similar to: How to set the number of multiple comparisons (Bonferroni-Holm)

Hello. We have some questions concerning the statistical analysis of a dataset. We aim to compare the sample means of more than 2 independent samples; the sample sizes are unbalanced. The requirements of normality distribution and variance homogeneity were not met even after transforming the data. Thus we applied a nonparametric test: the Kruskal-Wallis-test (H-Test). The null hypothesis was

Dear R community I sent a message out a while ago asking for help with multiple comparison tests for ANOVA's, but haven't had any response yet. I'm sending a final desperate plea. If I can't get this done in R I'm going to have to redo a whole lot of stuff in a commercial package, which I'm REALLY not keen to do! My problem is how to implement these tests in R. Below

The Partek package (www.partek.com) allows only two selections for Multiple Test Correction: Bonferroni and Dunn-Sidak. Can anyone suggest why Partek implemented Dunn-Sidak and not the other methods that R has? Is there any particular advantage to the Dunn-Sidak method? R knows about these methods (in R 2.1.1): > p.adjust.methods [1] "holm" "hochberg" "hommel"

Hi! I need to run a wilcoxon (Mann-whitly, in fact) test with bonferroni correction, as I am running 10 consecutive wilcoxon test not independent, and I know that bonferroni will partially correct for this problem, but I have no idea how to do it with R, I have been looking in the archive but couldn't understand how to do it. The format I am using at the moment is r4_o <- [1]

I am performing a sequential bonferroni adjustment on the results of an ANCOVA but the equation I have for calculating p-values from the t-values is not working. I can't seem to find it anywhere else. This is the code I have now: 2*(1-pt(t,df)) where t=t-value and df=degrees of freedom from the ANCOVA. Is there an error in the code? maybe another way to find the p-values? Help! -- View this

I append below a suggested update for p.adjust(). 1. A new method "yh" for control of FDR is included which is valid for any dependency structure. Reference is Benjamini, Y., and Yekutieli, D. (2001). The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29, 1165-1188. 2. I've re-named the "fdr" method to "bh" but

Hi, I have two questions want to ask. 1. If I have a matrix like this, and I want to figure out the rows whose value in the 3rd column are less than 0.05. How can I do it with R. hsa-let-7a--MBTD1 0.528239197 2.41E-05 hsa-let-7a--APOBEC1 0.507869409 5.51E-05 hsa-let-7a--PAPOLA 0.470451884 0.000221774 hsa-let-7a--NF2 0.469280186 0.000231065 hsa-let-7a--SLC17A5

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

Dear R users I have a question which is only partly related to R, but I'm sending it to this list in the hope that someone with a better statistical background than mine will be kind enough to help me. It concerns multiple comparison testing in ANOVA. I know this subject has been raised before on this list, and I have looked through the relevant messages and read the article in

Hi, I'm having no luck figuring out how to find Bonferroni simultaneous confidence intervals to obtain a family of estimates in R. Does anyone know how to do this? Thank you!

Hello- I have run an ANOVA on 4 treatments with unequal sample sizes (n=9,7,10 and 10). I want to determine where my sig. differences are between treatments using a Bonferroni test, and have run the code: pairwise.t.test(Wk16, Treatment, p.adf="bonf") I receive an error message stating that my arguments are of unequal length: Error in tapply(x, g, mean, na.rm = TRUE) :

Dear R-users, In a randomized placebo-controlled within-subject design, subjects recieved a psycho-active drug and placebo. Subjects filled out a questionnaire containing 15 scales on four different time points after drug administration. In order to detect drug effects on each time point, I compared scale values between placebo and drug for all time conditions and scales, which sums up to

Here is a cleanroom specification of RTMPE which has been documented from rtmpdump v1.6 source code. updates will be at: http://lkcl.net/rtmp/RTMPE.txt it is from the perspective of a client however implementing from the perspective of a server is a trivial matter. please distribute freely and widely. please implement and distribute even wider, in both client and server. note: absolutely no

Hola a todos/as estoy analizando un diseño factorial con una variable intrasujeto y una inter-sujeto. Ambas resultan significativas en el anova y quiero hacer un análisis por pares, pero aún no sé como puedo hacerlo en R. Me interesan ambos Bonferroni y Tukey. ¿Alguien podría indicarme? Gracias [[alternative HTML version deleted]]

Please can someone advise me how I can adjust correlations using bonferroni's correction? I am doing manny correlation tests as part of an investigation of the validity/reliability of a psychometric measure. Help would be so appreciated! Cheers, Louise

Hi- Is anyone working on multiple comparisons of means or post-hoc tests (ie: Tukey, Bonferroni) for R? I saw in the winter 98' archives of the R mailing lists that these tests had not been implemented yet, I was just wondering if I could look foward to having them. ;-) I also looked through the contributed packages and didn't see anything that offered such tests. I guess I could check

I am familiar with pairwise t-tests, corrections for multiple testing, etc. however I have a problem whose answer I have not found after extensive R-help archive and Google searching. What I have done in the past: I have N items which are measured, exposed to a condition, and then measured again. I wish to know if the condition changes the items so I can perform a t-test. Better yet I can

Hello every GNU's, I have a question about mt.plot, on multtest package. I'm wondering how do a plot with test like bonferroni, holm, hochberg,... and also résults of SAM(Significiance Analysis Microarrays) as we can see on several document of Sandrine Dudoit. Thanks a lot and Have a nice day Sandrine --------------------------------------------------------------------------------

Also Consider Bonferroni Hochberg Holm type procedures or . Dunn OJ. Multiple contrasts using rank sum tests. Technometrics 1964;6:241#/52. [[alternative HTML version deleted]]