similar to: P values option

>>>>> "BDR" == Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes: BDR> On Tue, 7 Mar 2000, Martin Maechler wrote: >> >>>>> "BDR" == Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes: >> BDR> On Tue, 7 Mar 2000, Trenkler, Dietrich wrote: >> >> I think I've discovered what went

>>>>> "BDR" == Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes: BDR> On Tue, 7 Mar 2000, Trenkler, Dietrich wrote: >> I think I've discovered what went wrong. >> >> My workspace included a function wilcox.test formerly copied from >> ctest. Now ctest ist part of the distribution and because of that I

On Tue, 7 Mar 2000, Martin Maechler wrote: > >>>>> "BDR" == Prof Brian D Ripley <ripley@stats.ox.ac.uk> writes: > > BDR> On Tue, 7 Mar 2000, Trenkler, Dietrich wrote: > >> I think I've discovered what went wrong. > >> > >> My workspace included a function wilcox.test formerly copied from > >>

# Your mailer is set to "none" (default on Windows), # hence we cannot send the bug report directly from R. # Please copy the bug report (after finishing it) to # your favorite email program and send it to # # r-bugs@biostat.ku.dk # ###################################################### Under R version 0.6.51 the following A_c(13,9,15,5,25,15,3,9,6,12) B_c(42,24,41,19,27)

Hi, Below I've flagged some sentences in the R documentation (from cvs) which are garbled in some way. If anyone can clarify what these sentences should say I'd be grateful. The problems were picked up as part of the proof-reading of the R manuals by Ciaran O'Riordan. After looking at the text neither of us could figure out what the documentation was meant to say. -- Brian Gough

Full_Name: Jason Polak Version: R version 2.5.0 (2007-04-23) OS: Xubuntu 7.04 Submission from: (NULL) (137.122.144.35) Dear R group, I have noticed a strange anomaly with the shapiro.test() function. Unfortunately I do not know how to calculate the shapiro test P values manually so I don't know if this is an actual bug. So, to produce the results, run the following code: pvalues = 0; for

I am working with some survival data with missing values. I am using the mice package to do multiple imputation. I have found code in this thread which handles pooling of the MI results: https://stat.ethz.ch/pipermail/r-help/2007-May/132180.html Now I would like to plot a survival curve using the pooled results. Here is a reproducible example: require(survival) require(mice) set.seed(2) dt

How do we get 2-tailed p-values for the rlm summary? I'm using the following: > fit <- rlm(oatRT ~ oatoacData$erp, psi=psi.bisquare, maxit=100, na.action='na.omit') > fitsum <- summary(fit, cor=F) > print(fitsum) Call: rlm(formula = oatRT ~ oatoacData$erp, psi = psi.bisquare, maxit = 100, na.action = "na.omit") Residuals: Min 1Q Median

It might help if you provided the code you used. It's possible that you didn't use direction="backward" in stepAIC(). Or if you did, it was still running, so whatever else you try will still be slow. The statement "R provides only the pvalues for each level" is wrong: look at the anova() function. Bob On 29 June 2017 at 11:13, Beno?t PELE <benoit.pele at

Hi folks, I'm doing ANOVA with multiple comparisons. I've used both the TukeyHSD function and the multcomp procedure. In both cases, I get some tantalizing results such as this... e = aov(lm(d.all[,(n+4)] ~ d.all[,4]) TukeyHSD(e) Tukey multiple comparisons of means 95% family-wise confidence level Fit: aov(formula = lm(d.all[, (n + 4)] ~ d.all[, 4], contrast = C)) $`d.all[,

Hello, i am a newby on R and i am trying to make a backward selection on a binomial-logit glm on a large dataset (69000 lines for 145 predictors). After 3 days working, the stepAIC function did not terminate. I do not know if that is normal but i would like to try computing a "homemade" backward with a repeated glm ; at each step, the predictor with the max pvalue would be

Hi everyone, I am working on analyzing spectra which requires hundreds of kruskal wallis tests to be performed. Once significant results are found post tests are performed ect. My questions are: how do I perform all these kw tests and save just the pvalues in a table? Is there anyway to have R automatically perform post tests if a result of p<0.05 is found? Currently when I run the test

Hi Michael, > -----Original Message----- > From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Michael > Friendly > Sent: Thursday, June 29, 2017 9:04 AM > To: Beno?t PELE <benoit.pele at acoss.fr>; r-help at r-project.org > Subject: Re: [R] Help : glm p-values for a factor predictor > > On 6/29/17 11:13 AM, Beno?t PELE wrote: > > My question is

Hello, I'm using the R package called mvpart, which is about the multivariate regression trees. The function I wrote is: mrt1<- mvpart(coefmat~sChip+sScreen+sMem,data=mixdata, xv="pick", plot.add=TRUE,uniform=TRUE,which=4,all=TRUE,xadj=2,yadj=2,rsq=TRUE,big.pts=TRUE,wgt.ave.pca=TRUE,legend=TRUE,bars=F, pca=TRUE) where "coefmat" is a matrix(of dimension N*K) to store

My two matrices are roughly the sizes of m1 and m2. I tried using two apply and cor.test to compute the correlation p.values. More than an hour, and the codes are still running. Please help to make it more efficient. m1 <- matrix(rnorm(100000), ncol=100) m2 <- matrix(rnorm(10000000), ncol=100) cor.pvalues <- apply(m1, 1, function(x) { apply(m2, 1, function(y) { cor.test(x,y)$p.value

Dear R users, Recent changes to the MuMIn package now means that the model averaging command (model.avg) no longer returns confidence intervals, but instead returns zvalues and corresponding pvalues for fixed effects included in models. Previously I have used this package for model selection/averaging following Greuber et al (2011) where it suggests that one should use confidence intervals from

Full_Name: Ludo Pagie Version: 2.8.1 OS: linux Submission from: (NULL) (194.171.7.39) p.adjust in stats seems to have a bug in handling n>length(p) for (at least) the methods 'holm' and 'hochberg'. For method 'holm' the relevant code: i <- 1:n o <- order(p) ro <- order(o) pmin(1, cummax((n - i + 1) * p[o]))[ro] where p is the

Hello, I'm using the R package called mvpart, which is about the multivariate regression trees. The function I wrote is: mrt1<- mvpart(coefmat~sChip+sScreen+sMem,data=mixdata, xv="pick", plot.add=TRUE,uniform=TRUE,which=4,all=TRUE,xadj=2,yadj=2,rsq=TRUE,big.pts=TRUE,wgt.ave.pca=TRUE,legend=TRUE,bars=F, pca=TRUE) where "coefmat" is a matrix(of dimension N*K) to store

How can I get the p values from a glm ? I want to get the p values so I can add them to a custom report fitwean<- glm(data[,"JWean"]~data[,"Group"],data=data,family=binomial(link ="logit")) summary(fitwean) # This lists the coefficeints, SEs, z and p values, but I can't isolate the pvalues. names(summary(fitwean)) # I see the coefficients,

On 01/04/2016 6:14 PM, John Sorkin wrote: > How can I get the p values from a glm ? I want to get the p values so I can add them to a custom report > > > fitwean<- glm(data[,"JWean"]~data[,"Group"],data=data,family=binomial(link ="logit")) > summary(fitwean) # This lists the coefficeints, SEs, z and p values, but I can't