Amelia January 2011

amelia@lists.gking.harvard.edu

5 participants
3 discussions

Multiple imputation in an atypical context

by Pablo Mitnik

Dear all, I need to do multiple imputation in a context that, I believe, is at least somewhat atypical. I have two datasets. In one dataset, family income is measured in broad dollar intervals (e.g., $0 - $4,999, $5,000 - $9,999, and so forth). In the other, family income is measured in dollars. Many demographic variables are common to both datasets. I want to do MI of income measured in dollars in the first dataset using the information contained in the second and the interval measure of income included in the first. I have two questions: (a) Is there any methodological reason NOT to do MI using information in one dataset to impute in the other? (b) In order to use the information contained in the interval measure of income, I am considering doing MI by interval. That is, I would create as many auxiliary datasets as income intervals there are in the first dataset. Each of these auxiliary datasets would have all observations in that income interval from both original datasets. The idea is to do MI in each auxiliary dataset separately, and then put together MI'ed complete datasets by combining in the obvious way the original observations (including the imputed variable) from all auxiliary datasets, but discarding the observations contributed by the second original dataset. My question is whether this would be a legit procedure, and whether there may be a better way of achieving my goal. I would appreciate any help with these issues enormously. All best, Pablo Mitnik -- Pablo A. Mitnik Postdoctoral Scholar Stanford University (http://www.stanford.edu) Center for the Study of Poverty and Inequality (http://stanford.edu/group/scspi-dev/index.html) 450 Serra Mall, Building 80 Room 110 Stanford, CA 94305-2029 650 724 3889 pmitnik(a)stanford.edu - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia

13 years, 2 months

model selection and stepAIC

by Emily C

Hi, I'm trying to use the stepAIC function after creating a model via zelig like this (where a.out is an amelia object with 5 multiply imputed data sets): m<-zelig(y ~ x1 + x2 + x3 + x4 + x5, data=a.out$imputations, model="negbin") selected<-stepAIC(m, direction="backward") But it gives me this error at the stepAIC step: "Error in terms.default(object) : no terms component" Anyone know how to perform model selection with amelia/zelig? Thanks in advance! Emily

13 years, 2 months

imputation without taking into account the "not applicable" value

by Blais, Martin

Hi I need to impute data with two types of missing. Some are real missing that I want to impute (participants should have answered the questions, but they skipped it or they were not exposed to it (planned missingness)). Others are "not applicable" items, so I do not want to impute them... The two types have different codes in the database (888 for N/A and 999 for missing). The problem is that I don't know how to prevent the 888s to be taken as valid value in the imputation model... I want the items to be taken into account in the imputation model when they have a valid value (that is not 888) and to be imputed when their value is 999. How can I avoid the "not applicable" code 888 being taken as a valid value? Is there an easy way to implement this in AmeliaView? (I do not use R.) Hope I am clear enough... I could not find any information about this issue (although it must be very common), but I might not have the right keywords. Very many thanks, Martin

13 years, 2 months

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Amelia January 2011