similar to: Please help: ANOVA with SS Type III for unequal sample sized data

Dear R experts, I'm new in R and a beginner in terms of statistics. It should be simple question, but definitely difficult to solve it by myself. I'd like to see main effect of group(gender: sample size is different(M:F=23:18) and one of condition(cond) and the interaction at each subset from 90 datasets So I perform anova 90 times using a command like below; for(i in 1:90)

Hello everybody, I use the sweetpotato database included in R package: data(sweetpotato) This dataset contains two variables: yield(continous variable) and virus(factor variable). Due to Levene test is significant I cannot assume homogeneity of variances and I apply Welch test in R instead of one-way ANOVA followed by Tukey posthoc. Nevertheless, the problems come from when I apply posthoc

Dear colleagues, In checking a function I am adding to an R package, I get the following warning pair: ... Bad \usage lines found in documentation object 'nominal': "\\method{print}{nominal}"(x, max.print = 10, posthoc = "std.pearson.residuals.sign", assoc = ifelse("univariate" list(c("N", "alpha.X2",

dear all, I want to perform a posthoc test for my ANCOVA: a1<-aov(seeds~treatment*length) With summary(glht(a1, linfct = mcp(treatment = "Tukey"))) R tells me: "covariate interactions found -- please choose appropriate contrast" How do I build these contrasts? Ideally, I would like to have the posthoc test for the ANCOVA including a block-effect

Hello all, Quick question: I want to do posthoc contrasts for a linear mixed effects model. However, when trying to use the csimtest function in multcomp package I receive an error message saying it cannot find the function, even after installing and loading package multcomp. Any pointers would be greatly appreciated Daniel

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

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 quiet new with R and I have some problems to perform a posthoc test with an lme model. My model is the following: >lme1<-lme(eexp~meal+time, random=~1|id,na.action=na.omit) and then i try to get a post hoc test: >summary(glht(lme1,linfct=mcp(meal="Tukey))) but I get a warning message: Erreur dans as.vector(x, mode) : argument 'mode' incorrect Thank you for your

Thanks in advance for your help. I am running a repeated measures ANOVA in r. The same group undergoes to four different treatment conditions. So, all individuals are treated with treatments A, B, C and D in four different occasions. Once I get a significant ANOVA, I first run a paired samples t-test using the code: t.test(X1,X2,paired=TRUE) #being x1 the punctuation after treatment 1 and x2 the

DaeRyong Jeong reports a race between vhost_dev_cleanup() and vhost_process_iotlb_msg(): Thread interleaving: CPU0 (vhost_process_iotlb_msg) CPU1 (vhost_dev_cleanup) (In the case of both VHOST_IOTLB_UPDATE and VHOST_IOTLB_INVALIDATE) ===== ===== vhost_umem_clean(dev->iotlb); if (!dev->iotlb) { ret = -EFAULT; break; } dev->iotlb = NULL; The reason is

Hi all, I apologize for what might be a silly question. I am interested in doing a one way anova. This is not too hard in and of itself, either with anova, aov or oneway.test . However, I need to 1) get pvalues, 2) do a posthoc analysis with Tukey HSD, 3) and have (sometimes) an unbalanced design. I just can't seem to put all the pieces together. Any suggestions? Thanks in advance, Dan.

On Mon, May 21, 2018 at 10:38:10AM +0800, Jason Wang wrote: > > > On 2018?05?18? 17:24, Jason Wang wrote: > > > > > > On 2018?05?17? 21:45, DaeRyong Jeong wrote: > > > We report the crash: KASAN: use-after-free Read in vhost_chr_write_iter > > > > > > This crash has been found in v4.17-rc1 using RaceFuzzer (a modified > > >

On Mon, May 21, 2018 at 10:38:10AM +0800, Jason Wang wrote: > > > On 2018?05?18? 17:24, Jason Wang wrote: > > > > > > On 2018?05?17? 21:45, DaeRyong Jeong wrote: > > > We report the crash: KASAN: use-after-free Read in vhost_chr_write_iter > > > > > > This crash has been found in v4.17-rc1 using RaceFuzzer (a modified > > >

On 2018?05?17? 21:45, DaeRyong Jeong wrote: > We report the crash: KASAN: use-after-free Read in vhost_chr_write_iter > > This crash has been found in v4.17-rc1 using RaceFuzzer (a modified > version of Syzkaller), which we describe more at the end of this > report. Our analysis shows that the race occurs when invoking two > syscalls concurrently, write$vnet and

On 2018?05?17? 21:45, DaeRyong Jeong wrote: > We report the crash: KASAN: use-after-free Read in vhost_chr_write_iter > > This crash has been found in v4.17-rc1 using RaceFuzzer (a modified > version of Syzkaller), which we describe more at the end of this > report. Our analysis shows that the race occurs when invoking two > syscalls concurrently, write$vnet and

Dear R experts I am running a negative-binomial GLM (glm.nb) to test the null hypotheses that species 1 and 2 are equally abundant between site 1 and site2, and between each other. So, I have a 2x2 factorial design with factors Site (1,2) and Taxon (1,2). Since the Site:Taxon interaction is significant, I need to do the equivalent to a "post-hoc test" for ANOVA, however, the same tests

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

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

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

Hi everyone I have the following situation: In a longitudinal study, subjects fill out a questionnaire every year (repeated measurements over time). Also, the subjects are nested within departments. There is an intervention going on over time. The outcome variable is continuous. Now I'd like to analyse two things: 1. Is there a significant change over time? I think this is done by a