similar to: zelig logit survey

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 +

I am working on a new package, one in which the user needs to specify the role that different variables play in the analysis. Where I'm stumped is the best way to have users specify those roles. Approach #1: Separate formula for each special component First I thought to have users specify each formula separately, like: new.function(formula=y~X1+X2+X3, weights=~w,

Hello, I'm running R 2.14.1 on OS X (x86_64-apple-darwin9.8.0/x86_64 (64-bit)), with version 3.28 of Thomas Lumley's survey package. I was using predict() from svyglm(). E.g.: data(api) dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) out <- svyglm(sch.wide~ell+mobility, design=dstrat, family=quasibinomial()) pred.df <-

Hi R users, I'm using the survey package to calculate summary statistics for a large health survey (the Demographic and Health Survey for Honduras, 2006), and when I try to calculate the variances for several variables, I get negative numbers. I thought it may be my data, so I ran the example on the help page: data(api) ## one-stage cluster sample dclus1<-svydesign(id=~dnum, weights=~pw,

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

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)

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"),

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

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 +

Hello, all. I wanted to use the "survey" package to analyze data from the National Ambulatory Medical Care Survey, and am having some difficulty translating the analysis keywords from one package (Stata) to the other (R). The data were collected using a multistage probability sampling, and there are variables included to identify the sampling units and weights. Documentation from the

Howdy, Referencing the below exchange: https://stat.ethz.ch/pipermail/r-help/2006-April/103862.html I am still getting the same warning ("non-integer #successes in a binomial glm!") when using svyglm:::survey. Using the API data: library(survey) data(api) #stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

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-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 =

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

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,

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)

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