similar to: delta-beta's

R-users E-mail: r-help@r-project.org Hi! R-users. I am just wondering what the definition of "dffits" in R language is. Let me show you an simple example. function() { library(MASS) xx <- c(1,2,3,4,5) yy <- c(1,3,4,2,4) data1 <- data.frame(x=xx, y=yy) lm.out <- lm(y~., data=data1, x=T) lev1 <- lm.influence(lm.out)$hat sig1 <-

All - My question is a bit involved, so bear with me. I have some data that looks like: Lake LL LW 81 2.176091259 1.342422681 81 2.176091259 1.414973348 81 2.176091259 1.447158031 81 2.181843588 1.414973348 81 2.181843588 1.447158031 81 2.184691431 1.462397998 81 2.187520721 1.447158031 81 2.187520721 1.477121255 81 2.187520721 1.505149978 ... [truncated] I'm trying to: 1) fit a simple

Hi all I'm getting a NaN returned on using dffits, as explained below. To me, there seems no obvious (or non-obvious reason for that matter) reason why a NaN appears. Before I start digging further, can anyone see why dffits might be failing? Is there a problem with the data? Consider: # Load data dep <-

I am looking a risk factors for disease in cattle and am interested in modelling farm and sampling cluster as random effects (My outcome is positive or negative at the level of the farm). I am using R version 2.0.1 on a Mac and have identified glmmPQL as hopefully the correct function to use. I have run a couple of models using this but was hoping that you might be able to answer a few questions.

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:

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

A couple of clarifications for you. 1. I write mixed effects Cox models as exp(X beta + Z b), beta = fixed effects coefficients and b = random effects coefficients. I'm using notation that is common in linear mixed effects models (on purpose). About 2/3 of the papers use exp(X beta)* c, i.e., pull the random effects out of the exponent. Does it make a difference? Not much: b will be

Hello. I want to get the proper influence function for the glm coefficients in R. This is supposed to be inv(information)*(y-yhat)*x. So I am wondering what is the exact mathematical formula for the output that the functions: dfbeta() OR lm.influence()$coefficients return for a glm model. I am confused because: 1. Their columns don't sum to zero as influences should. 2. They

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

Deducer 0.1 has been released to CRAN Deducer is designed to be a free, easy to use, alternative to proprietary software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and data analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is to two fold. 1. Provide an intuitive interface so that non-technical

Deducer 0.1 has been released to CRAN Deducer is designed to be a free, easy to use, alternative to proprietary software such as SPSS, JMP, and Minitab. It has a menu system to do common data manipulation and data analysis tasks, and an excel-like spreadsheet in which to view and edit data frames. The goal of the project is to two fold. 1. Provide an intuitive interface so that non-technical

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,

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,

Hi, I would like to see if a matching variable is an effect-modifier in a conditional logistic regression. Naturally, the matching variable can't enter directly in the model but as an interaction with terms that are in. However, I have problems in formulating the correct model the term that's already in the model is a factor. I am using treatment contrasts and the problem is that if I

Reviving this thread with a patch! And some comments inline. Please take a look and let me know what you think. - Hans On Wed, Apr 25, 2012 at 12:39 PM, Hans Wennborg <hans at chromium.org> wrote: > Hi all, > > I would like to hear your thoughts on adding support for extending the > IR to allow for explicitly selecting which model to use for > thread-local storage of a

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 +

Dear R - help, I am working on the Credit scorecard model. I am using the Logistic regression to arrive at the regression coefficients model. I want to use the Hosmer - Lemeshow test . In order to understand the use of R - language, I had referred the following URL       http://www.stat.sc.edu/~hitchcock/diseaseoutbreakRexample704.txt The related data 'diseaseoutbreak' is available