similar to: Variance-covariance matrix from GLM

Hi, I am running a simple linear model with (say) 5 independent variables. Is there a simple way of getting the variance-covariance matrix of the coeffcient estimates? None of the values of the lm() seem to provide this. Thanks in advance, Ritwik Sinha rsinha@darwin.cwru.edu Grad Student Case Western Reserve University [[alternative HTML version deleted]]

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

Dear friends, After fitting a generalized linear models ,i hope to get the variance of alpha,variance of beta and their covariance, that is , the variance-covariance matrix/information of alpha and beta , suppose *B* is the object of GLMs, i use attributes(B) to look for the options ,but can't find it, anybody knows how to get it? > attributes(B) $names [1] "coefficients"

In "lm" command, we can use "vcov" option to get variance-covariance matrix. Does anyone know how to get variance-covariance matrix in nlrq? Thanks, Kate [[alternative HTML version deleted]]

Hallo! I want to understand / recalculate what is done to get the CI of the logistic regression evaluated with lrm. As far as I came back, my problem is the variance-covariance matrix fit$var of the fit (fit<-lrm(...), fit$var). Here what I found and where I stucked: ----------------- library(Design) # data D<-c(rep("a", 20), rep("b", 20)) V<-0.25*(1:40) V[1]<-25

R 2.2.0 windows XP How can I perform a regression analyses using a vector of means, a variance-covariance matrix? I looked at the help screen for lm and did not see any option for using the afore mentioned structures as input to lm. Thanks, John John Sorkin M.D., Ph.D. Chief, Biostatistics and Informatics Baltimore VA Medical Center GRECC, University of Maryland School of Medicine Claude D.

Hello,   I am running a loop to compare some residual deviances obtained from glm, with codes: ........   OUT<-NULL for (i in 1:10){  myglm<-glm(mat ~X1+X2+X3,family = binomial, data =myDATA) OUT<-c(OUT,myglm$deviance) }   .......   In the loop, X1, X2, and X3 chage with i. If X1, X2 and X3 are not highly correlated, OUT is a vector of 10 values.   The problem is:  if there there is

Dear All,   is there a function in R that would help me convert a covariance matrix built based on arithmetic returns to a covariance matrix from log-returns?   As an example of the means and covariance from arithmetic:   mu <-c(0.094,0.006,1.337,1.046,0.263) sigma

There has been recent discussion about alternatives to the model-based standard error estimators for lm. While some people like the sandwich estimator and others don't, it is clear that neither estimator dominates the other for any sane loss function. It is also worth noting that the sandwich estimator is the default for t.test(). I think it would be useful for models using other

Greetings, Cohen, Cohen, West, and Aiken 2003 (Applied Multiple Regression-Correlation Analysis for the Behavioral Sciences, Third Edition) on page 273 state the covariance matrix of the regression coefficients is provided by standard programs for multiple regression, including SAS, SPSS, and SYSTAT. How can it be calculated with R. Thank you very much. pbm Peter B. Mandeville cel:

Hi, R developers. when running mle inside a loop I found a nasty behavior. From time to time, my model had a degenerate minimum and the loop just crashed. I tracked it down to "vcov <- if (length(coef)) solve(oout$hessian)" line, being the hessian singular. Note that the minimum reached was good, it just did not make sense to calculate the covariance matrix as the inverse of a

Dear R-ers, I feel I am close, but can't get it quite right. Thanks a lot for your help! Dimitri # I have 2 data frames: x<-data.frame(a=c("aa","aa","ab","ab","ba","ba","bb","bb"),b=c(1:2,1:2,1:2,1:2),d=c(10,20,30,40,50,60,70,80))

Hello I am fitting a Cox PH model using the function coxph(). Does anyone know how to obtain the estimate of the covariance matrix under the null hypothesis. The function coxph.detail() does not seem to be useful for this purpose. Thanks, KD. [[alternative HTML version deleted]]

Dear R-users, I would like to compute a robust covariance matrix of two series of realizations of random variables: ###Begin Example### data <- cbind(rnorm(100), rnorm(100)) model <- lm(data ~ 1) vcov(model) library(sandwich) NeweyWest(model) #produces an error ###End Example### NeweyWest() produces an error but sandwich(), vcovHAC(), kernHAC, weave(),... do not produce any errors. It

Dear R community members I have been struggling on this simple question, but never get appropriate solution. So please help. # my data, though I have a large number of variables var1 <- rnorm(500, 10,4) var2 <- rnorm(500, 20, 8) var3 <- rnorm(500, 30, 18) var4 <- rnorm(500, 40, 20) datafr1 <- data.frame(var1, var2, var3, var4) # my unsuccessful codes nvar <- ncol(datafr1)

I have a question, I generated some data and tried survreg to analyze them, but here, I want to get the covariance matrix of the coefficients. I know how to get corr from survreg, say, summary(f,corr=T), but how to get covariance matrix of the coefficient ? can I put them in a matrix and output ? thanks below is what I did <mailto:r-help@stat.math.ethz.ch> n=100; beta=1.0

Hello R Users and Developers, I have a basic question about how R works. Over the past few years I have struggled when I try to generate a new data frame that I believe should contain numeric data in some columns and character data in others only to find everything converted to character data. Is there a general method to create data frames that contain the data in the desired format: numbers

x1<-c(5.548,4.896,1.964,3.586,3.824,3.111,3.607,3.557,2.989,18.053,3.773,1.253,2.094,2.726,1.758,5.011,2.455,0.913,0.890,2.468,4.168,4.810,34.319,1.531,1.481,2.239,4.204,3.463,1.727) y<-c(2.590,3.770,1.270,1.445,3.290,0.930,1.600,1.250,3.450,1.096,1.745,1.060,0.890,2.755,1.515,4.770,2.220,0.590,0.530,1.910,4.010,1.745,1.965,2.555,0.770,0.720,1.730,2.860,0.760)

I am new at using R and was wondering if someone can give me the exact syntax for getting the covariance matrix. I used logit to generate the equation. Specifically the syntax was: glm(formula = Status ~ A + B + C + D + E, family = binomial(link = logit), data = logit) Do you know the syntax to get the covariance matrix? Thanks! [[alternative HTML version deleted]]

Hello everyone., I am looking for a covariance function this not the first time I have this type of problem (to find which function does something). I try in google with "R cran covariance function" but usually this ends with different results that do not help me that much. Could you please try to advice me how to search for what function implements the functionality you want.