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...e a total sample size of 360. Thus i have as a design Response: density Factors: regions (3 levels, fixed) sites (18 levels, random) I started with creating a dataframe that included density, region (coded 1, 2, 3), and site (coded 1,2...18) I then created a grouped data object with the code; datag <- groupedData(density_recruit ~ region | site, data=data, labels = list(x="Region", y="Recruit Density"), units = list(y="dm^2")) 1: my first question is this code correctly creating a grouped data object for a nested design with one nested random factor? I than...

...sed read.table( ) to read in the data and then tried to convert it to geodata type. But it failed and I got "Error in vector("double", length) : cannot allocate vector of length 1351974000" message. All the commands I used were: rawdata<-read.table(file=" ") datag<-as.geodata(rawdata) BTW, what is the biggest origin data size as.geodata( ) can convert? Thanks! Yaqing Gu [[alternative HTML version deleted]]

...A hierarchical clustering algorithm L = cut-tree(T; k): produces a partition with k non-singleton clusters s(L1;L2): a similarity between two partitions 1: f = 0:8 2: T =cluster(X) fthe reference clustering 3: for i = 1 to num subsamples do 4: subi =subsamp(X; f) fsub-sample a fraction f of the datag 5: Ti=cluster(subi) 6: end for 7: for k = 2 to kmax do 8: L1=cut-tree(T; k) fpartition the reference clusteringg 9: for i = 1 to maximum iterations do 10: L2 =cut-tree(Ti; k) 11: S(i; k) = s(L2;L1) computed only on the patterns of subi. 12: end for 13: end for I am glad about any help, don't...

...omp package appropriate for using with a lme built under the nlme package. I know i can get it to work (i.e report p-values for a tukey test) but i am not unsure if this is appropriate for a linear mixed effects model. Below is the code i used lmm1 <- lme(asinh(density_recruit) ~ region, data=datag, random=list(site=~1)) mc<-glht(lmm1, linfct = mcp(region = "Tukey")) Simultaneous Tests for General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Fit: lme.formula(fixed = asinh(density_recruit) ~ region, data = datag, random = list(site = ~1)) Linea...

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