search for: dfbetas

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.

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]]

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

...ave 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, they are different, although the same model is calculated (the coefficients are the same etc.) It seems that different calculation-formulas are used for cooks.distance and dfbetas in SPSS compared to R. Unfortunately...

...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 influence influence.glm influence.lm rstudent rstudent.glm rstudent.lm >x <- 1:10 >y <- 2*x+3+2*rnorm(10) >model<-lm(y~x) >qq.plot(model) Bus error ---End transcript--- No plot appears. After the bus error, R no longer operates normally, an...

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

...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, component+residual plots, ceres plots, Durbin-Watson statistics); some tools for graphing and exploring data (e.g., enhanced quantile-comparison plots -- with simulated envelopes for studentized residuals from linear models, scatterplot, and scatterplot-matrix functions);...

...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, component+residual plots, ceres plots, Durbin-Watson statistics); some tools for graphing and exploring data (e.g., enhanced quantile-comparison plots -- with simulated envelopes for studentized residuals from linear models, scatterplot, and scatterplot-matrix functions);...

...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, component+residual plots, ceres plots, Durbin-Watson statistics); some tools for graphing and exploring data (e.g., enhanced quantile-comparison plots -- with simulated envelopes for studentized residuals from linear models, scatterplot, and scatterplot-matrix functions);...

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

It's a bug in summary.aareg which no one found until now. What's wrong: If dfbeta=TRUE then there is a second estimate of variance calculated, labeled as test.var2. If maxtime is set, then both estimates of variance need to be recalculated by the summary routine. An incorrect if-then-else flow led it to look for test.var2 when it wasn't relevant. My test cases with maxtime also

On 06/05/2013 12:33 AM, r-help-request at r-project.org wrote: > Dear friends - I'm on windows 7, R 2.15.2 > > when I run the example for aareg in survival package I see this: > > plot(lfit[4], ylim=c(-4,4)) > error in xy.coords(x, y, xlabel, ylabel, log) : > 'x' is a list, but does not have components 'x' and 'y' > > Is that a matter

...Argument tcex= is the character size of the title # Argument cex= is the character size of the x and y axis labels # Argument id=T is for identification mode: left click on points # to have them identified, middle click when done. # Argument proportional=T plots points as circles proportional to DFBETAS # Argument relsize changes proportional circles relative size # Argument res=T plots residuals instead # Argument f=2/3, e.g. sets neighbor fraction for lowess line partreg_function(xlst, yname, outname, df, proportional=F, relsize=1, main=NULL, tcex=1, cex=1, id=F, ares=F, f=NULL) { jnk_par...

...UTPUT. > dat <- data.frame(x = rnorm(100), y = rnorm(100)) > lm1 <- lm(y ~ x, data = dat) > > methods(class = "lm") [1] add1.lm* alias.lm* anova.lm case.names.lm* [5] confint.lm* cooks.distance.lm* deviance.lm* dfbeta.lm* [9] dfbetas.lm* drop1.lm* dummy.coef.lm* effects.lm* [13] extractAIC.lm* family.lm* formula.lm* hatvalues.lm [17] influence.lm* kappa.lm labels.lm* logLik.lm* [21] model.frame.lm model.matrix.lm nobs.lm* plot.lm [25] predict.lm...

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 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

...veral functions are based on lm.influence function, and it seems that it returns elements sigma and coefficients that are only based on the first variable (first column of the residual matrix wt.res) and give wrong results for other variables. This will influence functions dfbeta.lm (coefficients), dfbetas.lm (coefficients, sigma), dffits (sigma), rstudent.lm (sigma) and covratio (sigma). lm.influence finds these elements in compiled code and this is harder to fix. MASS (the book & the package) avoid using compiled code in their (univariate) studentized residuals, and instead use a clever short-c...

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

...should I be extracting? Or, is this not an option for a clogit model? ## The default residuals of coxph in R are the martingale residuals. ## resid(fit1,type=c("martingale", "deviance", "score", "schoenfeld", ## "dfbeta", "dfbetas", "scaledsch","partial")) R code below shows equivalence between clogit and binomial GLM fit on the differences (note: these would not be equivalent if used a "cluster" argument in clogit), and GOF "test" for binomial GLM fit on the differences. I wou...

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