Hi,

 

I am having trouble using the lme function to perform a nested ANOVA
with a random nested factor.

 

My design is as follows:

 

Location (n=6) (Random)

Site nested within each Location (n=12) (2 Sites nested within each
Location) (Random)

Dependent variable: sp (species abundance) 

 

 

By using the aov function I can generate a nested ANOVA, however this
assumes that my nested factor is fixed.

 

> summary(aov(sp~Location/Site, data=mavric))

                  

Df Sum Sq Mean Sq F value Pr(>F)

Location           4 112366   28092  1.2742 0.2962

Location:Transect  5 121690   24338  1.1039 0.3736

Residuals         40 881875   22047               

 

 

I have tried the following lme function to specify that Site is random:

 

> lme1 <- lme(sp~Location, random=~1|Site, data=mavric)

> lme2 <- lme(sp~Location, random=~1|Location/Site, data=mavric)

 

> anova(lme1)

            numDF denDF  F-value p-value

(Intercept)     1    40 3.418077  0.0719

Location        4     5 1.152505  0.4294

 

This gives me the correct F-value for Location from
MSLocation/MSLocation:Transect, but the p-value doesn't seem to be
correct (by my calculations in Microsoft Excel it should be 0.345)

 

> anova(lme2)

Warning in pf(q, df1, df2, lower.tail, log.p) : 

         NaNs produced

Warning: NAs introduced by coercion

            numDF denDF  F-value p-value

(Intercept)     1    40 0.459966  0.5015

Location        4     0 0.155091     NaN

 

? I don't know what this output means

 

> anova(lme1,lme2)

     Model df      AIC      BIC    logLik   Test      L.Ratio p-value

lme1     1  7 603.7534 616.4000 -294.8767                            

lme2     2  8 605.7534 620.2067 -294.8767 1 vs 2 1.815674e-05  0.9966  

 

? I also don't know what this output means.

 

Can anyone tell me if there is a way to use the lme() function in order
to obtain the same output as the aov() function (above), but so it
correctly calculates the MS, F and p values for my main Location factor?

 

Thanks,

 

Prue 
  
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