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.
Formula: LnRT ~ 1 + DerF + bg + (1 | Subj) + (1 | Item)
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 0.178694number of obs: 1880, groups: Item, 120; Subj, 37
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
Analysis of Variance Table
Df Sum Sq Mean Sq
DerF 1 1.81871 1.81871
bg 1 0.05157 0.05157
Centre for Speech and Language
Department of Experimental Psychology
Tel: +44 (0) 1223 766559
Fax: +44 (0) 1223 766452
[[alternative HTML version deleted]]
Archive maintained by Robert King, hosted by
the discipline of
statistics at the
University of Newcastle,
Archive generated by hypermail 2.1.8, at Mon 09 Oct 2006 - 12:30:11 GMT.
Mailing list information is available at https://stat.ethz.ch/mailman/listinfo/r-help. Please read the posting guide before posting to the list.