similar to: Multiply Iterated Measurements and Pairwise Comparison

I am analyzing some data obtained after measuring some parameters at different times in samples obtained from many subjects. The model is quite simple: aov(parameter ~ Time + Error(Subject/Time)) Now I want to make a pairwise comparison between the levels of Time. However, I have not find how to do such a thing. I cannot use TukeyHSD or pairwise.t.test, I supposse. Maybe using contrasts? Could

Dear list I am comparing the results of several different experimental setups. With kruskal.test() I can test if there is any difference at all in any of them, if I understand it correctly. But now, when there is a difference, how do I generate a (half-) table of pairwise comparisons, using e.g. wilcox.test(), to find the ones where the difference actually occurs. I guess I don't have to

Hi, I'm wondering if i can use the function "TukeyHSD" to perform the all pairwise comparisons of a "aov()" model with one factor (e.g., GROUP) and one continuous covariate (e.g., AGE). I did for example: library(multcomp) data('litter', package = 'multcomp') litter.aov <- aov(weight ~ gesttime + dose, data = litter) TukeyHSD(litter.aov, which =

I am wondering how to customize a pairwise comparisons plot of a factorial ANOVA, without doing a lot of manual manipulation of a TukeyHSD object. The customizations I'd like are: 1. The aov used log-transformed response data, but I'd like to plot the intervals on their original, untransformed scales 2. Plot all the main and interaction effects together, rather than in a separate

Hi R people I am using regsubsets() to fit large numbers of models and collect summary statistics in order to perform a Bayesian analysis of multi-way ANOVA with specific prior information. In general the variables have differing numbers of levels >=2. This works well but with variable of more than 2 levels there are naturally some arbitrary decisions about which treatment contrasts to

Hi! I have been working several years with R but it's my first public question. I hope I'll be clear :) . This question is related to obtaining letter-based representation of non-parametric pairwise comparisons. I have a dataframe with this structure (but with quite more rows and cols): A B C factor 1 2 2 one 2 1 2 one 2 2 3 two 2 3 2 two 1 4 2 three 9 8 1 three I have no normality,

Hi!, I'm really hoping someone out there will be able to help me. I recently started my MSc dissertation on Population Projection Matrices, which has been going well until now. I am trying to set-up a general script that does a pairwise comparison of all elements in my matrices. So for example, given that I have the following matrix S: > S [,1] [,2] [,3] [1,]

Hello, I have a linear model with different slopes for different treatment groups. I need to pairwise compare the different slope estimates for the different treatment groups. Is there a package that does pairwise comparisons of slope coefficients, making the appropriate adjustments in the P values? Thanks, John. -- ========================================== John Janmaat Department 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

Hi, I've got this model and following Hothorn et al advices, I used glht for a post hoc comparison > modezqM<-glm(rojos~estacion*zona3,quasipoisson,subset=(edadysexo=="M")) > anova(modezqM,test="F") Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 293 41148

Also, this works (taking out multcomp=TRUE, multcompmethod="adjusted"): com.test=ma(Head.W1~Leg.3.1+Site, type="elevation", data=queens) print(com.test) ....so for some reason it will do an MA regression on all my data point together, but shows an error when I try to do pairwise comparisons between groups. Thank you, Ioulia -------- On Fri, May 11, 2012 at 2:09 AM, Ioulia

Hello, I have a unconventional question arising from my current master thesis on regression modeling. Suppose we have fitted a (linear) relationship between a dependent variable y and an independent variable x. Now we choose two points on the x-axis, i.e. according to percentiles x10 and x90. These two points are chosen to select the data points for two groups in order to perform pairwise

Hello R listing, I did two-way anova on lm. Further question the investigator interested in is what two treatments are different? I am looking for a command which could do pairwise comparison for every treatment. Could anyone help me out? Thanks a bunch Kevin

Hello, I am a new R user. I am trying to calculate vector correlations for all pairwise comparisons in my data frame without repeats. I am familiar with the expand.grid function, but this includes repeats. Is there a way to use expand.grid and eliminate repeats? Or is there another function that can be used to do this? Thank you. Rebecca -- Rebecca Young Graduate Student Ecology &

Hey all, I'm trying to get Samba 3.0.4 to work with Netscape Directory Server 4.x with LDAP. The problem is that I'm not getting authenticated thru LDAP. I've tried searching the archives(MARC), google and can find some info but nothing seems to fix it. I've loaded the Netscape V4 schema from the examples/LDAP. Included at the end is both my smb.conf file and my LDIF

Hi, I have a number of genes (columns) for which I want to examine pairwise associations of genotypes (each row is an individual)...For example (see data below), I would like to compare M1 to M2, M2 to M3, and M1 to M3 (i.e. does ac from M1 tend to be found with bc from M2 more often than expected.) Down stream I will be performing chi square tests for each pair. But I am looking for a way to

Hello, this is probably more a statistical question than an R-specific problem, but I'll risk it. I've fitted a Cox Proportional hazard model with one factor Treatment (seven levels) as a predictor variable. The general Null hypothesis (all groups show the same survival behaviour) is clearly rejected. Now, is there any (statistically sensible) way of doing pairwise comparisons and/or

Dear R-help, I have a question about pairwise.t.test and adjustment for multiple comparisons for paired data points. I have the following data: n=c("x", "x", "x", "x", "x", "x", "x", "x", "x", "x", "y", "y", "y", "y", "y", "y",

pairwise.t.test is returning NAs when one of the samples only has one entry, while TukeyHSD returns results (maybe not trustworthy or believable, but results). I stumbled on this because I did not realize one of my samples only had one entry while most of the others had several hundred, so I realize this is not a desirable situation. I'm really just curious about the difference between how

Hi UseRs, I don't want to die beeing idiot... I dont understand the different results between: cor() and cov2cov(cov()). See this little example: > x=matrix(c(0.5,0.2,0.3,0.1,0.4,NA,0.7,0.2,0.6,0.1,0.4,0.9),ncol=3) > cov2cor(cov(x,use="pairwise.complete.obs")) [,1] [,2] [,3] [1,] 1.0000000 0.4653400 -0.1159542 [2,] 0.4653400 1.0000000