Dear all,
I have a question about the aovlmer.fnc {languageR}.
How can one get the p-val for the factors when the lmer model have been
simplified?
I'm analyzing some data, where I have four fixed factors:

   - FA -> (FA_1,FA_2,FA_3)
   - FB -> continuos predictor
   - FC -> (FC_1, FC_2)
   - FD- > (FD_1, FD_2)

The original model looks like this
m0 = lmer(DV~FA*FB*FC*FE + (1|RF),data=data1)
After fitting the lmer model  I simplified it resulting in.
m1 = lmer(DV ~ FA + FB + FC + FD + FB:FC + FB:FD + FC:FD + FB:FC:FC + (1 |
RF),data=data1)

I got the p-values of the parameters by using the pvals.fnc function but I
want also to obtain the p-values of the factors.
In theory this is possible by using the aovlmer.fnc and the mcmc output of
the pvals.fnc. However, the mcmc table does not contain all
the necessary levels to compute the p-val of the factors with the aovlmer.fnc
(or i did not understand how to use it).

My mcmc table looks like this
intercept | FA_2 | FA_3 | FB | FC_1 | FD_1 | FB:FC_1 | FB:FD_1 | FC_1:FD_1
| FB:FC_1:FD_1 |

following the examples, if i wanna to obtain the p-val of factor FA I have
to do
aovlmer.fnc(m1, mcmc$mcmc, c("FA_2","FA_3"))
and this works.
But how can I obtain the p-values for other factors and interactions?
If i try
aovlmer.fnc(m1, mcmc$mcmc, c("FB"))
I receive the message "x must be an array of at least two dimensions".

I was tempted to use the pamer.fnc{LMERConvenienceFunctions} which works.
But due the few data that I have I need more conservative p-values (pMCMC).
I would appreciate if you have any advice to deal with this problem.
Thanks in advance,
Jaime


-- 
Jaime Undurraga, Eng
PhD Student
ExpORL, Dept. Neurosciences
Katholieke Universiteit Leuven
Herestraat 49, bus 721
3000 Leuven, Belgium
Tel:  +32 16 330485
Fax: +32 16 330486
_____________________________________________________
http://jaime.undurraga.googlepages.com

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