similar to: {clarify}: Simulation-Based Inference for Regression Models

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

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-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 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-group, Could somebody recommend a package that can deal with a multinomial response variable (choice of breeding tactic in mice, which has four unordered levels), multiply-imputed data (generated using the Amelia package) and two non-nested random effects: individual identity (133 individuals made up to four choices each) and year (for which there are six levels and sample size varies

Hi I have used the amelia command from the Amelia R package. this gives me a number of imputed datasets. This may be a silly question, but i am not a statistician, but I am not sure how to combine these results to obtain the imputed dataset to usse for further statistical analysis. I have looked through the amelia and zelig manuals but still can not find the answer. This maybe because I dont

Hello: I am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

Hi, all, I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the

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

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

install.packages works for Linux because the directory structure is not version dependent, as it is for the Windows download. That should be fixed as soon as my sys admin consents to install R 1.9.0 so we can do the cross compile. Of course, you are right, Martin and Peter, that Zelig should be on CRAN proper. We just need to write a few .Rd files to be compliant with R check, and change our

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"),

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 +

On the topic of when to use "suggests" and "depends" and on R version requirements. I have cc'd this message to R-devel because I am curious about what senior developpers think about these issues. The problem arises because we are using some functions from the package "matchit" in a new version of our package "portfolio". We are listing the matchit in

Hello, I am writing as an administrator, not as an R user, so forgive me if I am not completely knowledgeable about R. I have a user who is creating an R package for windows from a Linux environment using the crossbuild environment by Jun Yan and A.J. Rossini. The packages she generated worked fine until she tried to install in R 1.9.1 for Windows. Now when she installs with

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 +

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