Hi, 
I'm analysing a dataset in which the same 5 subjects (male.pair) were subjected to two treatments (treatment) and were measured for 12 successive days within each treatment (layingday). Overall 5*2*12=120 observations. 

I want to test the effect of treatment, time (layingday) and their interaction. I have done so through the ANOVA below:

> bmc3<-aov(Mean1~treatment*layingday+Error(male.pair/treatment/layingday))
> summary(bmc3)

Error: male.pair
          Df  Sum Sq Mean Sq F value Pr(>F)
Residuals  1 0.13850 0.13850               

Error: male.pair:treatment
          Df  Sum Sq Mean Sq
treatment  1 0.60525 0.60525

Error: male.pair:treatment:layingday
          Df  Sum Sq Mean Sq
layingday  1 0.64037 0.64037

Error: Within
                     Df  Sum Sq Mean Sq F value    Pr(>F)    
treatment             1 0.02015 0.02015  0.7340    0.3934    
layingday             1 0.52937 0.52937 19.2878 2.545e-05 ***
treatment:layingday   1 0.02959 0.02959  1.0782    0.3013    
Residuals           113 3.10135 0.02745                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

I then wanted to compare this outcome with an lme, and used the model below. However, its outcome doesn't make much sense to me. 

> bmc4<- lme(Mean1 ~ treatment*layingday, random = ~1|male.pair)
> summary(bmc4)
Linear mixed-effects model fit by REML
 Data: NULL 
        AIC       BIC   logLik
  -118.4522 -101.9306 65.22609

Random effects:
 Formula: ~1 | male.pair
        (Intercept)  Residual
StdDev:   0.1313573 0.1185902

Fixed effects: Mean1 ~ treatment * layingday 
                         Value  Std.Error  DF   t-value p-value
(Intercept)          0.5311005 0.09369140 112  5.668615  0.0000
treatment            0.0495373 0.04616116 112  1.073138  0.2855
layingday           -0.0488055 0.00991701 112 -4.921389  0.0000
treatment:layingday  0.0138449 0.00627207 112  2.207388  0.0293
 Correlation: 
                    (Intr) trtmnt lyngdy
treatment           -0.739              
layingday           -0.688  0.838       
treatment:layingday  0.653 -0.883 -0.949

Standardized Within-Group Residuals:
        Min          Q1         Med          Q3         Max 
-2.44529424 -0.68505388  0.01663401  0.59009515  3.53354000 

Number of Observations: 120
Number of Groups: 5 

I struggle to understand the discrepancy in df between the anova and lme, and the fact that the interaction term is not significant in the anova but significant in lme. Any help would be greatly appreciated. 
Best
Tom

-- 
Dr. Tommaso Pizzari
Edward Grey Institute, Dept of Zoology, 
University of Oxford, Oxford OX1 3PS
Tel: (44) 1865 271279, Fax: (44) 1865 271168

