similar to: F test for clustered data regression ?

Hi, I am trying to figure out exactly what the bootcov() function in the Design package is doing within the context of clustered data. From reading the documentation/source code it appears that using bootcov() with the cluster argument constructs standard errors by resampling whole clusters of observations with replacement rather than resampling individual observations. Is that right, and is

Please help, I have a strange problem. I've got a balanced panel data set. I use dummy variable regression and I've got results with lm function. summary(lm(y ~ post + t19961 + t19962 + t19963 + t19964 + t19971 + t19972 + t19973 + t19974 + t19981+factor( id))) The problem is that I would like to get my standard errors clustered but then gets the following error message: f<-(lm(y ~

I can't seem to find the right set of commands to enable me to do perform a regression with cluster-adjusted standard-errors. There seems to be nothing in the archives about this -- so this thread could help generate some useful content. I've searched everywhere. Can anyone point me to the right set of commands? An example would be most helpful as well. Bo [[alternative HTML version

Dear list, I made a logistic regression model (MyModel) using lrm and penalization by pentrace for data of 104 patients, which consists of 5 explanatory variables and one binary outcome (poor/good). Then, I found bootcov and robcov function in rms package for calculation of confidence range of coefficients and odds ratio by bootstrap covariance matrix and Huber-White sandwich method,

If I wanted to fit a logit model and account for clustering of observations, I would do something like: library(Design) f <- lrm(Y1 ~ X1 + X2, x=TRUE, y=TRUE, data=d) g <- robcov(f, d$st.year) What would I do if I wanted to do the same thing with a probit model? ?robcov says the input model must come from the Design package, but the Design package appears not to do probit? Thanks very

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

I am cleaning up the rms package to not export functions not to be called directly by users. rms uses generic functions defined in other packages. For example there is a latex method in the Hmisc package, and rms has a latex method for objects of class "anova.rms" so there are anova.rms and latex.anova.rms functions in rms. I use:

Hi R-helpers I have a dataframe DF (lets say with the variables, y, x1, x2, x3, ..., clust) containing relatively many NAs. When I fit an ordinal regression model with the function lrm from the Design library: model.lrm <- lrm(y ~ x1 + x2, data=DF, x=TRUE, y=TRUE) it will by default delete missing values in the variables y, x1, x2. Based on model.lrm, I want to apply the robust covariance

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

On Wed, 4 Jan 2006, Peter Muhlberger wrote: One comment in advance: please use a more meaningful subject. I would have missed this mail if a colleague hadn't pointed me to it. > I'm someone who from time to time comes to R to do applied stats for social > science research. [snip] > I would also prefer not to have to work through a > couple books on R or S+ to learn how to

Using hccm() I got a heteroscedasticity correction factor on the diagonal of the return matrix, but I don't know how to incorporate this into my linear model: METHOD 1: > OLS1 <- lm(formula=uer92~uer+low2+mlo+spec+degree+hit) Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.0623377 0.0323461 -1.927 0.057217 . uer 0.2274742 0.0758720

I am trying to extract significance levels out of a robcov+ols call. For background: I am analysing data where multiple measurements(2 per topic) were taken from individuals(36) on their emotional reaction (dependent variable) to various topics (3 topics). Because I have several emotions and a rotation to do on the topics, I'd like to have the results pumped into a nice table.

Hello, I'm an MD working in an eye clinic. I'm learning by myself to use R for use in my research works and for implementation in a software project. There are some authors who recomends the use of Cox regression as a substitute for Logistic regression (<a href="http://www.biomedcentral.com/1471-2288/3/21.pdf"> Barros AJD, Hirakata VN. BMCMedical Research Methodology, 2003;

Hello, Does R have any capabilities (or are there any add on packages) which can do estimation of standard statistical models (means, regression, logistic regression, etc) which take into account not only weights (e.g. post-stratification weights) but also the sample design, such as stratification and clustering information (to compute a robust taylor linearized variance estimator, for

I am trying to get robust standard errors in a logistic regression. Is there any way to do it, either in car or in MASS? Thanks for the help, Celso [[alternative HTML version deleted]]

Dear all, my aim is to estimate the efficacy over time of a treatment for headache prevention. Data consist of long sequences of repeated binary outcomes (1 if the subject has at least 1 episode of headache , 0 otherwise) on subjects randomized to placebo or treatment. I have fit a logistic regression model with Huber-White cluster sandwich covariance estimator. I have put in the model the

Dear All, I'm analysing a negative binomial dataset from a population-based study. Many covariates were determined on household level, so all members of a household have the same value for those covariates. In STATA, there seems to be an option for 'clustered analysis' for neg-bin regression. Does an equivalent exist for R(MASS)'s glm.nb or a comparable function? Many thanks for

Readers of this list might be interested in the following commenta about R. In a recent report, by Michael N. Mitchell http://www.ats.ucla.edu/stat/technicalreports/ says about R: "Perhaps the most notable exception to this discussion is R, a language for statistical computing and graphics. R is free to download under the terms of the GNU General Public License (see http://www.r-project.

Hallo, Errormessage in log, but it delivers correct Sep 23 21:30:40 serv postfix/pickup[15974]: 8807F4E127: uid=0 from=<root> Sep 23 21:30:40 serv postfix/cleanup[16049]: 8807F4E127: message-id=<20100923193040.8807F4E127 at serv.chefe.dyndns.org> Sep 23 21:30:40 serv postfix/qmgr[15975]: 8807F4E127: from=<root at chefe.dyndns.org>, size=451, nrcpt=1 (queue active) Sep 23

Dear all, I would like to know if it possible to use the the robcov()-command in the Design- package in order to obtain a robust variance-estimate that adjusts for within-cluster correlation. Does the ids-option in the survey-package the same job? TIA, Bernd