[R-sig-eco] Question regarding a associating a p-value with the residual variance form a lme model

[Mike Dunbar]

Dear Charles

It's very difficult to tell without more information on what these papers are and what exactly they are saying.

Also with your correlation structure, what are you trying to model? corCAR is a continuous AR structure, allowing fractional time points, but you don't have a time variable specified, so lme will be using the within-group position of the observation in the dataset, which can only be a integer. 

regards

Mike


>>> Charles Nock <charles.nock at gmail.com> 11/11/2008 11:47 >>>
I have a lme model of radial variation in wood density which includes  
the fixed effects of the year of the tree ring, annual increment  
(width of the ring), a year*increment interaction, and random effects  
for differences in the slope and intercept of the change in wood  
density with year from tree to tree (see below).

I understand that p-values can be calculated for the individual random  
effects by a likelihood ratio test of models differing by the term of  
interest.

In some papers I have seen a p-value calculated for the residual,  
which I do not understand how to obtain, or what it is telling you.

thanks,
Charles
_____________________________

Charles Nock, M.Sc.F
Doctoral candidate
Institute of Botany
University of Natural Resources and
Applied Life Sciences Vienna



agexincrement.lme.corr <- lme(densmean ~  
logyearcorr*increment_distrange_cm , random=~ logyearcorr | Tree,  
data=memasterfinalage, method="ML", correlation=corCAR1(form=~1| Tree))

 > summary(agexincrement.lme.corr )

Linear mixed-effects model fit by maximum likelihood
  Data: memasterfinalage
        AIC      BIC    logLik
   3122.368 3154.984 -1552.184

Random effects:
  Formula: ~logyearcorr | Tree
  Structure: General positive-definite, Log-Cholesky parametrization
             StdDev    Corr
(Intercept) 138.56419 (Intr)
logyearcorr 115.58319 -0.833
Residual     64.10158

Correlation Structure: Continuous AR(1)
  Formula: ~1 | Tree
  Parameter estimate(s):
       Phi
0.3360245
Fixed effects: densmean ~ logyearcorr * increment_distrange_cm
                                        Value Std.Error  DF   t-value  
p-value
(Intercept)                         450.5252  60.91811 263  7.395587    
0e+00
logyearcorr                         177.8543  50.64480 263  3.511799    
5e-04
increment_distrange_cm             -124.2732  34.50346 263 -3.601761    
4e-04
logyearcorr:increment_distrange_cm  116.2502  33.51491 263  3.468610    
6e-04
  Correlation:
                                    (Intr) lgyrcr incr__
logyearcorr                        -0.901
increment_distrange_cm             -0.498  0.495
logyearcorr:increment_distrange_cm  0.397 -0.443 -0.942

Standardized Within-Group Residuals:
           Min            Q1           Med            Q3           Max
-2.0842970533 -0.5809674446  0.0003970132  0.6389420030  2.7177642650

Number of Observations: 277
Number of Groups: 11















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