similar to: Create grouping from TukeyHSD (as a duncan test does)?

I want to use the plot(model) function to generate Tukey-anscomb and Q-Q plots of a lm(). I manage to change all labels but the main one which apparently is neither main or sub. So far I have tried as par setting: cex (changes symbol size within the plot), cex.main (no effect), cex.sub (no effect) cex.lab (changes label size), cex.axis (changes axis label size). What I would like to change is the

I' ve seen that several people are looking for a function that creates a barplot with an error indicators (I was one of them myself). Maybe you will find the following code helpful (There are some examples how to use it at the end): # Creates a barplot. #bar.plot() needs a datavector for the height of bars and a error #indicator for the interval #many of the usual R parameters can be set:

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

Hi, I am searching for a possibility to display a logarithimic y-axis in a histogram. With plot that's easy (e.g. plot(1:10, log="y") but for histograms this does not work the same way: I tried hist(rnorm(1000), freq=FALSE, seq(-4, 4, .5), ylim=c(0.001, 0.5), log="y") Which gives the expected histogram but also warnings for my log="y" command

En innebygd og tegnsett-uspesifisert tekst ble skilt ut... Navn: ikke tilgjengelig Nettadresse: https://stat.ethz.ch/pipermail/r-help/attachments/20080129/5f8a6ac4/attachment.pl

I am trying to run a GLMM on some binomial data. My fixed factors include 2 dichotomous variables, day, and distance. When I run the model: modelA<-glmmPQL(Leaving~Trial*Day*Dist,random=~1|Indiv,family="binomial") I get the error: iteration 1 Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 >From looking at previous help

Dear R-Helpers, I would like to know what syntax I need to use to do a nested anova for 1. a continuous variable and 2. count data (x out of y) 1. The first I used to do in SPSS and I would like to be able to do it in R as well. This is the hierarchical model I would like to use: a continuous variable explained by factor A(fixed) + factor B(random) nested in A + factor C (random) nested in

list, i am using lmer to fit multilevel models and trying to use anova to compare the models. however, whenever i run the anova, the AIC, BIC and loglik are different from the original model output- as below. can someone help me out with why this is happening? (i'm hoping the output assocaited with the anova is right!). thank you, darren > unconditional<-lmer(log50 ~ 1 + (1 |

Dear All, I wonder if anyone can please offer any advice on a model including 2 fixed effects and 1 random effect, as well as a covariate? The experimental design is as follows: I have a two by two factor design, where the two factors, Age (A) and Group size (G), both have 2 levels (old or young, and 1 or 3 respectively), and I am interested in the effect of these factors upon a continuous

Hello, I am looking for an R function that impliments Duncan's post-hoc test. I am aware of multcomp and its function "glht" but, unless I am missing something, this cannot impliment the Duncan test. Any help of pointers are welcome. Bill Shipley

Hi, i?ve read all that i could about the post-hoc test for kruskall-Wallis, now i know that i need to run Duncan test, but i?m not able to find in any package or script. I s anybody knows where can i find it? Thanks ----- Mario Garrido Escudero PhD student Dpto. de Biolog?a Animal, Ecolog?a, Parasitolog?a, Edafolog?a y Qca. Agr?cola Universidad de Salamanca -- View this message in context:

I am searching for Duncan's Multiple Range test in R. Is it implemented in one of the packages the meantime? [[alternative HTML version deleted]]

Duncan writes: >Howyd all? Seems that I have been routed. Possibly >by a physical B&E, but who knows? Probably some >of you do.... anyways, some politically sensitive >email was deleted from a user account and the >line > >low -tr & > >inserted into my .xinitrc . > >Duncan (Dhu) Campbell > I didn't see a lot of feedback that struck me as

Hi All, Does anyone know how to get contrast statements or an output similar to TukeyHSD when doing survival analysis with the coxph function? Thanks, Adriana -- View this message in context: http://r.789695.n4.nabble.com/TukeyHSD-responses-in-R-tp2530120p2530120.html Sent from the R help mailing list archive at Nabble.com.

Hello, I think that to apply the Neuman-Keuls test under R it's possible with the TukeyHSD function and I know that to do a Neuman-Keuls test I have to sort the means but I can't use the TukeyHSD function I don't understand how to do to apply this function to my data. In the R help they use the TukeyHSD function to a aov() do you think that I have to do teh same? thanks.

R Version 2.0.0 Under development (unstable) (2004-05-07) I have been wanting to use TukeyHSD for two and three way aov's, as they are especially simple for students to use correctly. I realize model simplification is usually a preferred methodology, but my disciplinary enertia and simplicity of TukeyHSD is also compelling. However, the lack of names on the comparisons for the higher order

simint and TukeyHSD work for aov objects. Can someone point me to similar functions for lme objects? Dieter Menne

Dear list I need to see the code of plot.TukeyHSD. There is no .default for it. Please help. Lize -- This e-mail and its contents are subject to the South African Medical Research Council e-mail legal notice available at http://www.mrc.ac.za/about/EmailLegalNotice.htm

Hi, What is expression IC on TukeyHSD? contrast +/- qtukey(.95, nmeans, df) * sqrt(MSe/n) ? Thanks, Bruno [[alternative HTML version deleted]]

Hello all, I would like to relate this behaviour of: plot.table & plot.TukeyHSD. They don't work with some graphical parameters. Thanks for your attetion. Cleber > ### example: > plot( table(1:10), cex.axis=0.6) > plot( table(1:10), las=2) > > tukaov <- TukeyHSD( aov(breaks ~ wool + tension, data = warpbreaks) ) > > plot( tukaov, main='xxx' ) Error