Zelig November 2010

zelig@lists.gking.harvard.edu

5 participants
5 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, 2 months

factor analysis question

by Laura Selkirk

Hi everyone, I'm trying to run a factor analysis with imputed data in Zelig. Here's my code: >z.out <- zelig(cbind(simp3, Cert2, Source9, Source7, simp10,Cert10, Just19, Cert8, Source22, Cert11,simp19, Cert1, Just12, Cert5, simp9, simp20, Cert3, source5, simp13, simp12, simp1, source17, cert14, cert15, Just17, source21, Just14, simp15, simp18, Just22, Just25, Just20, source19, source10, Cert4, source4, Cert9, simp2, simp17, simp7, just24, just8, Cert13, just16, source11, cert17, Cert18, simp8, just21, source18, cert12, source16, simp11, source6, just1, just6, just26, source13, just15, Source1, Cert7, simp14, source8, source15, just3, source12, just23, source14, just11, simp16, just9, just10, source2, Cert6, just13, just5, Cert19, just7, just18, simp4, simp6, just2, just4, cert16, just27, simp5, source20, cert20), factors=5, model= "factor.bayes", data = mi(outdata1, outdata2, outdata3, outdata4, outdata5), cite=F) The problem is that when I try to run this code I get the following error message: "Error in cbind(simp3, Cert2, Source9, Source7, simp10, Cert10, Just19, : object 'simp3' not found" If I remove variable simp3 it just continues down the list of variables. I checked and R is reading my data correctly and I did import the data with "header=TRUE" just to make sure the variable names would be read. I've looked through the manual, but can't find an explanation for the error. Any help would be greatly appreciated. Laura - 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, 5 months

Use Zelig with lme4a

by Shige Song

Dear All, I am using zelig for estimating and simulating a mixed effect logit model. The lme4 package, which Zelig used as the default, yield a false convergence, while the experimental lme4a package yields convergence and reasonable estimates. My question is: is it possible to modify Zelig slightly to use lme4a instead of lme4 as the underlying estimation engine for generalized linear mixed effect model for this particular case? Many thanks. Best, Shige - 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, 5 months

RE: Calculating conditional ATT using regression ran over full sa mple not just controls

by Stephen Kay

Sorted it - just needed to insert command: k.x.out$nasal_ocular[k.x.out$nasal_ocular==1] <- 0 after setx. Thought I'd post, in case of use to someone else (or more likely myself in a years time). > -----Original Message----- > From: Stephen Kay > Sent: 11 November 2010 14:31 > To: 'zelig(a)lists.gking.harvard.edu' > Subject: Calculating conditional ATT using regression ran over full > sample not just controls > > Hi, > > I've trying to utilise Matchit and zelig together to produce a > conditional ATT quantity. Published examples show how to do this when the > parametric model coefficient estimates are obtained from zelig from > running the model just over the control units (with suitable weights > obtained from Matchit). My control sample is really too small to > accurately estimate these coefficients. My adapted code is below, which > runs without errors, but produces an estimate that (I think) are too far > near zero and statistically insignificant. It's certainly much smaller > than the relevant highly statistically significant treatment coefficient > in the parametric model. > > I've a feeling the actual ATT estimate I'm producing is the average > difference between the actual dependent outcome value and its predicted > value in the treated group. Possibly the "sim" command doesn't realise the > treatment/control binary indicator needs to be set to 0 in the data matrix > input from the "setx" output to estimate the counterfactual (not sure from > reading help file)? Any help is much appreciated - especially suggested > code changes. Many thanks from an R and zelig novice. > > In code below "ocular_nasal" is my treatment/control binary indicator > variable with 1 = treatment; and 0 = control. > > k.match.out<- matchit(nasal_ocular ~ m_age + m_bmi + smoke, data = k.gsk, > distance = "mahalanobis", method = "genetic", discard = "both", ratio = > 1,pop.size=400, int.seed=3313) > > k.z.out <- zelig(RQ_Act ~ nasal_ocular + m_age + m_bmi + smoke, model = > "ls", data = match.data(k.match.out)) > > k.x.out <- setx(k.z.out,fn = NULL, data = match.data(k.match.out, > "treat"), cond = TRUE) > set.seed(3251) > k.s.out <- sim(k.z.out, x=k.x.out, num=1000) > summary(k.s.out) > > Many thanks, > Stephen > Stephen Kay ¦ Director of Statistics > for Adelphi Real World (Manchester) > Adelphi Mill, Bollington, Cheshire SK10 5JB UK > > > Web: http://www.adelphigroup.com/companies/company_real_world.asp > <http://www.adelphigroup.com/companies/company_real_world.asp> > > Adelphi retains ownership of all DSP data and fieldwork materials. In > accordance with the company's Terms and Conditions, written prior approval > must be obtained with regard to any use of the data in any items submitted > for publication or for use in marketing materials. > > Please consider the environment before printing this e-mail > > > DISCLAIMER: The information in this message is confidential and may be legally privileged. It is intended solely for the addressee. Access to this message by anyone else is unauthorised. If you are not the intended recipient, any disclosure, copying, or distribution of the message, or any action or omission taken by you in reliance on it, is prohibited and may be unlawful. Please immediately contact the sender if you have received this message in error. Thank you.

13 years, 5 months

Calculating conditional ATT using regression ran over full sample not just controls

by Stephen Kay

Hi, I've trying to utilise Matchit and zelig together to produce a conditional ATT quantity. Published examples show how to do this when the parametric model coefficient estimates are obtained from zelig from running the model just over the control units (with suitable weights obtained from Matchit). My control sample is really too small to accurately estimate these coefficients. My adapted code is below, which runs without errors, but produces an estimate that (I think) are too far near zero and statistically insignificant. It's certainly much smaller than the relevant highly statistically significant treatment coefficient in the parametric model. I've a feeling the actual ATT estimate I'm producing is the average difference between the actual dependent outcome value and its predicted value in the treated group. Possibly the "sim" command doesn't realise the treatment/control binary indicator needs to be set to 0 in the data matrix input from the "setx" output to estimate the counterfactual (not sure from reading help file)? Any help is much appreciated - especially suggested code changes. Many thanks from an R and zelig novice. In code below "ocular_nasal" is my treatment/control binary indicator variable with 1 = treatment; and 0 = control. k.match.out<- matchit(nasal_ocular ~ m_age + m_bmi + smoke, data = k.gsk, distance = "mahalanobis", method = "genetic", discard = "both", ratio = 1,pop.size=400, int.seed=3313) k.z.out <- zelig(RQ_Act ~ nasal_ocular + m_age + m_bmi + smoke, model = "ls", data = match.data(k.match.out)) k.x.out <- setx(k.z.out,fn = NULL, data = match.data(k.match.out, "treat"), cond = TRUE) set.seed(3251) k.s.out <- sim(k.z.out, x=k.x.out, num=1000) summary(k.s.out) Many thanks, Stephen Stephen Kay ¦ Director of Statistics for Adelphi Real World (Manchester) Adelphi Mill, Bollington, Cheshire SK10 5JB UK Web: http://www.adelphigroup.com/companies/company_real_world.asp <http://www.adelphigroup.com/companies/company_real_world.asp> Adelphi retains ownership of all DSP data and fieldwork materials. In accordance with the company's Terms and Conditions, written prior approval must be obtained with regard to any use of the data in any items submitted for publication or for use in marketing materials. Please consider the environment before printing this e-mail DISCLAIMER: The information in this message is confidential and may be legally privileged. It is intended solely for the addressee. Access to this message by anyone else is unauthorised. If you are not the intended recipient, any disclosure, copying, or distribution of the message, or any action or omission taken by you in reliance on it, is prohibited and may be unlawful. Please immediately contact the sender if you have received this message in error. Thank you.

13 years, 5 months

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

Zelig November 2010