Thank you for the pointer to the FAQ. Thought I had searched the FAQ 
thoroughly, obviously I didn't.

Unfortunately, my stats aren't up to fully understanding the explanation 
and the proposed solution in the FAQ.

>For the time being, I would recommend using a Markov Chain Monte Carlo
>sample (function mcmcsamp) to evaluate the properties of individual
>coefficients (use HPDinterval or just summary from the "coda"
>package).  Evaluating entire terms is more difficult but you can
>always calculate the F ratio and put a lower bound on the denominator
>degrees of freedom.

Does anyone have the time to explain how I can do the above to get 
reportable degrees of freedom for the fixed effects for the analysis below.

Thank you.

- Mike

 > newtwods7.lmer
Formula: LnRT ~ 1 + DerF + bg + (1 | Subj) + (1 | Item)
    Data: newtwods2
     AIC    BIC logLik MLdeviance REMLdeviance
  -852.1 -824.4    431     -883.6         -862
Random effects:
  Groups   Name        Variance  Std.Dev.
  Item     (Intercept) 0.0036683 0.060567
  Subj     (Intercept) 0.0264120 0.162518
  Residual             0.0319315 0.178694
number of obs: 1880, groups: Item, 120; Subj, 37

Fixed effects:
              Estimate Std. Error t value
(Intercept)  6.328827   0.027611  229.21
DerF        -0.053572   0.007028   -7.62
bg           0.008921   0.007020    1.27

Correlation of Fixed Effects:
      (Intr) DerF
DerF -0.011
bg    0.001 -0.078

 > anova(newtwods2.lmer7)
Analysis of Variance Table
      Df  Sum Sq Mean Sq
DerF  1 1.81871 1.81871
bg    1 0.05157 0.05157

Mike Ford
Centre for Speech and Language
Department of Experimental Psychology
Downing Street
Cambridge
CB2 3EB

Tel: +44 (0) 1223 766559
Fax: +44 (0) 1223 766452

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