

##I am trying to test for fixed factor main effects in an unbalanced mixed effects model but when I fit the reduced model for "mic" factor effects, the extra degrees of freedom are being allocated to a nested term rather than the residuals.  The model has inc, mic and spp are independent variables and vial nested within spp.  inc and spp are already coded as factors since they were entered as text in the dataframe "t".


##Full Model
> fit<-lm(cmm~inc+factor(mic)+spp/factor(vial),t)
> anova(fit)
Analysis of Variance Table

Response: cmm
                   Df Sum Sq Mean Sq  F value    Pr(>F)    
inc                 1 2099.3  2099.3 387.6854 < 2.2e-16 ***
factor(mic)         2  502.3   251.2  46.3823 < 2.2e-16 ***
spp                 2 7947.1  3973.5 733.8008 < 2.2e-16 ***
spp:factor(vial)   94 3746.9    39.9   7.3612 < 2.2e-16 ***
Residuals        1112 6021.5     5.4                       

## Reduced Model
> fitredmic<-lm(cmm~inc+spp/factor(vial),t)
> anova(fitredmic)
Analysis of Variance Table

Response: cmm
                   Df Sum Sq Mean Sq  F value    Pr(>F)    
inc                 1 2099.3  2099.3 387.6854 < 2.2e-16 ***
spp                 2 8149.7  4074.9 752.5124 < 2.2e-16 ***
spp:factor(vial)   96 4046.6    42.2   7.7843 < 2.2e-16 ***
Residuals        1112 6021.5     5.4                       
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 


##So the degrees of freedom for the comparison are 0: 

> anova(fit,fitredmic)
Analysis of Variance Table

Model 1: cmm ~ inc + factor(mic) + spp/factor(vial)
Model 2: cmm ~ inc + spp/factor(vial)
  Res.Df    RSS Df   Sum of Sq F Pr(>F)
1   1112 6021.5                        
2   1112 6021.5  0 -9.0949e-13    

Is this meaningful?  How can I fix this?  Thank you!

Wes

 

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