similar to: Tukey test for subgroups in a data frame

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 all!

I'm new using R im trying to do a tukey test, but when i see the results the p value results in NA im guessing its because i have missing values but im not sure how to fix it AnovaModel.2 <- aov(area ~ trat, data=apilados) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) trat 4 11847 2961.76 9.9905 1.500e-06 *** Residuals 76 22531 296.46

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

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

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

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

I would like to do a one way anova and then a tukeyHSD. I have three vectors A,B and C. In a previous help message, I was told to do the following for the anova: y = c(A,B,C) group = factor(rep(a:3,c(7,9,13))) #provided there a 7 elements in A,9 in B and 13 in C and then anova(lm(y~group)) Looking at the tukeyHSD method it looks like it wants the aov method which I don't understand.

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 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

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,]

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

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[,

Hi, the function TukeyHSD gives incorrect results for balanced incomplete block designs, as the example below shows, but I can only half fix it. There are two problems, 1. It uses model.tables to estimate treatment means, 2. It uses the wrong standard error The first problem can be fixed using dummy.coef, if the lines > TukeyHSD.aov function (x, which = seq(along = tabs), ordered = FALSE,

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

Hello all, here's a real-world example: I'm measuring a quantity (d) at five sites (site1 thru site5) on a silicon wafer. There is a clear site-dependence of the measured value. To find out if this is a measurement artifact I measured the wafer four times: twice in the normal position (posN), and twice rotated by 180 degrees (posR). My data looks like this (full, self-contained code at

Hello dear R-help mailing list, A friend of mine teaches a regression and experimental design course and asked me the following question. She is trying to find a way to display the "homogeneous groups" (after performing tukey test on an aov object). here's an example for what she means by "homogeneous groups": She did one way anova and got these results for tukey test:

Hi all: I have a question about multi-comparison. The data is in the attachment. My purpose: Compare the predicted means of the 3 methods(a,b,c) pairwisely. I have 3 ideas: #idea1 result_aov<-aov(y~ method + x1 + x2) TukeyHSD(result_aov) diff lwr upr p adj b-a 0.845 0.5861098 1.1038902 0.0000001 c-a 0.790 0.5311098 1.0488902 0.0000002 c-b -0.055 -0.3138902

Hello, I'm trying to add a symbol (Delta) to plot legend with text using "expression(paste())" but this disables the text.font that allows to use bold or italic text. as follows: x=c(1:10) y=c(1:10) plot(x,y) legend(1,10,legend=c("A","B","C",expression(paste(Delta, D))), pch=c(24,18,17,16),cex=2,text.font=3,bty="n") Any suggestion to

Dear R People: In the DASL library, there is a story about hot dogs. Here are the data: Beef 186 495 Beef 181 477 Beef 176 425 Beef 149 322 Beef 184 482 Beef 190 587 Beef 158 370 Beef 139 322 Beef 175 479 Beef 148 375 Beef 152 330 Beef 111 300 Beef 141 386 Beef 153 401 Beef 190 645 Beef 157 440 Beef 131 317 Beef 149 319 Beef 135 298 Beef 132 253 Meat 173 458 Meat 191 506 Meat 182 473 Meat 190