Zelig January 2011

zelig@lists.gking.harvard.edu

4 participants
4 discussions

summary with imputations bug ... any workarounds?

by Peter Flom

Good morning If I run <<< susan.lsmixed.out <- zelig(formula = unprot_vag_sex ~ married + age + TREATMENT.ARM*time + highest_grade + income + tag(1|id), data = susanMI.out$imputations, model = "ls.mixed") summary(susan.lsmixed.out) >>>> I get an error Error in x$coef : $ operator is invalid for atomic vectors Searching the archives, I see that others have had similar problems. Is there a workaround? summary(susan.lsmixed.out[[1]]) works fine; should I then average across the five imputed data sets? thanks! Peter Peter L. Flom, PhD Statistical Consultant Website: http://www DOT statisticalanalysisconsulting DOT com/ Writing; http://www.associatedcontent.com/user/582880/peter_flom.html Twitter: @peterflom - Zelig Mailing List, served by Harvard-MIT Data Center Send messages: zelig(a)lists.gking.harvard.edu [un]subscribe Options: http://lists.gking.harvard.edu/?info=zelig Zelig program information: http://gking.harvard.edu/zelig/

12 years, 1 month

mixed.probit, gamma specification

by Jason McMann

Hello, I am trying to run a multilevel probit model using Zelig, but keep receiving the following error message: " in .deparseTag(TT.vars[[vind]]) : wrong use of tag function!!" A simplified version of the model I am trying to run is: z.out <- zelig(formula= list(mu=investment.binary ~ edlevel + tag(1 + edlevel, gamma | country), gamma = ~ tag(GDPpc06.full| country)), data=data2006.mod1, model="probit.mixed") What I would like to do is allow the intercept and the edlevel variable listed within the first tag() to vary by country as a function of the GDPpc06.full variable, all of which are included in the same dataframe. I followed the syntax here - http://cran.r-project.org/web/packages/Zelig /vignettes/probit.mixed.pdf - but I think that I am incorrectly specifying the gamma part of the syntax, which may be causing the error. I *am* able to get the model to run when I allow the intercept and edlevel variable to vary using the following syntax: z.out <- zelig(investment.binary ~ edlevel + + tag(1 + edlevel | country), data=data2006.mod1, model="probit.mixed") However, this syntax does not allow me to specify that the intercept and edlevel variable should vary as a function of GDPpc06.full, as in the first model specified above. I have tried including multiple tags at the non-group level of the model specification - i.e. one for the intercept and one for the edlevel variable - but this does not seem to work either. Do you have any suggestions for how to fix the syntax? Sincerely, Jason -- Jason I. McMann PhD Student | Department of Politics Princeton University | jmcmann(a)princeton.edu

12 years, 5 months

gamma.mixed & weights

by Alicia Doyle Lynch

I am running a gamma.mixed model on data that requires the use of survey weights. Is there a way to account for weighting in the gamma.mixed command? Thank you, Alicia -- Alicia Doyle Lynch Statistical Trainer Harvard MIT Data Center 1737 Cambridge Street, K318 Cambridge MA 02138 - Zelig Mailing List, served by Harvard-MIT Data Center Send messages: zelig(a)lists.gking.harvard.edu [un]subscribe Options: http://lists.gking.harvard.edu/?info=zelig Zelig program information: http://gking.harvard.edu/zelig/

13 years, 3 months

"variable lengths differ (found for '(weights)')" error in Zelig library

by Sotiris Adamakis

Hi, I posted the following on the R discussion group, but this might be relevant here as well. I am trying 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). This is my source: http://imai.princeton.edu/research/files/matchit.pdf I encounter a slight problem when I apply the weights that are produced in the stage of preprocessing the data. The idea of this is to use the MatchIt software to preprocess the data and then use the Zelig software to generate the distribution of ATT. I believe that the main reason for preprocessing the data is to create weights (depending on the matching technique you use) so that balance would be achieved for the matching variables between the treatment and the control group. Then you use these weights in the regressions that follow in the Zelig library. Copied from the matchit article, whose link I provide above, the authors say: "If one chooses options that allow matching with replacement, or any solution that has different numbers of controls (or treateds) within each subclass or strata (such as full matching), then the parametric analysis following matching must accomodate these procedures, such as by using fixed effects or weights, as appropriate. (Similar procedures can also be used to estimate various other quantities of interest such as the average treatment effect by computing it for all observations, but then one must be aware that the quantity of interest may change during the matching procedure as some control units may be dropped.)" The following code is for the "lalonde" data set, where I get an error message in the end. I use the more recent R version (2.12.1) and have updated the Zelig and MatchIt libraries. > update.packages("MatchIt") > update.packages("Zelig") > library(Zelig) > library(MatchIt) > data(lalonde) > m.out1 = matchit(treat ~ age + educ + black + hispan + nodegree + married + re74 + re75, method = "subclass", subclass=6, data = lalonde) > z.out1 = zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75, data = match.data(m.out1, "control"), model = "ls", weights="weights") > x.out1 = setx(z.out1, data = match.data(m.out1, "treat"), cond = TRUE) > s.out1 = sim(z.out1, x = x.out1) Error in model.frame.default(formula = re78 ~ age + educ + black + hispan + : variable lengths differ (found for '(weights)') I was wondering if somebody could tell me how to get around with this problem? Also, I have seen people adding the propensity scores in the regression analysis applied in the Zelig package, i.e. > z.out1 = zelig(re78 ~ age + educ + black + hispan + nodegree + married + re74 + re75 + distance, data = match.data(m.out1, "control"), model = "ls", weights="weights") Does anyone have a clue of why this can happen? Kind regards, Sotiris

13 years, 3 months

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Zelig January 2011