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
[[alternative HTML version deleted]]