Hello,
I previously wrote to R-help, I got some advices and was kindly redirected by Kingsford to this specific Help-list (mixed models) for further questions. Since then I have moved a bit but I still have some points where I would appreciate having clarification.

I have a factorial experiment ( with 4 repetitions for each treatment combination) to study the effects of Grazing and Fire on Forest biomass production. The experimental unit (to which the treatment combinations are applied) are PLOTs. The measures were made repeatedly for 13 years. Below is how I organized my data; Plot is the plot naming in the field; BA.B is the response variable (Basal area) I am using to express myself here


Grazing             Fire                     Plot                     Year                    BA.B
Ungrazed         Unburnt            102                      1                           398.13
Ungrazed         Unburnt            102                      2                           4728.54
Ungrazed         Unburnt            102                      3                           2092.05
Ungrazed         Unburnt            102                      4                           3076.70
Ungrazed         Unburnt            102                      5                           2578.54
Ungrazed         Unburnt            102                      6                           2541.07
Ungrazed         Unburnt            102                      7                           3191.61
Ungrazed         Unburnt            102                      8                           2526.75
Ungrazed         Unburnt            102                      9                           3665.42
Ungrazed         Unburnt            102                      10                        3077.42
Ungrazed         Unburnt            102                      11                        3911.63
Ungrazed         Unburnt            102                      12                        4067.28
Ungrazed         Unburnt            102                      13                        4457.94
Ungrazed         Unburnt            108                      1                           370.99
Ungrazed         Unburnt            108                      2                           2184.39
Ungrazed         Unburnt            108                      3                           2008.66
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.
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I fitted the below model to account for the temporal autocorrelation and the variance heterogeneity. I also have a missing value.

Model<-lme(BA.B~Grazing*Fire*Year, random=~1|Year/Plot/Fire/Grazing, correlation=corAR1(form=~Year), weights=varIdent(form=~1|Grazing*Fire*Year), na.action=na.omit)

For the random effect I got something like this

Random effects:
 Formula: ~1 | Year
        (Intercept)
StdDev:    234.6285

 Formula: ~1 | Plot %in% Year
        (Intercept)
StdDev:    67.01272

 Formula: ~1 | Fire %in% Plot %in% Year
        (Intercept)
StdDev:    66.80442

 Formula: ~1 | Grazing %in% Fire %in% Plot %in% Year
        (Intercept) Residual
StdDev:    66.83408           153.0221


For the correlation structure, the Phi value is 0 (zero)

Correlation Structure: AR(1)
 Formula: ~Year | Year/Plot/Fire/Grazing
 Parameter estimate(s):
Phi
  0

For the fixed effects, please note the identical degree of freedom for every term (except Year)

Fixed effects: BA.B ~ Grazing * Fire * Year
                                     Value                   Std.Error       DF                 t-value           p-value
(Intercept)                       461.4655          178.15612     396                2.590231       0.0099
GrazingUngrazed                  -180.1041   104.88596     396                -1.717143      0.0867
FireUnburnt                      -236.3691       124.54654     396                -1.897837      0.0584
Year                              410.9426             31.68234       11                  12.970718     0.0000
GrazingUngrazed:FireUnburnt 224.5099 153.17048    396                1.465752       0.1435
GrazingUngrazed:Year         -104.4984  28.99989       396                -3.603406      0.0004
FireUnburnt:Year                  -21.3375     38.52046       396                -0.553927      0.5799
GrazingUngrazed:FireUnburnt:Year 85.2475  44.47663   396           1.916680       0.0560


My concern is:
Does my model specification looks ok?
Is Phi = 0 real and what does this mean (in all case studies I have seen, it had value different from zero)?
I am also wondering about the identical degree of freedom for all term (except for Year).
Thanks in advance


With regards,

Djibril.




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