Zelig March 2013

zelig@lists.gking.harvard.edu

9 participants
10 discussions

Re: MatchIt Zelig and multiple imputation

by Kosuke Imai

Unfortunately, I don't think we have an automated procedure for everything. You would have to multiply impute the data, do matching on each imputed data set, and then combine it in zelig() using mi() function. But this does not require any programming. You can simply run the same matching procedure on each data set via matchit() and then feed the resulting multiple matched data sets into zelig(). Good luck, Kosuke Department of Politics Princeton University http://imai.princeton.edu On Sep 13, 2011, at 6:02 PM, Pingaul jb wrote: > Dear Professor, > I’m a post-doctoral student at Montreal University. I’m actually in Columbia, working and propensity scores with a colleague and using MatchIt and Zelig. First, congratulations for your packages that are very flexible. > > My question is about multiple imputation and propensity scores with these softwares. From what I understand, combining both approaches would include: > > 1/ Doing multiple imputation and testing which variables to include. > > 2/ Propensity score analysis on each imputed data set and pooling the overall balance to check if it is ok (or on each data set?). > > 3/ Calculation of the quantities of interest for each data set > > 4/ Pooling the quantities across data sets. > > I would like to know if there is a written syntax to perform the MatchIt analysis for all of the imputed data set without having to do it manually and check the overall balance. Also, in theory, the number of individuals retained after propensity score matching and the weights can be different for each imputed data set. So that we have to perform the final analysis on each one and then pool the data with a specific procedure to take into account the eventual varying Ns? I normally use Mice package for multiple imputation but it seems that Zelig handle Amelia. My colleague seems to do be able to do all that in stata, but I’m not sure how to make all the three R packages work together. > > I would be very happy if you could indicate to me a reference or a place where I can find the syntax to do that (I’ve been using R for some times so I can use packages easily but I have no programming skills). > > > Best Regards! > > > > Jean-Baptiste > - 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/

10 years, 8 months

logit.mixed error

by Yphtach Lelkes

Hi there, I am encountering this error when using sim after logit.mixed or probit.mixed: Error in qifunction(obj, x = x, x1 = x1, y = y, param = param, num = num) > : could not find function "setup.x.matrix" This is with "ZeligMixed". If I use ZeligMultilevel I get: Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), > : > attempt to set an attribute on NULL Toy example: d <- expand.grid(y=rbinom(10,1,.33),x=rnorm(10,1),t=1:4) z.out <- zelig(y~x+tag(1|t),data=d,model="logit.mixed") sim(z.out) thanks, Yph

10 years, 10 months

assistance with predictions for logit.mixed

by Karl Kosciuch

Hello - I was happy to find the Zelig package as I had been looking for code for implementing a generalized linear mixed model. I'm having some issues obtaining predicted probabilities from my rather simple logit.mixed regression. I am using logit.mixed with avoidance behavior as the binomial response variable, height as the predictor (ranging from 150 to 1500m), and year as a random effect (there are 3 years). The model runs fine, but I encounter an error starting with x.out. I've looked at the package documentation to try to solve the error, and have been unsuccessful. I'd like to produce one figure showing with the predicted probability on the y-axis and height on the x-axis. I'd like to show the regression line with the 95% CI for the predicted probabilities. I've been successful at coding this analysis and figure for a fixed effects logistic regression, but not so much for the mixed effects logistic regression. Thank you library(Zelig) library(ZeligMultilevel) avd$year <- factor(avd$year) z.out <- zelig(avoid ~ height + tag(1|year), data = avd, model = "logit.mixed") summary(z.out) x.out <-setx(z.out, fn = NULL) s.out <- sim(z.out, x = x.out) summary(s.out) Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type> summary(s.out)Error in summary(s.out) : error in evaluating the argument 'object' in selecting a method for function 'summary': Error: object 's.out' not found -- Karl Kosciuch kosciuch(a)gmail.com

10 years, 10 months

ameliabind and zelig

by One Hwang

Hi, I want to combine multiple amelia runs, and use the zelig function on the entire set of amelia outputs. However, I noticed that when I run zelig on the combined set, the coefficients don't look right. Here are the script and the R outputs: > require(Amelia) ## Amelia II: Multiple Imputation ## (Version 1.7, built: 2013-02-10) > library(Zelig) Attaching package: ‘zoo’ The following object(s) are masked from ‘package:base’: as.Date, as.Date.numeric ZELIG (Versions 4.1-3, built: 2013-01-30) > data(freetrade) > a.out1 <-amelia(freetrade, m = 1, ts = "year", cs = "country") -- Imputation 1 -- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 > a.out2 <-amelia(freetrade, m = 1, ts = "year", cs = "country") -- Imputation 1 -- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 > a.out3 <-ameliabind(a.out1, a.out2) # combine the amelia runs > summary(a.out3) Amelia output with 2 imputed datasets. Return code: 1 Message: Normal EM convergence Chain Lengths: -------------- Imputation 1: 14 Imputation 2: 15 Rows after Listwise Deletion: 96 Rows after Imputation: 171 Patterns of missingness in the data: 8 Fraction Missing for original variables: ----------------------------------------- Fraction Missing year 0.00000000 country 0.00000000 tariff 0.33918129 polity 0.01169591 pop 0.00000000 gdp.pc 0.00000000 intresmi 0.07602339 signed 0.01754386 fiveop 0.10526316 usheg 0.00000000 # run zelig on the first amelia run > z.out1 <- zelig(tariff ~ polity + pop + gdp.pc, data = a.out1$imputations, model = "ls", cite = FALSE) > summary(z.out1) Call: lm(formula = formula, weights = weights, model = F, data = data) Residuals: Min 1Q Median 3Q Max -29.686 -11.067 -3.367 10.003 45.556 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.317e+01 1.891e+00 17.538 < 2e-16 *** polity -1.060e-01 2.434e-01 -0.435 0.664 pop 2.925e-08 5.405e-09 5.411 2.15e-07 *** gdp.pc -2.738e-03 5.235e-04 -5.231 4.99e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 16.62 on 167 degrees of freedom Multiple R-squared: 0.3234, Adjusted R-squared: 0.3112 F-statistic: 26.6 on 3 and 167 DF, p-value: 4.07e-14 # run zelig on the second amelia run > z.out2 <- zelig(tariff ~ polity + pop + gdp.pc, data = a.out2$imputations, model = "ls", cite = FALSE) > summary(z.out2) Call: lm(formula = formula, weights = weights, model = F, data = data) Residuals: Min 1Q Median 3Q Max -40.13 -11.72 -3.63 10.16 43.77 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.400e+01 1.984e+00 17.133 < 2e-16 *** polity -6.123e-02 2.545e-01 -0.241 0.81 pop 3.289e-08 5.689e-09 5.781 3.56e-08 *** gdp.pc -3.016e-03 5.501e-04 -5.484 1.51e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 17.47 on 167 degrees of freedom Multiple R-squared: 0.3495, Adjusted R-squared: 0.3378 F-statistic: 29.91 on 3 and 167 DF, p-value: 1.574e-15 # run zelig on the combined amelia runs > z.out3 <- zelig(tariff ~ polity + pop + gdp.pc, data = a.out3$imputations, model = "ls", cite = FALSE) > summary(z.out3) Call: lm(formula = formula, weights = weights, model = F, data = data) Residuals: Min 1Q Median 3Q Max -29.686 -11.067 -3.367 10.003 45.556 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 3.317e+01 1.891e+00 17.538 < 2e-16 *** polity -1.060e-01 2.434e-01 -0.435 0.664 pop 2.925e-08 5.405e-09 5.411 2.15e-07 *** gdp.pc -2.738e-03 5.235e-04 -5.231 4.99e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 16.62 on 167 degrees of freedom Multiple R-squared: 0.3234, Adjusted R-squared: 0.3112 F-statistic: 26.6 on 3 and 167 DF, p-value: 4.07e-14 Can someone please tell me why summary(z.out3) is returning the exact same results as summary(z.out1)? How can I fix this problem so that the ameliabind output is treated the same as a direct output from the amelia function? Thanks.

10 years, 12 months

Re: [zelig] sim() problems with ordered logit: incorrect dimensions

by Honaker, James

Evann, I'm glad you worked that out. And of course, now that correction is on the list should it come up for others. However, those are also data issues (missingness, factors) that we should try to catch with more proactive internal checking in Zelig and softer, more informative error messages. Thanks for pointing that out, James. -- James Honaker, Senior Research Scientist //// Institute for Quantitative Social Science, Harvard University -----Original message----- From: "Smith, Evann" <egsmith(a)fas.harvard.edu> To: "zelig(a)lists.gking.harvard.edu" <zelig(a)lists.gking.harvard.edu> Sent: Fri, Mar 29, 2013 23:57:39 GMT+00:00 Subject: Re: [zelig] sim() problems with ordered logit: incorrect dimensions In case anyone is wondering, I figured out my problems. 1) I needed to set my dependent variable as a factor outside the zelig call. 2) It didn't like that I had missing data (which produced NAs in the setx values). Listwise deletion prior to running the model solved that problem. Cheers, Evann On Mar 29, 2013, at 6:25 PM, wrote: Hi, When running an ordered logit, I get the following error: d1t.x <- setx(d1t) d1t.s <- sim(d1t, x=d1t.x) Error in as.matrix(x)[, -1] : incorrect number of dimensions As best I can discern, this is because the coef(d1t) command pulls the coefficients from the model, but not the values of the intercepts. While vcov(d1t) includes the intercepts. Is there a fix or workaround for this? Or am I doing something else wrong? Cheers, Evann ==================================== Evann Smith Ph.D. Candidate Department of Government Harvard University egsmith(a)fas.harvard.edu<mailto:egsmith@fas.harvard.edu> ==================================== ==================================== Evann Smith Ph.D. Candidate Department of Government Harvard University egsmith(a)fas.harvard.edu<mailto:egsmith@fas.harvard.edu> ====================================

11 years, 1 month

Re: [zelig] sim() problems with ordered logit: incorrect dimensions

by Smith, Evann

In case anyone is wondering, I figured out my problems. 1) I needed to set my dependent variable as a factor outside the zelig call. 2) It didn't like that I had missing data (which produced NAs in the setx values). Listwise deletion prior to running the model solved that problem. Cheers, Evann On Mar 29, 2013, at 6:25 PM, wrote: Hi, When running an ordered logit, I get the following error: d1t.x <- setx(d1t) d1t.s <- sim(d1t, x=d1t.x) Error in as.matrix(x)[, -1] : incorrect number of dimensions As best I can discern, this is because the coef(d1t) command pulls the coefficients from the model, but not the values of the intercepts. While vcov(d1t) includes the intercepts. Is there a fix or workaround for this? Or am I doing something else wrong? Cheers, Evann ==================================== Evann Smith Ph.D. Candidate Department of Government Harvard University egsmith(a)fas.harvard.edu<mailto:egsmith@fas.harvard.edu> ==================================== ==================================== Evann Smith Ph.D. Candidate Department of Government Harvard University egsmith(a)fas.harvard.edu<mailto:egsmith@fas.harvard.edu> ====================================

11 years, 1 month

sim() problems with ordered logit: incorrect dimensions

by Smith, Evann

Hi, When running an ordered logit, I get the following error: > d1t.x <- setx(d1t) > d1t.s <- sim(d1t, x=d1t.x) Error in as.matrix(x)[, -1] : incorrect number of dimensions As best I can discern, this is because the coef(d1t) command pulls the coefficients from the model, but not the values of the intercepts. While vcov(d1t) includes the intercepts. Is there a fix or workaround for this? Or am I doing something else wrong? Cheers, Evann ==================================== Evann Smith Ph.D. Candidate Department of Government Harvard University egsmith(a)fas.harvard.edu ====================================

11 years, 1 month

plot.ci problems

by dustin tingley

Hi- I went to change some graphs I produced earlier with Zelig. I can still run summary on my zelig object, but when I run plot.ci I get > plot.ci(s.out1) NULL This was not a problem in the last version of Zelig I had (3.5ish, I think). Can someone help me? best, Dustin Dustin Tingley Government Department Harvard University http://scholar.harvard.edu/dtingley Director, Program on Experience Based Learning in the Social Sciences http://projects.iq.harvard.edu/peblss/ Contribute to the Research Reservoir: http://projects.iq.harvard.edu/undergradscholars/pages/research-resources

11 years, 1 month

degrees of freedom, t-statistic, and f-values of combined multiply imputed data

by One Hwang

Hi, I am a novice user of Zelig and Amelia, and installed version 4.1-3. I am puzzled on how I can obtain the degrees of freedom, t-statistic, and f-values of combined multiply imputed data using Amelia or Zelig. I created imputations for a dataset. Then, to understand how to combine imputations, I used code straight from the Amelia manual. When I loaded Zelig and used the zelig function, I got coefficients that appeared to be coefficients of individual imputations, and not those of the combined set. The code I used was: a.out <- amelia(freetrade, m = 5) z.out.imp <- zelig(tariff ~ polity + pop + gdp.pc + year + country, data = a.out$imputations, model = "ls") summary(z.out.imp) Furthermore, I have also tried this code: > b.out <-NULL > se.out <-NULL > for(i in 1:a.out$m){ + ols.out <- lm(tariff ~ polity + pop + gdp.pc, data = a.out$imputations[[i]]) + b.out <- rbind(b.out, ols.out$coef) + se.out <-rbind(se.out, coef(summary(ols.out))[,2]) + } > combined.results<-mi.meld(q=b.out, se = se.out) > combined.results The returned information reflects the combined values and the combined standard error. There seems to be no way to get the t-statistic, degrees of freedom, or f-values using mi.meld(). Without having to do a manual calculation or use Stata, how can I obtain the degrees of freedom, t-statistic, and f-values? Thanks.

11 years, 1 month

calculation of sim () confidence intervals

by Zsófia Ignácz

Dear Developers, I would be interested in the function you used for the sim() command to create the 95% confidence intervals for the predicted probabilities. (I have not found it in the documentation). I've managed to get the predicted probabilities with the /qi$ev, and then replicate the mean and sd, but I seem to be too ignorant to replicate the confidence intervals that the sim() command gives me. I originally thought it was a basic calculation used for any normal distribution, but that did not yield identical results. My code, that I tried was the following: GAPtrend <- zelig(as.factor(jp) ~ youngA+transgen+statshift+lostgen+male+educ+empl+inc+ind+egali, model="mlogit", data=isjp) transEMPL <- setx(GAPtrend, youngA=1, transgen=0, statshift=0,lostgen=0, male=1, educ=4, empl=1, inc=52, ind=0, egali=0) pp.transEMPL <- sim(GAPtrend, x = transEMPL) predicted<-pp.transEMPL$qi$ev ###Calculation of CI lower <- mean(predicted$Pr.Y.0.)-1.96*(sd(predicted$Pr.Y.0.)) upper <- mean(predicted$Pr.Y.0.)+1.96*(sd(predicted$Pr.Y.0.)) Thank you in advance for your help, Zsófia Ignácz -- Zsófia Ignácz PhD Student Humboldt Universität zu Berlin Berlin Graduate School of Social Sciences European PhD for Social-economic and Social Statistics Email: zsofia.ignacz(a)sowi.hu-berlin.de Research Associate Jacobs University Bremen Campus Ring 1 D-28759 Bremen Germany Phone: +49 421 200-3012 Email: z.ignacz(a)jacobs-university.de URL: http://jacobs-university.de/directory/zignacz

11 years, 1 month

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Zelig March 2013