similar to: Studentized maximum distribution

Can anyone tell me how to calculate quantiles of the studentized maximum modulus distribution using R? Thanks! __________________ Rob Kabacoff, Ph.D. www.statmethods.net [[alternative HTML version deleted]]

Well, it is clear enough that the problem is in interpreting the documentation. However, when you claim you tested something, and found it inconsistent with tables, it would be advisable to back it up with examples! The description in the help files and in the sources is admittedly confusing. The original paper has this, rather more clear, description in the abstract: "We consider the

We cannot help you understand what you are doing if you do not show us what you are doing. Here are some discussions about how to communicate questions about R [1][2][3]. [1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [2] http://adv-r.had.co.nz/Reproducibility.html [3] https://cran.r-project.org/web/packages/reprex/index.html -- Sent from my phone.

Dear all, I wanted to compare Bonferroni vs TukeyHSD correction over a range of groups and group sizes, and wanted to use the function qtukey. In the help documentation it says qtukey(p, nmeans, df, nranges = 1, lower.tail = TRUE, log.p = FALSE) Arguments q vector of quantiles. p vector of probabilities. nmeans sample size for range (same for each group). df degrees of freedom for s (see

Dear colleagues, hi. I am using the function gls in R-package nlme and I want to compute either the leverage values or the studentized residuals. Can any body tell me how can do that? With regards ___________________________________________________________ 围蟻畏蟽喂渭慰蟺慰喂蔚委蟍[elided Yahoo spam]] 螔伪蟻蔚胃萎魏伪蟿蔚 蟿伪 蔚谓慰蠂位畏蟿喂魏维 渭畏 未喂伪胃苇蟿蔚喂 蟿畏谓 魏伪位蠉蟿蔚蟻畏 未蠀谓伪蟿萎 蟺蟻慰蟽蟿伪蟽委伪 魏伪蟿维 蟿蠅谓 蔚谓慰蠂位畏蟿喂魏蠋谓 渭畏谓蠀渭维蟿蠅谓

Hi all, My question is not really urgent. I can write a loop and solve the problem. But I know that I'll be in a similar situation many more times so it would be useful to find out the answer Is there a fast way to perform linear fit to all the columns of a matrix? (or in the one dimension of a multi-dimensional array.) I'm talking about many single linear fits, not about a multiple fit.

Hello, I am trying to calculate and plot mean and confidence intervall for a set of data. This is the code that I am currently using: means <- sapply(data, mean, na.rm=TRUE) n <- sapply(data,length) stdev <- sqrt(sapply(data, var, na.rm=TRUE)) ciw <- qt(0.98, n) * stdev / sqrt(n) par(mgp=c(2,0.6,0), las=2, fin=c(7,3), mai=c(1,0.5,0.2,0.2), cex=0.8) plotCI(x=means, uiw=ciw,

Dear Friends and Colleagues, I'm puzzling over how to interpret or use some bootstrap intervals. I think that I know what I should do, but I want to check with knowledgeable people first! I'm using a studentized non-parametric bootstrap to estimate 95% confidence intervals for three parameters. I estimate the variance of the bootstrap replicates using another bootstrap. The script

I am trying to find studentized bootstrap intervals for the skewness of a data set. I have tried the following (nerve.dat is a set of data containing observations on one variable) (using Windows XP): x <- scan("e:/Flashbackup2009/Nonparametrics/nerve.dat") n <- length(x) library(e1071) skewness(x) library(boot) sampleskew <- function(x,d) {return(skewness(x[d]))} bb <-

Dear friends, In s-plus, lm() generates the the studentized residuals automatically for us, and In R, it seems don't have the results: After i fitted lm(), i use attibutes() to see the objects and didn't find studentized residuals . How to get the the studentized residuals in lm(),have i missed something? thanks very much! -- Kind Regards, Zhi Jie,Zhang ,PHD Department of Epidemiology

Hi folks, This applies to src/share/man/man5/moduli.5 in the OpenBSD source tree, and doesn't seem to apply to the portable OpenSSH, so I've sent this change here instead of via Bugzilla. The wording of moduli(5) implies that sshd puts more thought about which modulus it selects than it really does. The following patch corrects this. Simon. -- Simon Burge

Hi all, I'm trying to apply loess regression to my data and then use the fitted model to get the *standardized/studentized residuals. I understood that for linear regression (lm) there are functions to do that:* * * fit1 = lm(y~x) stdres.fit1 = rstandard(fit1) studres.fit1 = rstudent(fit1) I was wondering if there is an equally simple way to get the standardized/studentized residuals for a

I have summary statistics from many sets (10,000's) of near-normal continuous data. From previously generated QQplots of these data I can visually see that most of them are normal with a few which are not normal. I have the raw data for a few (700) of these sets. I have applied several tests of normality, skew, and kurtosis to these sets to see which test might yield a parameter which

Dear R-Help, I am using the studres function from the MASS package to compute studentized deleted residuals in a oneway anova. I'm having trouble interpreting the results in situations where a factor level has only one observation. Sometimes studres yields an NaN and sometimes it produces a numeric value for cases where a factor level has only one observation. I would think it should

its quite likely fixed in 0.4.22 ---Aki TuomiDovecot oy -------- Original message --------From: Larry Rosenman <larryrtx at gmail.com> Date: 02/03/2018 19:55 (GMT+02:00) To: Konstantinos Tsakiltzidis <ktsakiltzidis at modulus.gr>, dovecot at dovecot.org Subject: Re: Sieve filter imapflags with "flagvar" not working I don't remember if this was fixed in 0.4.21 or not,

Dear list, I have got an array with observational values t and I would like to fit a geometric distribution to it. As I understand the geometric distribution, there is only one parameter, the probability p. I estimated it by 1/mean(t). Now I plotted the estimated density function by plot(ecdf(t),do.points=FALSE,col.h="blue"); and I would like to add the geometric distribution. This

Hi, I must be doing something silly here, because I can't get the studentised and standardised residuals from r output of a linear model to agree with what I think they should be from equation form. Thanks in advance, Jennifer x = seq(1,10) y = x + rnorm(10) mod = lm(y~x) rstandard(mod) residuals(mod)/(summary(mod)$sigma) rstudent(mod)

Dear useRs I need to compute studentized confidence intervals for a correlation, using the boot library. For this CIs we need to compute a variance estimate of the statistic (here correlation coeff) from each boostrap sample. There are 2 important points, I think: (1) We need to do a fisher transformation (atanh(x)) to correct for non-normality, this can be done easily be specifying h, hinv, and

Hi, I am wondering if there is a way to extract studentized residues from a nls() object? I searched archive, someone posted a similar question before (http://tolstoy.newcastle.edu.au/R/e6/help/09/04/10845.html), but didn't get an answer. Thanks for any suggestions, John

Hi there, I hope I can get advice regarding the calculation of leverage values or studentized residual values of a non-linear regression model. It seems like rstudent() does not work on a nls object. Many thanks in advance! Best regards, Xingli