anyone who can help me with this?
I have one fixed (PESTICIDES) factor nested in (5) block factor and one
random factor (SUBSTRATES) nested in the fixed factor. I think it“s the
correct model. (Four pesticides (including control) were randomly
assign into the five blocks and then a soil sample were taken and treated
with 12 substrates to evaluate respiration rate).
Firs, I fit my model with lme() function like this
> lme09<-lme (RESPIRACION~PESTICIDA, random=~1| BLOQUE/PESTICIDA, weights =
varIdent(form = ~1|SUSTRATO), data=RESP09, na.action=na.omit,
control=lmeControl(maxIter=200, msMaxIter=200, niterEM=100))
> summary(lme09)
Linear mixed-effects model fit by REML
 Data: RESP09
       AIC      BIC    logLik
  111.7775 166.4314 -38.88874

Random effects:
 Formula: ~1 | BLOQUE
         (Intercept)
StdDev: 3.086521e-08

 Formula: ~1 | PESTICIDA %in% BLOQUE
        (Intercept)     Residual
StdDev:   0.1366234 7.911512e-07

Variance function:
 Structure: Different standard deviations per stratum
 Formula: ~1 | SUSTRATO
 Parameter estimates:
AC CITRICO    AC GLUT  AC MALICO    AC OXAL     ASPARR    FENILAL
 GLUCOSA  HISTIDINA
       1.0   704328.3   675541.5   186809.3   380757.2   410564.8
370571.6   441234.7
    MANOSA     LISINA   ARGININA
  338306.9   257493.9   330352.4
Fixed effects: RESPIRACION ~ PESTICIDA
                     Value  Std.Error  DF   t-value p-value
(Intercept)     0.13914000 0.06109982 168  2.277257  0.0240
PESTICIDACIP    0.02539722 0.08762818  12  0.289829  0.7769
PESTICIDAGLIF  -0.14447975 0.09004731  12 -1.604487  0.1346
PESTICIDASEC 1 -0.29229772 0.08990939  12 -3.251026  0.0069
 Correlation:
               (Intr) PESTICIDAC PESTICIDAG
PESTICIDACIP   -0.697
PESTICIDAGLIF  -0.679  0.473
PESTICIDASEC 1 -0.680  0.474      0.461

Standardized Within-Group Residuals:
          Min            Q1           Med            Q3           Max
-1.983260e+00 -5.103333e-01  1.970778e-06  7.050516e-01  2.783527e+00

Number of Observations: 188
Number of Groups:
               BLOQUE PESTICIDA %in% BLOQUE
                    5                    20

When I tried to make a barchart with the mean and standard errors of the
fixed effects (I used the model without the intercept) I realized that
these ES are not the correct ones because I had a non-significant p-value
and the ES didn“t reflect that. Then I tried to get the confidence
intervals of the fixed effects mean and I got these error message:

> intervals (lme09)

Error in intervals.lme(lme09) :

  cannot get confidence intervals on var-cov components: Non-positive
definite approximate variance-covariance

I googled what this error means and I found that these message could be an
advise that the model may be wrong. At this point, I don“t know how to fit
my model correctly.

Then I tried to fit the model with lmer() function but I don“t know if the
syntax is the correct one and when I run the lmer model i get a warning
message

> lmer093<-lmer (RESPIRACION~PESTICIDA+(0+SUSTRATO|BLOQUE/PESTICIDA),
data=RESP09, na.action=na.omit)

Warning message:

In mer_finalize(ans) : iteration limit reached without convergence (9)

I think this is the same message as before with the lme model and so, my
model is wrong.

So, I couldn“t get the confidence intervals to make a barchart and now I “m
in doubt with the model.

I“m doing something wrong. I tried other models but the degrees of freedoms
were pseudoreplicated.

Thanks

-- 
Lic. Analķa V. Giménez
IFEVA-CONICET
Facultad de Agronomķa.
Av. San Martķn 4453 C1417DSE
Bs. As. Argentina

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