similar to: What PRECISELY is the dfbetas() or lm.influence()$coef ?

Hello. I am making a program that allows a user to enter a formula to be used in coxph() and then does some other stuff to it, and also allows the user to set other options within coxph(). What is the proper way to pass a formula to a function along with a ...? I have a function: func <- function(coxformula, data = parent.frame(),...) { # Cut to relevant part of function coxmod <-

Hi, I've looked around but can't find a clear answer to the difference for these two? Any help? Thanks! -- View this message in context: http://old.nabble.com/dfbetas-vs-dfbeta-tp26331704p26331704.html Sent from the R help mailing list archive at Nabble.com.

Hi there, I have the problem, that I'm not able to reproduce the SPSS residual statistics (dfbeta and cook's distance) with a simple binary logistic regression model obtained in R via the glm-function. I tried the following: fit <- glm(y ~ x1 + x2 + x3, data, family=binomial) cooks.distance(fit) dfbetas(fit) When i compare the returned values with the values that I get in SPSS,

Hello I am running a regression without the intercept, and want to compute dfbetas. How do I do this? The dfbetas function only works when the intercept is included in the model. Regards K [[alternative HTML version deleted]]

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

Hi, I am using coxph with weights to represent sampling fraction of subjects. Our simulation results show that the robust SE of beta systematically under-estimate the empirical SD of beta. Does anyone know how the robust SE are estimated in coxph using weights? Is there any analytical formula for the “weighted” robust SE? Any help is appreciated! Thanks so much in advance Willy

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

influence.measures gives several measures of influence for each observation (Cook's Distance, etc) and actually flags observations that it determines are influential by any of the measures. Looks good! But how does it discriminate between the influential and non- influential observations by each of the measures? Like does it do a Bonferroni-corrected t on the residuals identified by

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)

Am I asking for too much: for any object that a stat proc returns ( y <- lm( y~x) , etc ) ) , is there a super convenient function like give_all_extractors( y ) that lists all extractor functions , the datatype returned , and a text descriptor field ("pairwisepval" "lsmean" etc) That would just be so convenient. What are my options for querying an object so that I can

Dear all, After a thorough research, I still find myself unable to find a function that does linear regression of 2 vectors of data using the "total least squares", also called "orthogonal regression" (see : http://en.wikipedia.org/wiki/Total_least_squares) instead of the "ordinary least squares" method. Indeed, the "lm" function has a

Hi there I have created a multivariate logistic regression model looking at the presence/absence of disease on farms. I would like to plot the diagnostic plots recommended by Hosmer & Lemeshow to look particularly for any points of high influence. In order to do this I need to extract values for delta-beta. The function dfbeta gives a value for change in each coefficient but I am looking

hello all I am using the R package Zelig for some tobit regression with robust standard errors. I have got R version 2.9.2 (2009-08-24) and Zelig Version: 3.4-5 when i do demo(robust) It ends like this way data(coalition) > # Fit the model with robust standard error > user.prompt() Press <return> to continue: > z.out3 <- zelig(Surv(duration, ciep12) ~ polar + numst2 +

Hi Can anyone help with the technique of obtaining leverages from a conditional logistic regression model? The code lm.influence does not seem to work for this data. Thanks Jane Murray

Hi, Storer and Crowley (JASA 1985) presented an approach for approximating the changes in maximum partial-likelihood parameter estimates for the Cox model when a single observation is deleted. Is there an R implementation of this approach? Any help is greatly appreciated. Thanks. Best, Ravi. ---------------------------------------------------------------------------- ------- Ravi

Dear R-Users, A message to continue the discussions we had in March and June. I have read the documents written since then (http://www.r-project.org/doc/R-FDA.pdf ; http://user2007.org/program/presentations/harrell.pdf ; http://user2007.org/program/presentations/rossini.pdf ; http://user2007.org/program/presentations/soukup.pdf) which are very interesting and useful. I work in a CRO and we

Hi, P-values in Durbin-Watson test obtained through the use of functions available in packages "lmtest" and "car" are different. The difference is quite significant. function "dwtest" in "lmtest" is much faster than "burbinwatson" in "car". Actually, you can take a nap while the latter trying to calculated Durbin-Watson test. My question

dear:teacher i have a problem which about the polr()(package "MASS"), if the response must have 3 or more levels? and how to fit the polr() to 2 levels? thank you. turly yours [[alternative HTML version deleted]]