similar to: studentized deleted residuals and NA's

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

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

I have run into a general problem with subsetting of which the following is a simple example. Suppose that x <- c(5, NA, 7, 5, NA, 3) y <- c(1, 2, 3, 4, 5, 6) I want to extract the values of y for which x = 5, but y[x==5] yields > y[x==5] [1] 1 NA 4 NA I find that y[!is.na(x) & x==5] yields the desired result: > y[!is.na(x) & x==5] [1] 1 4 but I am wondering whether

Hi, I wonder if anybody could help me in converting this easy SAS program into R. (I'm still trying to do that!) PROC IMPORT OUT= WORK.CHLA_italian DATAFILE= "C:\Documents and Settings\carleal\My Documents\REBECCA\stat\sas\All&nutrients.xls" DBMS=EXCEL2000 REPLACE; GETNAMES=YES; RUN; data chla_italian; set chla_italian;

I have a puzzle as to how R is computing Cook's distance in weighted linear regression. In this case cook's distance should be given not as in OLS case by h_ii*r_i^2/(1-hii)^2 divided by k*s^2 (1) (where r is plain unadjusted residual, k is number of parameters in model, etc. ) but rather by w_ii*h_ii*r_i^2/(1-hii)^2 divided by k*s^2,

Well I suppose a warning's not going to hurt. Even in a case like the occupationalStatus example where you know some points have been fitted exactly, it might be useful to be reminded that the standardised residuals for these points are then NaN and cannot be displayed. Of course when you don't know in advance that this issue will arise, there is even more reason to give a warning.

Dear R Users, I want to write a function that applies to the dataframe and variables that were used in a previous call to lm or aov. In order to do this, I need to write a function that applies to the output of lm or aov, and yields the names of the dataframe and variables that were used in the lm or aov analysis. For example, suppose that I give the command: aov.out <- aov( Rt ~

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 R Help, I am trying to create a boolean vector that is TRUE whenever a particular value occurs in a numeric vector, and FALSE otherwise. For example, suppose that > y <- c(5, 2, 4, 3, 1) > y [1] 5 2 4 3 1 and suppose that I want to find where 3 occurs in y. Then, the following yields the solution: > y == 3 [1] FALSE FALSE FALSE TRUE FALSE My problem arises when the

As some of you R-devel readers may know, the plot() method for "lm" objects is based in large parts on contributions by John Maindonald, subsequently "massaged" by me and other R-core members. In the statistics litterature on applied regression, people have had diverse oppinions on what (and how many!) plots should be used for goodness-of-fit / residual diagnostics, and to my

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]]

A student of mine wants to use R to do some nonmetric multidimensional scaling. According to the R FAQ, there's a package called pcurve that computes multidimensional scaling solutions, but I was not able to locate it the contrib page (I am a Windows user with R version 1.4.1). Can anyone tell me whether it is possible to do nonmetric multidimensional scaling with R, and if so, how? John

I sent a query to R-Help about the availability of nonmetric multidimensional scaling (MDS) algorithms in R. I would like to thank Tony Rossini, Jonathan Baron, Sundar Dorai-Raj, and Brian Ripley for helpful replies. The gist of the replies is that isoMDS in the MASS library provides Kruskal's method for nonmetric MDS, sammon in the MASS library provides Sammon's nonlinear mapping method

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Dear R-Help, I'm using R version 1.6.0 on a Windows computer. I am trying to create a function that, among other things, constructs strings that refer to Windows files, e.g., I might want to construct a string like 'c:\work\part1.txt'. I have found that the following does not work. > paste("c:", "\", "work", "\", "part1.txt",

Dear Help, Suppose I have a character vector. x <- c("Bob", "loves", "Sally") I want to combine it into a single string: "Bob loves Sally" . paste(x) yields: paste(x) [1] "Bob" "loves" "Sally" The following function combines the character vector into a string in the way that I want, but it seems somewhat inelegant.

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)

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]]

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