search for: robcov

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...

...rdinal 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 estimator using a cluster variable: model.robcov.lrm <- robcov(model.lrm, cluster=clust) How can I remove observations in the cluster variable clust that contain NAs in y, x1, and x2? Thanks, Christof

...standard errors by resampling whole clusters of observations with replacement rather than resampling individual observations. Is that right, and is there any more detailed documentation on the math behind this? Also, what is the difference between these two functions: bootcov(my.model, cluster.id) robcov(my.model, cluster.id) Thank you. -- View this message in context: http://www.nabble.com/Clustered-data-with-Design-package--bootcov%28%29-vs.-robcov%28%29-tp23016400p23016400.html Sent from the R help mailing list archive at Nabble.com.

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, respectively. > MyModel.boot <- bootcov(MyModel, B=1000, coef.reps=T) > MyModel.robcov <- robcov(MyModel) > anova(MyModel)...

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 ols(nareit ~ SnP500 + d3yrtr - 1) : length of dimnames [2] not equal to array extent same with +0 inst...

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

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 much! ____________________________________________________________ FREE 3D MARINE AQUA...

...t;RS")* *>system <- list(RS)* *>Reg2 <- systemfit("2SLS", system, labels, Inst, saveMemory=TRUE) * *>summary (Reg2)* Now I want to obtain the t-tests of the coefficients but applying the HCSE. I know two different ways to obtain them: First, applying the function robcov() from package Design, or Second, applying coeftest() and vcovHV() from packages lmtest and sandwich. The program lines are the following: *Program lines 2:* *>#### First option: using robcov() ####* *>library (Design)* *>options(scipen=20)* *>robcov(Reg1) ### I tried with Reg 2...

...placebo or treatment. I have fit a logistic regression model with Huber-White cluster sandwich covariance estimator. I have put in the model the variables treatment (trt),sex,age and a restricted cubic spline of time (days) to allow for non-linear treatment effects. I use the functions lrm and robcov from R Design library: h<-lrm(head ~ trt*rcs(days)+ age+ sex,x=T,y=T) h.rob<-robcov(h,id) I want to estimate treatment effect over time, then: k<-contrast(h.rob,list(day=1:240, trt=1), list(day=1:240, trt=0)) xYplot(Cbind(exp(Contrast), exp(Lower),exp( Upper))...

I am using the Design library and robcov to compute variance-covariance matrices for clustered data regression. Is there an easy way to compute the F-test (i.e. linear hypothesis) for clustered data regression ? Thanks in advance! Benn

...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 ~ post + t19961 + t19962 + t19963 + t19964 + t19971 + t19972 + t19973 + t19974 + t19981+factor( id))) library(Design) g.clust1 <- robcov(f, id) Error in match.arg(type) : 'arg' should be one of working, response, deviance, pearson, partial All my variables is vectors and I've tried with other variables inside and outside the model and all results in the same errormessage. Best regards Anders Eklund Stockholm, Sweden.

I am trying to compute the white heteroskedasticity-robust standard errors (also called the Huber standard errors) in a linear model, but I can't seem to find a function to do it. I know that the design library in S+ has something like this (robcov?), but I have not yet seen this library ported to R. Anyone know if there is already a function built into R to do this relatively simple job? Thanks, Grant ------------------------------------------------------ Grant Verdell Farnsworth gvf at email.byu.edu http://thegrantman.freewebsites.com --...

...) #### systemfit #### library (systemfit) RS <- LnP~Sc+Ag+Ag2+Var+R+D Inst <- ~I2+Ag+Ag2+Var+R+D labels <-list("RS") system <-list(RS) Reg2SLS <- systemfit("2SLS", system, labels, Inst, saveMemory=TRUE) summary (Reg2SLS) If I try to obtain the HCSE with robcov I obtain the following error message in both cases: Error in rep.default(1, p) : rep() incorrect type for second argument If I tried to apply vcovHAC or vcovHC in the systemfit output of 2SLS I receive the following messages of errors respectively: Error in estfun(x) : no applicable method...

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

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 to give to maxlik objet for "fitting" the call of vcovHC? Thank you [[alternative HTML...

...actions.containing,legend.nomabbrev,lm.pfit,lrm,lrtest,lsp,matinv,matrx,Newlabels,Newlevels,nomogram,num.intercepts,ols,ols.influence,oos.loglik,pantext,Penalty.matrix,Penalty.setup,pentrace,perimeter,perlcode,plot.xmean.ordinaly,pol,pphsm,predab.resample,Predict,psm,rcs,related.predictors,reVector,robcov,Rq,sascode,scored,sensuc,setPb,show.influence,specs,strat,Surv,"[.Surv",survdiffplot,survest,Survival,survplot,univarLR,validate,val.prob,val.probg,val.surv,vif,which.influence) importFrom(Hmisc) S3method(anova, rms) S3method(latex, anova.rms, bj, cph, Glm, Gls, lrm, naprint.delete, ols,...

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;

...ression with Huberized variance matrix. > I frequently have to run regressions w/ robust variances. In Stata, one > simply adds the word "robust" to the end of the command or > "cluster(cluster.variable)" for a cluster-robust error. In R, there are two > functions, robcov and hccm. I had to run tests to figure out what the > relationship is between them and between them and Stata (robcov w/o cluster > gives hccm's hc0; hccm's hc1 is equivalent to Stata's 'robust' w/o cluster; > etc.). This is rather clearly document on the respective m...

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 have a spatial data set with ordinal response variable containing four levels. I would like to know if and how spatial autocorrelation can be taken into account when ordinal logistic regression is used (e.g. the function lrm from the Design package). Thanks for your help! Christof