similar to: proposal: allowing alternative variance estimators in glm/lm

Hi, I would like to estimate coefficients using poisson regression and then get standard errors that are adjusted for heteroskedasticity, using a complex sample survey data. Then I will calculate prevalence ratio and confidence intervals. Can sandwich estimator of variance be used when observations aren?t independent? In my case, observations are independent across groups (clusters), but

Hi, I'm using maxlik with functions specified (L, his gradient & hessian). Now I would like determine some robust standard errors of my estimators. So I 'm try to use vcovHC, or hccm or robcov for example but in use one of them with my result of maxlik, I've a the following error message : Erreur dans terms.default(object) : no terms component Is there some attributes

When using function heckit() from package ‘sampleSelection’, is there anyway to make t-tests for the coefficients using robust covariance matrix estimator? By “robust” I mean something like if a had an object ‘lm’ called “reg” and then used: > coeftest(reg, vcov = vcovHC(reg)). I’m asking this because in Stata we could use function heckman and then use vce option “robust”. We could do the

Dear R-Users: I am trying to find the robust (or sandwich) estimates of the standard error of fixed effects parameter estimates using the package "lmer2". In model-1, I used "robust=TRUE" on the other, in model-2, I used "robust=FALSE". Both models giving me the same estimates. So my question is, does the robust option works in lmer2 to get the robust estimates of

Hello, first of all: I'm new to R and have only used SPSS befor this (which can't do this at all...). I'm trying to output some regression results to latex. The regressions are normal OLS and I'm trying to output the results with standardized regression coefficients and t-statistics based on "Huber-White sandwich estimator for variance". The final result should be

Hello Is there any function to calculate White's standard errors in R in an NLS regression. The sandwich and car package do it but they need an lm object to calculate the error's. Does anyone have idea how to do it for an NLS object ? Regards The woods are lovely, dark and deep But I have promises to keep And miles before I go to sleep And miles before I go to sleep ----- [[alternative

Dear R users, I have a question here library(AER) library(plm) library(sandwich) ## take the following data data("Gasoline", package="plm") Gasoline$f.year=as.factor(Gasoline$year) Now I run the following regression rhs <- "-1 + f.year + lincomep+lrpmg+lcarpcap" m1<- lm(as.formula(paste("lgaspcar ~", rhs)), data=Gasoline) ###Now I want to find the

Dear All, I'm trying to use waldtest to test poolability (parameter stability) between two logistic regressions. Because I need to use robust standard errors (using sandwich), I cannot use anova. anova has no problems running the test, but waldtest does, indipendently of specifying vcov or not. waldtest does not appear to see that my models are nested. H0 in my case is the the vector of

Hi, I have ove the robust standard error of an estimator but I don't know how to do this. The code for my regression is the following: reg<-lm(fsn~lctot) But then what do I need to do? -- Charlène Lisa Cosandier [[alternative HTML version deleted]]

How might you fit a generalized linear model (glm) with variance = mu+theta*mu^2 (where mu = mean of the exponential family random variable and theta is a parameter to be estimated)? This appears in Table 2.7 of Fahrmeir and Tutz (2001) Multivariate Statisticial Modeling Based on Generalized Linear Models, 2nd ed. (Springer, p. 60), where they compare "log-linear model fits to

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

Ted Harding said: > I can get the estimated RRs from > RRs <- exp(summary(GLM)$coef[,1]) > but do not see how to implement confidence intervals based > on "robust error variances" using the output in GLM. Thanks for the link to the data. Here's my best guess. If you use the following approach, with the HC0 type of robust standard errors in the

Hi All, I have a dataset, longitudinal in nature, each row is a 'visit' to a clinic, which has numerous data fields and a count variable for the number of 'events' that occurred since the previous visit. ~50k rows, ~2k unique subjects so ~25 rows/visits per subject, some have 50 some have 3 or 4. In STATA there is an adjustment for the fact that you have multiple rows per

Hello, I would like to estimate a VAR model with HAC corrected standard errors. I tried to do this by using the sandwich package, for example: > library(vars) > data(Canada) > myvar = VAR(Canada, p = 2, type = "const") > coeftest(myvar, vcov = vcovHAC) Error in umat - res : non-conformable arrays Which suggests that this function is not compatible with the VAR command.

Hello, I am trying to extract an estimator for the bread of the sandwich function. I used bread(fitted model) however it seems that I have missed something as an error message "no applicable method for "bread" appears. My fitted model is a Spatial simultaneous autoregressive error model.(errorsarlm in spdep package) Can anyone please tell me what I might be doing wrong? Your

Hello, I am interested in running generalized estimating equation models in R. Currently there are two main packages for doing so in R, geepack and gee. I understand that even though one can obtain similar to almost identical results using either of the two, that there are differences between the packages. The paper that introduces the geepack package (

Hi! I need to run ols regressions with Huber-White sandwich estimators and the correponding standard errors, without an intercept. What I'm trying to do is create an ols object and then use the robcov() function, on the order of: f <- ols(depvar ~ ind1 + ind2, x=TRUE) robcov(f) However, when I go f <- ols(depvar ~ ind1 + ind2 -1, x=TRUE) I get the following error: Error in

Hi all, I am a real novice to R. :) I am struggling with a problem for generating colored dendrogram. I have searched the R list and complied/collected a R code which can generated a colored dendrogram based on the rainbow color and 4x4 similarity matrix (say matrix:m). In this dendrogram, each leaf is colored differently. But, I do not want the leaf colored on a random basis. I want to assign

Dear all, Sorry if this is too obvious. I am trying to fit my multiple regression model using lm() Before starting model simplification using step() I checked whether the model presented heteroscedasticity with ncv.test() from the CAR package. It presents it. I want to correct for it, I used hccm() from the CAR package as well and got the Heteroscedasticity-Corrected Covariance Matrix. I am not

Dear R users: I'm happy to announce the first CRAN release of the clubSandwich package: https://cran.r-project.org/web/packages/clubSandwich clubSandwich provides several variants of the cluster-robust variance estimator for ordinary and weighted least squares linear regression models, including the bias-reduced linearization estimator of Bell and McCaffrey (2002). The package includes