similar to: Zelig method setx()

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

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"),

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

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

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 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)

Is anybody working on a way to standardize the creation of "newdata" objects for predict methods? When using predict, I find it difficult/tedious to create newdata data frames when there are many variables. It is necessary to set all variables at the mean/mode/median, and then for some variables of interest, one has to insert values for which predictions are desired. I was at a

Dear Clara, Thanks for the reply. I am forwarding your message to the list, ok. When I wrote was a way of get further information to help the helpers. happy holidays, milton ---------- Forwarded message ---------- From: Clara Brück <clara_brueck@web.de> Date: 2009/12/30 Subject: Re: [R] Negbin Error Warnings To: milton ruser <milton.ruser@gmail.com> Dear Milton, Thanks for

I ran three logit models in R with the Zelig package and I'm trying to compute the predicted probabilities for a number of different values on the independent variable. My dep variable was accepted or decline and my indep variable is bid amount, and varies. So for a bid amount of 3, what's the expected probability of winning. For a bid amount of 5, what's the expected probability of

Hi, I ran a negative binomial regression (NBR) using the Zelig-package and the negbin model. When I then try to use the simumlation approach using the setx () and sim() functions to calculate expected values and first difference for different levels of one of my independent variables, I get 50 errors warnings, telling me that the calculation rpois produced NAs. However, the data I use

I'm trying to calculate predicted probabilities in R with Zelig and keep getting the following error. Can anyone help? > x.low <- setx(mod, type=1)Error in dta[complete.cases(mf), names(dta) %in% vars, drop = FALSE] : incorrect number of dimensions When I ran the model, I ran everything but the explanatory variable as a numeric variable. Now, I'm trying everything and no

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 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 +

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

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),

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 =

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,