similar to: Anova and tukey-grouping

Hi, I am trying to combine a long list but I can't work out how to do it, for example: abun<-list(rep(0,5),rep(0,7),rep(0,4),rep(0,10)) nb<-c(5,5,1,8) fill.abun <- function(x, y) { set <- sample(1:length(x), size = y) x[set] <- rlnorm(length(set)) return(x) } abun <- mapply(fill.abun, abun, nb) abun ## I want all the data in one

Hi All, I am writing a simulation that examines the effects of species extinctions on ecological communties by sequentially removing individuals of a given species (sometimes using weighted probabilities) and replacing the lost individuals with species identities randomly sampled from the remaining individuals. Thus I use two dataframes. One contains all the individuals and their species

Hello, Im working on a 50933 point count bird abundance dataset. I've succeeded in calculating a distance matrix for this entire set, but I don't have sufficient memory to convert this to a matrix, as below... abun.dist <- dist(abun.mat[1:50993,1:235) test <- rowMeans(as.matrix(abun.dist)) Error in matrix(0, size, size) : too many elements specified ive been able to run a hclust()

Greetings, I am new to R and am hoping to get some tips from experienced R-programmers. I have a dataset that I've read into R as a dataframe. There are 5 columns: Plot location,species name, a species number code (unique to each species name), abundance, and treatment. There are 272 plots in each treatment, but only the plots in which the species was recorded have an abundance value. For

I have a model that looks like this (with various other methods) class HsdPair attr_accessor :hsd def initialize(hsd) @hsd = hsd @current_display = 0 @has_changed_flag = false end end After initializing this object in the controller @hsd_pairs = HsdPair.new(@job.hsd_pairs) using it in the view various times and in various ways, when I try to pass this object back to the

Dear list members, I'm running some tests looking at differences between means for various levels of a factor, using Tukey's HSD method. I would like to plot the data as boxplots or dotplots, with horizontal significance lines indicating which groups are statistically significantly different, according to Tukey HSD. Here's a nice image showing an example of such a graphical

Dear all, I would be glad if someone could tell me if Tukey HSD (function of stats library) accept the NA values and could correct the unbalanced design (different number of sample in each group) Thanks for help Alice Le Bars

Hi, I'm designing a experiment in order to compare the growing of several clones of a tree specie. It will be a complete randomized block design. How can I decide what model of mean comparision to choose? LSD, HSD,TukeyHSD, Duncan,...? Thanks in advance

Dear members of the list, I'm fitting ordinal regressions using polr, and in some models I get the error copied below. Dependent variable is an ordered factor of bird abundance categories, and predictors are continuous habitat variables. > ro6 <- polr(formula = abun ~ InOmbrot + Oliva.OC + ToCultAr + DivCulArb + AltitMax + COORXY) > summary(ro6) Re-fitting to get Hessian

I am a new R user but a long time SAS user. I searched for a response to this question but no luck, so forgive me if this topic has been covered before. I am running a TukeyHSD post hoc test after running an ANOVA. I get the results of all pairwise comparisons, no problem. However, the output table is a little "busy", and I'd like to make the output easier to read. Specifically, I

Hi, I need to determinate HSD value from a matrix like that Thesis Days A1 Cx 0 66.07 Cx 0 60.24 Cx 0 42.86 Tw 0 66.07 Tw 0 60.24 Tw 0 42.86 Aa 0 66.07 Aa 0 60.24 Aa 0 42.86 Qe 0 66.07 Qe 0 60.24 Qe 0 42.86 Cx 56 310.86 Cx 56 223.17 Cx 56 186.77 Tw 56 149.42 Tw 56 127.75 Tw 56 138.59 Aa 56 130.24 Aa 56 214.83 Aa 56 137.95 Qe 56 186.64 Qe 56 189.09 Qe 56 187.87 this is my script

Hi R-people, I am wanting to run Factorial ANOVA followed by Scheffe tests on some spatial subjective data. I'm comparing X-Y independent coordinates against x-y dependent coordinates. There are only four independent spatial coordinates that form a square. I am wondering whether I am doing the right thing, because there doesn't seem to be a simple way of doing this. I have attempted to

Hi, I did the analysis of variance of a split-plot and the effect of treatment was significant. I would like compare treatment means using Tukey. I can't extract the mean square to apply HSD.test to use in agricolae package. anava = aov(ganhos ~ Blocos + Trat*Supl + Error(Blocos/Trat)) names(anava) summary(anava) require(agricolae) HSD.test(ganhos, Trat, df, MSerror, alpha = 0.05)

I have insect data from twelve sites and like most environmental data it is non-normal mostly. I would like to preform an anova and a means seperation like tukey's HSD in a nonparametric sense (on some sort of central tendency measure - median?). I am searching around at this time on the internet. Any suggestions, books, etc. would be greatly appreciated. -- Stephen Sefick Research

I am trying to do Tukey HSD comparisons on a repeated measures expt. I found the following example on r-help and quoted approvingly elsewhere. It is broken. Can anyone please tell me how to get it to work? I am using R 2.4.1. > require(MASS) ## for oats data set > require(nlme) ## for lme() > require(multcomp) ## for multiple comparison stuff > Aov.mod <- aov(Y ~ N + V +

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 Hi. I have been using SAS for some time, and now I have discovered R. I am very happy with it, but I have not found out how to perform some of the multiple comparisons I was used to do in SAS. With the SAS/STAT, I generally used the MEANS (for comparison of arithmetic means) and the LSMEANS (for adjusted means) statements of the GLM procedure (I

Hi everyone I'm using Tukey HSD as post-hoc test following a lme analysis. I'm measuring hemicelluloses in different species treated with three different CO2 concentrations (l=low, m=medium, h=high). The whole experiment is a split-plot design and the Tukey-function from the package multcomp is suitable for lme-analysis with random factors. The analysis works fine but I get a non

Hi, A client has a consumption measure on each of four products. The sample size is 75. The consumption distributions are highly skewed for each product. He would like a pairwise comparison test of the products, much like Tukey's HSD but using medians rather than means. Is there such a median comparison test in R? Thanks, Walt ________________________ Walter R. Paczkowski, Ph.D.

Can anyone point me in the right direction of figuring out what downweight() is doing? I am using vegan to perform CCA on diatom assemblage data. I have a lot of rare species, so I want to reduce the influence of rare species in my CCA. I have read that some authors reduce rare species by only including species with an abundance of at least 1% in at least one sample (other authors use 5% as a

Dear all Is there any way of doing a Tukey HSD post-hoc test after a Kruskal- Wallis or Friedman rank sum test (in the ctest package)? Thanks in advance, Albertus Dr. Albertus J. Smit Department of Botany University of Cape Town Private Bag Rondebosch 7700 South Africa Tel. +27 21 689 3032