by writing "REML=false".
>
> Actually, that's REML=FALSE (case is important).
>
> >    - My questions are:
> >       1. Is it necessary to specify the ML method when fitting the m



      
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