R help - Apr 2010 - Degrees of Freedom Not Allocated to Residuals in Reduced Model

##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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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


##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