similar to: Calculating Leverage or studentized residuals with gls

Thank you all for your help about the encoding of my yahoo.gr. I am sending you now the original data. Hope tha you can read them. I am trying to create a clustered barplot for the following 2 variables: > h$rocbiopsy1 聽 [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 聽[38] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 聽[75] 0 0 0 0 0

Hi to everybody is there any way to update from an older version of R to a newer one without having to reload all the packages I've used up to that time?Many regards to all of you ___________________________________________________________ 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

Goodmorning to everyone, I am trying to create a clustered barplot for the following 2 variables who have more cases but as an example i am giving you 10 items: sex聽 socio-economic status 1聽聽聽聽 1 2聽聽聽聽 2 2聽聽聽聽 2 2聽聽聽聽 3 2聽聽聽聽 2 1聽聽聽聽 4 1聽聽聽聽 1 1聽聽聽聽 1 2聽聽聽聽 3 2聽聽聽聽 1 where sex: 1:man, 2:woman 聽聽聽聽聽聽聽聽聽聽 socio-economic status:1:Low, 2:Medium, 3:High, 4:Very High Does anybody came across with a

Goodevening to everyone, I am trying to create a clustered barplot for the following 2 variables who have more cases but as an example i am giving you 10 items: sex聽 socio-economic status 1聽聽聽聽 1 2聽聽聽聽 2 2聽聽聽聽 2 2聽聽聽聽 3 2聽聽聽聽 2 1聽聽聽聽 4 1聽聽聽聽 1 1聽聽聽聽 1 2聽聽聽聽 3 2聽聽聽聽 1 where sex: 1:man, 2:woman 聽聽聽聽聽聽聽聽聽聽 socio-economic status:1:Low, 2:Medium, 3:High, 4:Very High Does anybody came across with

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]]

Has anyone written a function to find the quantiles for the studentized maximum distribution, and the studentized maximum modulus distribution? Thanks for any help. Kostas --------------------------------- [[alternative HTML version deleted]]

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

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

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

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

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 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 <-

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

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

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