search for: bonferroni

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 mo...

Dear all, I am trying to run Wilcoxon Rank-Sum Test with Bonferroni's correction. I have two lists: l0, l1: mapply(function(x,y)wilcox.test(x,y)$p.value, l0, l1) How do I run Bonferroni's correction on mapply? Any help is much apperciated. Thanks, -Raj

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!

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

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" &quo...

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?...

Matt, Here's a Bonferroni-corrected multiple one-sample t-test that I wrote some years ago. It took a while to get it into R, as na.omit doesn't seem to handle vectors and I had to write a quick kludge (na.remove). Another more general point was that I discovered that the help page for t.test gives the name "para...

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]]

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 che...

...avor, edite la > linea del asunto (subject) para que el texto sea mas especifico que: > "Re: Contents of R-help-es digest...". Además, por favor, incluya en > la respuesta sólo aquellas partes del mensaje a las que está > respondiendo. > > Asuntos del día: > > 1. bonferroni en R (Miguel Lázaro) > 2. Re: bonferroni en R (Jorge Ivan Velez) > 3. Re: bonferroni en R (Olivier Nuñez) > 4. Re: bonferroni en R (Olivier Nuñez) > 5. distintos resultados en SPSS y R (Miguel Lázaro) > 6. Re: distintos resultados en SPSS y R (Carlos J. Gil Bellosta ) >...

...of a dataset, which resulted in rejection of the null hypothesis (Kruskal-Wallis-test = H-test). In order to find out which sample means actually are statistically different, we want to do multiple comparisons with the Wilcoxon rank sum test (= U-test); the p-level should be corrected according to Bonferroni-Holm. Thus we decided to use the following R syntax: pairwise.wilcox.test (x, g, p.adjust.method = "holm", paired = FALSE) Since there are 22 sample means in the dataset, lots of comparisons are possible and the Bonferroni-Hom correction method would apply very low p-levels. Since we ar...

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) : arguments must have same length Is there a way to run this test even with unequal samp...

Hi, I was trying to figure out how to do post-hoc tests for Two Way ANOVAs and found the following 2 approaches: a. Do pairwise t-tests (bonferroni corrected) if one finds significance with the ANOVA. Link- http://rtutorialseries.blogspot.com/2011/01/r-tutorial-series-two-way-anova-with.html b. Do TukeyHSD on an aov model Link- http://www.r-bloggers.com/post-hoc-pairwise-comparisons-of-two-way-anova/ Running the data set given in the first...

...'m battling to follow what this code exactly does (especially the last bit). How would I link it to the ANOVA results? What sort of output would these functions give me - adjusted p values? How would I then interpret these? I'd really appreciate any help!!! ----------------- Code for Bonferroni, Holm and Hochberg post hoc tests: p.adjust.holm <- function(p, n=length(p)) { ##n <- length(p) r <- rank(p) index <- order(p) qi <- p*(n+1-r) for (i in 2:length(p)) { qi[index[i]] <- max(qi[index[i+1]]) } list(adjp=pmin(qi, 1), p=p, method=&quot...

Hola, Lo primero muchas gracias a todos por vuesta habitual ayuda. Llevo meses con las funciones lmer, pero tengo un problema no se como plantear unas comparaciones multiples una vez demostrado que el factor tiene diferencias significativas. Tengo multiples opciones, Solución A: una es hacer un bonferroni a saco (pero eso me chirria ya que tego muchos datos) Solución B: Otra es utilizar glht, pero esa opción no me convence del todo. Solución C: Otra es intentar imitar este articulo cosa que he sido incapaz(tabla 1) http://www.mitpressjournals.org/doi/pdfplus/10.1162/jocn_a_00143 http://cran.r-proj...

...E, dimnames = list(c("A", "B"), c("C.1", "C.2", "C.3"))) # cm1 = contrast matrix for main effects v1 <- csimint(estpar=c(100, 6, 5), df=4*n-3, covm=cov1*sigma^2/n, cmatrix=cm1, conf.level=0.95) summary(v1) The adjusted p-values are almost the Bonferroni p-values. If I understood right: You need not to adjust for multiple testing on main effects in a 2x2 factorial design assuming the absence of interaction. I do not think that there is a bug, I want to understand, why multcomp does adjust for multiple tests having all information about the des...

...only corresponding to groups 5 and 6. This is, of course, equivalent to a t-test. I now get an F of 142.3 with 1 and 3 degrees of freedom and a null probability of 0.001. I know that multiple comparisons changes the model-wise error rate, but even if I did all 15 comparisons of the 6 groups, the Bonferroni correction to a 5% alpha is 0.003, yet the Bonferroni correction gives conservative rejection levels. How can such a result occur? Any clues would be helpful. Thanks. Bill Shipley Associate Editor, Ecology North American Editor, Annals of Botany Département de biologie, Université de Sher...

...Hi Rer's, R-1.1.0 I have two questions for you: 1) I am trying to complete a multiple comparison test after completing a one-way ANOVA on some data. I think this is pretty reasonable. aov(MetricSubset ~ GeneNameFactor) works pairwise.t.test(MetricSubset,GeneNameSubset,p.adjuxt.method=bonferroni,p ool.sd=FALSE) returns, Error in meanplot.R(c(3258, 3780, 3968, 2577, 8831, 2501, 2586, 2679, : couldn't find function "pairwise.t.test" I also looked at the R function p.adjust I am not sure how p.adjust with multiple comparisons. Does R support multiple comparison...

...Hasta ahora todo marcha bien, pero cuando intento hacer las comparaciones multiples con LSD obtengo un error. Lo declare de la siguiente forma: Anavar<-aov(Resp~A*B*C,data=Resultado) Comparacion1 <- LSD.test(ANAVAR,"A*B*C",DFerror=6213,MSerror=0.01619,p.adj="bonferroni",group=T) Comentaros tambien que declare p.adj="bonferroni" pues los datos son desbalanceados. Agradezco cualquier sugerencia que podais dar Saludos!! [[alternative HTML version deleted]]

...47625121599, -10.6664750811034, 21.3346023277454, 4.99216977422232) Then I do the tests > pairwise.t.test(d,n,p.adjust="bonf",paired=F) Pairwise comparisons using t tests with pooled SD data: d and n x y y 0.092 - z 0.326 1.000 P value adjustment method: bonferroni Assuming the data are paired: > pairwise.t.test(d,n,p.adjust="bonf",paired=T) Pairwise comparisons using t tests with pooled SD data: d and n x y y 0.092 - z 0.326 1.000 P value adjustment method: bonferroni I have no indication that the test is taking...