Hi!

I try to explain the efffect of (1)  forest where i took samples's soils (*
Lugar*: categorical variable with three levels), (2) nitrogen addition
treatments (*Tra*: categorical variable with two levels) on total carbon
concentration's soil samples (*C: *continue* *variable) during four months
of sampling (*Time:* categorical and ordered variable with four levels).

I fitted the following final model with gls function:

var1<-varIdent(form=~ 1| Lugar* factor(Time))
FINAL<-gls(C ~  Tra+ Lugar+ Time + Time*Tra + Tra*Lugar, data=datos,
weights=var1, method="REML")

the summary function resulted in this first data's set (I omit correlation's
matrix):

Generalized least squares fit by REML
  Model: C ~ Tra + Lugar + Time + Time * Tra + Tra * Lugar
  Data: datos
       AIC      BIC    logLik
  1129.458 1191.982 -540.7291

Variance function:
 Structure: Different standard deviations per stratum
 Formula: ~1 | Lugar * factor(Time)
 Parameter estimates:
Chixchulub*0   Xmatkuil*0    Hobonil*0 Chixchulub*2   Xmatkuil*2
 Hobonil*2 Chixchulub*3
   1.0000000    0.7759324    0.5300811    0.9640559    0.8200742
 0.2966545    0.9553168
  Xmatkuil*3    Hobonil*3 Chixchulub*4   Xmatkuil*4    Hobonil*4
   1.7350290    0.3430286    0.6241658    0.9573922    0.4651515

Coefficients:
                                     Value             Std.Error    t-value
        p-value
(Intercept)                 260.48540  16.48991  15.796653  0.0000
Tra0                          -9.38703      23.74893  -0.395261  0.6935
LugarChixchulub       -0.15377  19.60260  -0.007845  0.9938
Lugar Hobonil        -173.21354  15.89736 -10.895741  0.0000
Time2                    -14.74999  14.55909  -1.013112  0.3135
Time3                     14.42177  15.64594   0.921758  0.3589
Time4                     14.77803  16.72367   0.883659  0.3790
Tra0:Time2             17.93859  20.78257   0.863156  0.3901
Tra0:Time3            -48.77118  22.17628  -2.199250  0.0302
Tra0:Time4            -52.63611  23.20192  -2.268610  0.0254
Tra0:LugarChixchulub   74.43956  28.11275   2.647893  0.0094
Tra0:Lugar Hobonil     43.03416  23.32391   1.845066  0.0680

anova function generated this table:

 enom. DF: 100
            numDF   F-value p-value
(Intercept)     1 1693.1234  <.0001
Tra             1    5.3225  0.0231
Lugar           2  247.7047  <.0001
 Time            3    0.4767  0.6992
Tra:Time        3    6.0531  0.0008
Tra:Lugar       2    3.5061  0.0338

I want to detetect differences between levels of Tra:Lugar interaction. For
example:

1. Tra0:LugarChixchulub vs Tra1:LugarChixchulub  (between treatment levels
for same forest) or,
2. Tra0:LugarChixchulub vs Tra0:LugarHobonil        (for same treatment
among forests levels)

I used function contrast (package contrast) whit following script to probe
the hypotesis 1.:

con<-contrast(FINAL, list(Lugar= 'Xmatkuil', Tra=1), list(Lugar='Xmatkuil',
Tra = 0))

but i found this error message:

Error en gendata.default(fit = list(modelStruct = list(varStruct =
c(-0.253689933940456,  :
  not enough factors

I would be grateful if somebody tell me I'm doing wrong with my contrast
function script.

Thanks in advance,


Marylin Bejarano
PHd candidate in Ecology Institute of Mexico's National Autonomous
University

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