similar to: Some graphical parameters don't works in plot.table and plot.TukeyHSD.

Dear R-developeRs, Attached follows a patch against svn 34959 that adds the printing of p-values to the TukeyHSD.aov function in stats package. I also updated the corresponding documentation file and added a 'see also' reference to the simint function of the multcomp package. As it was already brought up in a previous thread [1] in R-help, one can obtain the adjusted

Attached follows a patch to a minor change in the plot method of the TukeyHSD class (package stats). Basically it defines main= and xlab= as formal arguments of the plot function, with reasonable default values, passing them to title(), instead of using hard-coded only values for main= and xlab=. This way it's possible to change the title and xlab of the plots created using plot.TukeyHSD().

Dear R gurus, I have the following model: appcov.aov <- aov(yield ~ prevyield + trt + block) where prevyield is a continuous numeric covariate and trt and block are factors (yes, I did factor()!) Now, when I do a TukeyHSD, my diff's are all screwed up! For instance: treatment mean for treatmen "E" is 277.25 and for treatment "O" is 279.5, so I figure the diff O-E

Hey, I have this TukeyHSD output from which I would like to extract only the P-values (p adj, last number). The problem is that the test output is a character list. How can I "break" this sentence to separate the Pv? Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = Fe1$Fe ~ Fe1$genotype) $`Fe1$genotype` diff lwr upr

Hi, I am running a ANOVA on a factorial design, and using TukeyHSD for post hoc comparisons. I have 2 factors with three levels each: Factor B Factor A 1 2 3 1 2 3 When I look at the Tukey output (on the interaction of the factors) the comparisons come out numbered 1-36. e.g. $"A:B" diff lwr upr [1,] 49.1666667 -160.041022 258.3744 [2,]

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

I have a question about TukeyHSD and the glht function because I'm getting different answers when a covariate is included in model for ANCOVA.? I'm using the cabbages dataset in the 'MASS' package for repeatability.? If I include HeadWt as a covariate, then I get different answers when performing multiple comparisons using TukeyHSD and the glht function. The difference appears

Hi, I am seeking help with an error when running the example from R Documentation for TukeyHSD. The error occurs with any example I run, from any text book or website. thank you... > plot(TukeyHSD(fm1, "tension")). Error in plot(confint(as.glht(x)), ylim = c(0.5, n.contrasts + 0.5), ...) : error in evaluating the argument 'x' in selecting a method for function

Dear Help List, Thanks in advance for reading...I hope my questions are not too ignorant. I have an experiment looking at evolution of wing size [centroid] in fruitflies and the effect of 6 different experimental treatments [treatment]. I have five replicate populations [replic] in each treatment and have reared the flies in two different temperatures [cond] to assay the wing size, making

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

I'm interested in restricting the pairwise comparisons of interaction effects in a multi-way factorial ANOVA, because I find comparisons of interactions between all different variables different to interpret. For example (supposing a p<0.10 cutoff just to be able to use this example): > summary(fm1 <- aov(breaks ~ wool*tension, data = warpbreaks)) Df Sum Sq Mean Sq F

hello all I am trying to learn how to use the RGtk2 package... so, my first problem is: I don't get the right way for populate my gtkListStore object! any help is welcome... because I am trying several day to mount the code... Thanks in advanced Cleber N. Borges --------------------------- # my testing code library(RGtk2) win <- gtkWindowNew() datamodel <-

Hello useRs, The results of the SORT function differ from Scilab/Matlab for Complex Numbers in my example. This design is the desirable in R? Thanks. Cleber r <- c( 1.7507+0.1689i, 1.7507-0.1689i, 1.3886+0.0000i, 1.0458+0.0792i, 1.0458-0.0792i, 0.8279+0.1861i, 0.8279-0.1861i, 0.8263+0.3731i, 0.8263-0.3731i, 0.6548+0.0000i ) > cbind(sort(r, d=T)) [,1] [1,]

Hello All, I am trying to learn about the GUI in R (with GTK+Glade+RGtk2) and in my test I don't get sucess in to make an callback to destroy the application... When I try to define an function for "delet-event callback", I get the error message: (with mouse click in X window) *Error in function () : * * unused argument(s) (<pointer: 0x017ca000>, <pointer:

Hi all. This is more of a stats question, I suppose. Let's say I have two separate simple regressions of weight on year from two different datasets. I want to combine the regressions so that I can come up with a single equation for the total weight regressed on year. In reality, there is missing data, so I can't just sum the data across datasets and come up with a regression on the

Dear list, Sorry for posting a borderline statistical question on the list, but hte SPSS people around me just stares at me blankly when refering to tests with any term other than ANOVA and post-hoc. I would appreciate any insight on how this all is possible: I have a model fitted by aov() stored in "ppdur", which gives this result when using ANOVA: > anova(ppdur) Analysis of

Dear useRs, I'm working on multiple comparison design on two factor (2 3 levels) ANOVA. Each of the tests I have tried (Tukey, multcomp package) seem to do only with one factor at a time. fm1 <- aov(breaks ~ wool * tension, data = warpbreaks) tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE) $tension diff lwr upr p adj M-L -10.000000 -19.35342

With this data x <- c(0,0,1,1,2,2) y <- c(5,6,4,3,2,6) lwr <- y-1 upr <- y+1 xlab <- c("Low","Low","Med","Med","High","High") mydata <- data.frame(x,xlab,y,lwr,upr) I would like to make a dot plot and use lwr and upr as error bars. Above 0=Low. I would like there to be some space between the 5 and the 6 corresponding

Hi folks, I'm doing ANOVA with multiple comparisons. I've used both the TukeyHSD function and the multcomp procedure. In both cases, I get some tantalizing results such as this... e = aov(lm(d.all[,(n+4)] ~ d.all[,4]) TukeyHSD(e) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = lm(d.all[, (n + 4)] ~ d.all[, 4], contrast = C)) $`d.all[,

Dear all, I am studying a dataset using the aov() function. The independant variable 'cds' is a factor() with 8 levels and here is the result in studying the dependant variable 'rta' with aov() : > summary(aov(rta ~ cds)) Df Sum Sq Mean Sq F value Pr(>F) cds 7 0.34713 0.04959 2.3807 0.02777 Residuals 92 1.91635 0.02083 The dependant variable