Dear Steven, 
I am so happy, that you answered me! I tried what you said to put all the variable in one dataframe. The Pretreatment is not really necessary, because it didn't show any significance. I didn't copy it into the help, because I tried to concentrate on the essential things.

Here is the whole code:

PAMdata <-read.table("PAMdata.csv",sep = ";",header=TRUE)  #warning: should have no extra anything in your column names

##inspect##
head(PAMdata)
summary(PAMdata)
str(PAMdata) 
##end inspect##


install.packages(c("sciplot","nlme","multcomp"))
library(mvtnorm)
library(splines)
library(survival)
library(sciplot)
library(nlme)
library(multcomp)

#Factors
PAMdata$provenance[PAMdata$provenance == "1"] = "BG" 
PAMdata$provenance[PAMdata$provenance == "2"] = "DE" 
PAMdata$provenance[PAMdata$provenance == "3"] = "IT" 
PAMdata$provenance[PAMdata$provenance == "4"] = "SE" 
PAMdata$provenance[PAMdata$provenance == "5"] = "ES" 
PAMdata$provenance[PAMdata$provenance == "6"] = "HU"

PAMdata$Treatmentf <- factor(PAMdata$treatment, levels=c("C","F"))
PAMdata$Datef <- factor(PAMdata$Date, levels=c( "25.05.10 14:00","26.05.10 19:00","27.05.2010 7:30","27.05.10 14:00","01.06.10 14:00","02.06.10 19:00","23.06.10 12:30"),ordered=TRUE)

PAMdata$Pretreatmentf <- as.factor(PAMdata$pretreatment)
PAMdata$Provenancef <- as.factor(PAMdata$provenance)
PAMdata$Greenhousef <- as.factor(PAMdata$greenhouse)
PAMdata$Individualf <- as.factor(PAMdata$individual)

PAMdata$PAMval <- (PAMdata$DataPAM)
PAMdata$Code<-(PAMdata$code)

head(PAMdata)
#######  Statistischer Test mit Anova  #########

summary(PAMaov<-aov(PAMval~Treatmentf*Pretreatmentf*Provenancef+Error(Datef/Code),data = PAMdata))

#############################################################
##Linear fixed effects model lme

summary(PAM.lme<-lme(PAMval~Treatmentf*Provenancef*Pretreatmentf, random=~1|Datef/Code, data = PAMdata, na.action=na.omit))

### Tukey test ## 
summary(glht(PAM.lme, linfct = mcp(Provenancef = "Tukey")))

Error message:
Fehler in glht.matrix(model = list(modelStruct = list(reStruct = list(Code = 0.808654423456211,  : 
  ‘ncol(linfct)’ is not equal to ‘length(coef(model))’
Zusätzlich: Warnmeldung:
In mcp2matrix(model, linfct = linfct) :
  covariate interactions found -- default contrast might be inappropriate

summary(glht(PAM.lme, linfct = mcp(Treatmentf = "Tukey")))

--> gives the same error

traceback()
--> The whole traceback thing is huge, do you really wanna have it? Here are the last lines:
4: do.call("glht", args)
3: glht.mcp(PAM.lme, linfct = mcp(Provenancef = "Tukey"))
2: glht(PAM.lme, linfct = mcp(Provenancef = "Tukey"))
1: summary(glht(PAM.lme, linfct = mcp(Provenancef = "Tukey")))

with(PAMdata, table(Pretreatmentf, Provenancef, Treatmentf))

--> gives:
, , Treatmentf = C

             Provenancef
Pretreatmentf BG DE ES HU IT SE
               0  0  0  0  0  0
            C 63 63 42 63 63 63
            W 63 63 63 63 63 63

, , Treatmentf = F

             Provenancef
Pretreatmentf BG DE ES HU IT SE
               0  0  0  0  0  0
            C 63 63 63 63 63 63
            W 63 63 63 63 63 63

sessionInfo()
> sessionInfo()
R version 2.12.0 (2010-10-15)
Platform: i386-apple-darwin9.8.0/i386 (32-bit)

locale:
[1] de_DE.UTF-8/de_DE.UTF-8/C/C/de_DE.UTF-8/de_DE.UTF-8

attached base packages:
[1] splines   stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] multcomp_1.2-4  nlme_3.1-97     sciplot_1.0-7   survival_2.35-8 mvtnorm_0.9-92 

loaded via a namespace (and not attached):
[1] grid_2.12.0     lattice_0.19-13


I would like very much to share the data, I am just not exactly sure how to make it?

Thank you so much for your answer and your help! Now I don't feel so lonely with my problem anymore!
Best wishes,
Lilith


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