similar to: Zelig package: Coxph model problems

How do I apply clustered standard errors (by country) when using "relogit" in Zelig? I can not find a function covering this. [[alternative HTML version deleted]]

Hi! Does somebody out there has experience with the Zelig package from Harvard uni? I have a problem when trying to set the explanatory variables with setx Polytomous logistic regression: >z.out <- zelig(OPARS ~ v1+v2+v3+...+vn, model = "mlogit", data=heb) that's OK >x.out<-set(z.out) Error in seq.Date(along = object) : `from' must be specified #??? I have no date

I'm running an ordered logit in R with the Zelig package and am trying to calculate some predicted probabilities. However, I get the following error message. > x.low <- setx(mod, cars=1)Error in dta[complete.cases(mf), names(dta) %in% vars, drop = FALSE] : incorrect number of dimensions I googled this problem and couldn't find anything, minus a question by me on this same

Hello, I am attempting to "automate" a Bayesian normal linear regression using Zelig. Basically, I have a list containing several zelig() objects, each having a different formula, same data set, and same model (normal.bayes). My problem lies in the setx() method, where I am setting a numeric parameter to a value other than the mean. This is straightforward if I input the parameters

Hello, I'm getting an error message when I use the ordered probit model "oprobit" in the zelig function. Using the same form as in the help file, we get an error message. It produces coefficients, but no standard errors. See results below. Any hints? Thanks! > > o.probit <- zelig(as.factor(checks.change) ~ Oda + Oil + sh_neg + sh_pos + checks1,

Hi there, I'm wondering what Zelig in the following situation (code below) actually does. Is this considered as a so called regression adjustment after the propensity score matching? library(MatchIt) library(Zelig) data(lalonde) re78 represents the outcome variable 1. With Zelig m.out <- matchit(treat ~ age + educ + black + hispan + married + nodegree + re74 + re75, data = lalonde)

Hey, I want to estimate to regressions. First I want to estimate simple OLS. Since my dependent variable looks like a gamma distribution I want to estimate a gamme regression with the "zelig" package. But, I have some problems with the interpretation of the estimated coefficient. In the OLS the estimated coefficient is negative. In the gamma regression the estimated coefficient is

Dear R-friends, I am trying to use the Zelig library to simulate the following model: z.out<-zelig(formula = y ~ x1*x2 + x3 + x4 + x5, model="ls", data = my.data, weights = my.weights) Since the regression is weighted before running the regression I used the procedure: zelig2ls <- function(formula, model, data, M, ...) { mf <- match.call(expand.dots = TRUE)

Dear all, Does anybody know why the following code returns an error message? >library(MatchIt) >library(Zelig) >data(lalonde) > >m.out1<-matchit(treat~age+educ+black+hispan+nodegree+married +re74+re75, method="full", data=lalonde) > >z.out1<-zelig(re78~age+educ+black+hispan+nodegree+married+re74+re75, data=match.data(m.out1, "control"),

When I use zelig to compute parameter estimates with "model = logit.survey", I receive the following error: Nicholas Carnes. 2007. "logt.surveyWarning message: In eval(expr, envir, enclose) : non-integer #successes in a binomial glm! I believe this is because the model is not using "quasibinomial" (see R-News 2003, Analyzing Survey Data in R, by Thomas Lumley). Is there

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 +

I'm running a multinomial logit in R using the Zelig package. However I get the following error. HELP anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 + partisan962 + employment962 + union962 + home962 + market962 + race962 + income962, model="mlogit", data=data96) #Error in attr(tt, "depFactors")$depFactorVar : # $ operator is invalid

I've run an ordered logistic regression model in R with Zelig and am looking to calculate predicted probabilities. Zelig has a series of simple one line commands to generate the information I want on first differences and so forth. Unfortunately, I keep getting an error when running the zelig function and was wondering if there was a quick alternative for generating predicted probabilities for

Hi everyone, I have carried out a multiple imputation in R using Amelia II and have created 5 multiply imputed datasets. The purpose of my research is to fit a Poisson Model to the data to estimate numbers of hospital admissions. Now that I have 5 completed datasets and I have to pool all the 5 datasets to get one combined output for a poisson model. I have checked previous queries about

Dear R users, I am trying to estimate to estimate the average treatmen effect on the treated (ATT) using first the MatchIt software to weight the data set and, after this, the Zelig software as shown in Ho et al. (2007). See here for an explanation of how to apply this technique in R: http://imai.princeton.edu/research/files/matchit.pdf I encounter a slight problem when I apply the weights that

Dear R-user: After the fitting the Tobit model using zelig, if I use the following command then I can get the regression coefficents: beta=coefficients(il6.out) > beta (Intercept) apache 4.7826 0.9655 How may I extract the "Naive SE" from the following output please? > summary(il6w.out) Call: zelig(formula = il6.data$il6 ~ il6.data$apache, model =

I'm running a multinomial logit in R using the Zelig packages. According to str(trade962a), my dependent variable is a factor with three levels. When I run the multinomial logit I get an error message. However, when I run 'model=logit' it works fine. any ideas on whats wrong? ## MULTINOMIAL LOGIT anes96two <- zelig(trade962a ~ age962 + education962 + personal962 + economy962 +

Hi Folks, According to the PDF file blogit.pdf in the Zelig documentation: "Use the bivariate logistic regression model ["blogit"] if you have two binary dependent variables (Y1,Y2), and and wish to model them jointly as a function of some explanatory variables. Each pair of dependent variables (Yi1,Yi2) has four potential outcomes, (Yi1=1,Yi2=1), (Yi1=1,Yi2=0),