Hello mixed-modelers:

 

I am confused about detection of differences among fixed effect estimates in
mixed models. I am using lme() to compare the effect of the factor Exam (3
levels: P0,P1,P5) on the continuous variable BA. Measurements were done on
64 Plots. This is modeled with the statement m0 <- BA~Exam, data.,
random=~1|plot.  Summary(m0) tells me that Exam has a highly significant
effect, and that both P1 and P5 differ highly significantly from P0, but not
whether P1 and P5 differ. If I use a contrast statement ("library(contrast),
contrast(m0, list(Exam='P1'), list(Exam='P5'), the contrast is not
significant (p=0.16).  However, if I just use a paired t-test, the
difference is significant at p=0.001. I also get significant results with
lme if I just delete the 'P0' level. I would be grateful if anyone could
point me in the right direction for making a robust (yet sensitive)
determination of whether these factor levels differ. I possess a copy of the
Ur-text for R mixed models (i.e., Pinheiro & Bates) but it has not yet
provided insight. Appreciatively, Seth W. Bigelow

 

 

Linear mixed-effects model fit by REML

 Data: s2 

       AIC      BIC    logLik

  1746.913 1763.122 -868.4565

 

Random effects:

 Formula: ~1 | Plot

        (Intercept) Residual

StdDev:      25.779 16.18827

 

Fixed effects: BA ~ Exam 

                Value                    Std.Error  DF   t-value p-value

(Intercept)  72.01422  3.805048 126 18.925968   0e+00

ExamP1      -15.01266  2.861708 126 -5.246047   0e+00

ExamP5      -10.99922  2.861708 126 -3.843585   2e-04

 Correlation: 

       (Intr) ExamP1

ExamP1 -0.376       

ExamP5 -0.376  0.500

 

Standardized Within-Group Residuals:

        Min          Q1         Med          Q3         Max 

-1.74478652 -0.54972545  0.02464379  0.41020121  3.24236603 

 

Number of Observations: 192

Number of Groups: 64 

 

CONTRAST statement

lme model parameter contrast

 

   Contrast     S.E.     Lower    Upper    t  df Pr(>|t|)

1 -4.013438 2.861708 -9.622282 1.595407 -1.4 187   0.1624


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