similar to: pairwise comparisons among treatments

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,

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

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 &

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 # I have a question regarding pairwise calculations of a matrix using a "for-loop." # Below I have a matrix "X" with 8 columns. These are genotypic data so Column1 & Column2 is # a unit, Column3 & Column4 is a unit, Column5 & Column6 is a unit, and Coulmn7 & 8 is a unit. # I have a loop designed to calculate the number of times an individual in

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

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",

Dear list, When I use VennDiagram package, I got a error as follow: venn.plot <- draw.pairwise.venn( area1 = 3186, area2 = 325, cross.area = 5880); Error in draw.pairwise.venn(area1 = 3186, area2 = 325, cross.area = 588) : Impossible: cross section area too large. Does anyone have suggestion? Thank you.

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Is there a means to split a vector into its individual elements without going the brute-force route for arguments to a predefined function call? offred.rgb <- c(1, 0, 0) * 0.60; ## Brute force style offred.col <- rgb(offred.rgb[1], offred.rgb[2], offred.rgb[3], names = "offred") ## Desired style

Hi all, I'm not quite sure if this is a feature or a bug or if I just fail to understand the documentation: If I use cor() with pairwise.complete.obs and method=pearson, the result is a scalar: ->cor(c(1,2,3),c(3,4,6),use="pairwise.complete.obs",method="pearson") [1] 0.9819805 The documentation says that " '"pairwise.complete.obs"' only

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

Dear all, I am perplexed when trying to get the same results using pairwise.t.test and t.test. I'm using examples in the ISwR library, >attach(red.cell.folate) I can get the same result for pairwise.t.test and t.test when I set the variances to be non-equal, but not when they are assumed to be equal. Can anyone explain the differences, or what I'm doing wrong? Here's an example

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

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