similar to: studentized and standarized residuals

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

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

Dear community, I'm having this silly problem. I've a linear model. After fixing it, I wanted to know which data had studentized residuals larger than 3, so i tried this: d1 <- cooks.distance(lmmodel) r <- sqrt(abs(rstandard(lmmodel))) rstu <- abs(rstudent(lmmodel)) a <- cbind( mydata, d1, r,rstu) alargerthan3 <- a[rstu >3, ] And suddenly a[rstu >3, ] has

Greetings to r-help land. I've run into some program crashes and I've traced them back to methods() behavior after the package gdata is loaded. I provide now a minimal re-producible example. This seems bugish to me. How about you? dat <- data.frame(x = rnorm(100), y = rnorm(100)) lm1 <- lm(y ~ x, data = dat) methods(class = "lm") ## OK so far library(gdata)

Full_Name: Heather Turner Version: 2.2.0 OS: Windows XP Submission from: (NULL) (137.205.240.44) Standardized residuals as calculated by rstandard.lm, rstandard.glm and plot.lm are Inf/NaN rather than zero when the un-standardized residuals are zero. This causes plot.lm to break when calculating 'ylim' for any of the plots of standardized residuals. Example:

I am working on a function that will remove outliers for regression analysis. I am stating that a data point is an outlier if its studentized residual is above or below 3 and -3, respectively. The code below is what i have thus far for the function x = c(1:20) y = c(1,3,4,2,5,6,18,8,10,8,11,13,14,14,15,85,17,19,19,20) data1 = data.frame(x,y) rm.outliers =

Is there any reason that rstandard.glm doesn't have a "pearson" option? And if not, can it be added? Background: I'm currently teaching an undergrad/grad-service course from Agresti's "Introduction to Categorical Data Analysis (2nd edn)" and deviance residuals are not used in the text. For now I'll just provide the students with a simple function to use, but I

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 all, the application of the function rstandard() in the base package to a glm object does not produce residuals standardized to have variance one: the reason is that the deviance residuals are divided by the dispersion estimate and not by the square root of the estimate for the dispersion. Should the function not be changed to produce residuals with a variance about 1? R 1.8.1 on

I had a look at some influence measures, and it seems to me that currently several methods handle multiple lm (mlm) objects wrongly in R. In some cases there are separate "mlm" methods, but usually "mlm" objects are handled by the same methods as univariate "lm" methods, and in some cases this fails. There are two general patterns of problems in influence measures:

Sorry, I am sure, this must be documented somewhere (but there are that many docs and tutorials to scan for topics..., actually a great thing... but if you are in a hurry..): I want to save a plot as .ps (or .eps): > postscript("plot1.eps", horizontal=FALSE, onefile=FALSE,height=8,width=8,pointsize=10) > plot(hpfit$fit,rstudent(hpfit),xlab="Fitted

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson",

Dear list members, I want to save residuals above or less than a certain value to an R object. I have performed a multiple linear regression, and now I want to find out which cases have a residual of above + 2.5 and ? 2.5. Below I provide the R commands I have used. Reg<-lm(a~b+c+d+e+f) # perform multiple regression with a as the dependent variable. Residuals<-residuals(reg) # store

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 am using the Unix version of R (version 1.7.0), installed via fink on a G4 Macintosh. I recently upgraded from version 1.6.0 and found that the "car" library now has a problem: ---Begin transcript--- >library(car) Attaching package 'car': The following object(s) are masked from package:base : dfbeta dfbeta.lm dfbetas dfbetas.lm hatvalues hatvalues.lm

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 <-

Inline below: 2011/4/21 Jeremy Miles <jeremy.miles at gmail.com> > > Just because it comes from a book does not make it true or correct. Amen! > Books are subject to considerably less peer review than journal > articles. Yes, but ... Peer review among journals is uneven, especially for those from private for-profit publishers. And even for top flight journals, dealing with

I am embarrassed to have to ask this but can anyone tell me of a Tukey multiple comparisons procedure available for R? I have looked through the search page, through the FAQ, and in the index of V&R (1999), and I still can't find such a thing. I see there is a ptukey function and a qtukey function but that is as far as I got. Do I need to roll my own? -- Douglas Bates

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

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