similar to: n in pglm() and relogit()

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]]

I am currently using the PGLM package and I would like to implement a within-model. Unfortunately, I do not succeed as I am not a big expert in panel regression. I am using the example data set from the PGLM package: library(pglm) data('Unions', package = 'pglm') anb <- pglm(union~wage+exper+rural, Unions, family=binomial('probit'), model="within", method =

I'm using the package pglm and I'have estimated a "random probit model". I need to save in a vector the fitted values and the residuals of the model but I can not do it. I tried with the command fitted.values using the following procedure without results: library(pglm) m1_S<-pglm(Feed ~ Cons_PC_1 + imp_gen_1 + LGDP_PC_1 + lnEI_1 +

I'm running a rare events logit model in R using the Zelig package and am getting some of the oddest error messages that I can't figure out. y = rnorm(100) x = c(rep("0",1), rep("1",99)) d = data.frame(won=x, bid=y) d mod1 <- zelig(y~x, model="relogit", data=d, tau=1/100, case.correct="prior", bias.correct=TRUE, robust=TRUE) > mod1 <-

I am a student who is doing empirical work for his thesis and trying to switch to R. I am familiar with Stata, and at the moment I am trying to replicate some of my previous work. I have a large unbalanced panel data set, observations for different countries between 1970 and 2007. My dependent variable is an overdispersed count. So far I have used fixed-effects negative binomial regression,

Dear Sir, When requesting the summary of pglm analyses in R, I encounter repeatedly the following warning message: "Warning message: In pf(Fstat, object$k - 1, object$n - object$k, ncp = 0, lower.tail = FALSE, : full precision was not achieved in 'pnbeta'" This message appears both with the pglm estimate of Lambda and with Lambda set to 0. Could you tell me what

Hi, my first email, I hope I don't violate any rules. I want to run a panel (cross-section-time-series) regression with fixed effects. I have count data (patent citations) so a Poisson distribution applies. But I also want to control for over-dispersion and and excess zeros. I think I read all manuals of potentially helpful packages but I could not find a way to realise this. The closest I got

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 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"),

Zelig: Everyone's Statistical Software Kosuke Imai, Gary King and Olivia Lau Version 1.0 (Available at http://gking.harvard.edu/zelig) A growing proportion of statisticians and methodologists from many disciplines are converging on R, a powerful statistics package and programming language. As an open source project, R is freely

Hello everyone! I am currently working with a time series panel data set measuring six dependent variables: 4 of which are binary and 2 of which are count data. I am interested in constructing a model to measure if the dependent variables influence one another. For example: DV1~ DV2 + IV1+IV2+ Controls and DV2~ DV1 + IV1+ IV2+ Controls (where IV stands for independent variable, not

Hi, I am starting a project in rails and I wish to change scaffolding. I''ve already done some changes like prepare all texts for locale. Now I am wishing to change the way it generates the _form partial. Could someone give some guideline so I can study such thing? I saw "template_for_inclusion" in form.rhtml but couldn''t find out how to change it. Being more

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),

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

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

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

I estimated a negative binomial model using zelig. z.out<- zelig(NEWBHC~ PW80 + CHNGBLK + XBLK,data=data, model="negbin") How do I calculate predicted probabilities for this model? Is it the same process as a poisson regression? Thanks in advance Joe [[alternative HTML version deleted]]

Hi, I'm having problems with the Zelig package - when using the below R displays the follwing message (I'm running R i386 2.15.3 for Windows and have updated all the Zelig packages): z.out<-zelig(Surv(psurv2, pcens2) ~ ren_sup3 + age, data=data_urgent, model="coxph") ** The model "coxph" is not available with the currently loaded packages, ** and is not an

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)

Hello, would someone please help explain the following inconsistency in the if/else statement to me? The format of the if/else #3 below is ok, but if/else #1 is not? (I get an "unexpected else" type error.) In order for it to work I have to use if/else #2 Thanks .. maybe there is some reason for this, but this looks very inconsistent to me. R version 2.10.1 (2009-12-14) Ubuntu 9.04