Hi Sivan, I think they problem is that you may have most likely set the the polynomials of time to intersect with the cross-section. Unfortunately this adds many parameters to the model (2 for each cross-section unit). If you uncheck this box (in the TSCS menu), then it should run. Cheers, matt. On Wed, Mar 17, 2010 at 8:30 PM, Sivan Rotenberg <sivanrotenberg(a)gmail.com> wrote: of measure ID about p(p+3)/2

Hi Sivan, Typically, the length of the chain indicated how much information is in the missing part of your data. So, lower chain lengths are generally better and 4-6 are fairly short chains. Chains in large, complicated datasets can easily reach into the hundreds or thousands. Cheers, matt. On Wed, Mar 17, 2010 at 9:08 PM, Sivan Rotenberg <sivanrotenberg(a)gmail.com> wrote: - 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

Hi Shanruo, I'm not exactly sure what you mean. If there are certain variables in your data that you do not want to impute, you can always set them to an ID variable in the Amelia options. But you should only do this for variables that you do not want to include in the analysis or imputation, such as unit-specific identifiers. In most cases you do not want to re-introduce missingness into your data by removing imputed values. In any event, you can always use the combination rules for getting your estimates. Cheers, matt. On Tue, Mar 30, 2010 at 12:00 PM, Shanruo Ning Zhang <nizhang(a)calpoly.edu> wrote: - 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

This is correct. Amelia will only fill in missing cells with imputed values. Your observed data is always preserved in the imputed datasets, even when it is used in the imputation. Cheers, matt. On Thu, Apr 1, 2010 at 11:12 AM, Cyrus Samii <cds81(a)columbia.edu> wrote: > On this, Matt, correct me if I am wrong, but Amelia won't overwrite > existing data with imputed values, even though it will fully > incorporate variables with no missingness into the model (to get the > covariances, etc). So if I follow what you are saying, Shanruo, what > you are proposing is what is pretty much always done. Or maybe I am > misunderstanding... > > Cyrus > > On Wed, Mar 31, 2010 at 5:52 PM, Matt Blackwell > <blackwel(a)fas.harvard.edu> wrote: >> Hi Shanruo, >> >> I'm not exactly sure what you mean. If there are certain variables >> in >> your data that you do not want to impute, you can always set them to >> an ID variable in the Amelia options. But you should only do this >> for >> variables that you do not want to include in the analysis or >> imputation, such as unit-specific identifiers. In most cases you do >> not want to re-introduce missingness into your data by removing >> imputed values. >> >> In any event, you can always use the combination rules for getting >> your estimates. >> >> Cheers, >> matt. >> >> On Tue, Mar 30, 2010 at 12:00 PM, Shanruo Ning Zhang >> <nizhang(a)calpoly.edu> wrote: >>> Dear Amelia authors, >>> As an effort to reduce the amount of imputed values used in our >>> analysis, my >>> co-author and I are contemplating merging some imputed variables >>> back into >>> the original (un-imputed) data set and use imputed and un-imputed >>> variables >>> together. Is this a feasible plan? If so, can I still use the >>> functions on >>> page 5-6 on the the Amelia User's Guide (Amelia II: A Program for >>> Missing >>> Data) to combine the results? >>> Thanks very much. >>> Best, >>> Shanruo Ning Zhang >>> Assistant Professor >>> California Polytechnic State University >>> San Luis Obispo, CA 93401 >>> >>> On Mar 17, 2010, at 6:19 PM, Matt Blackwell wrote: >>> >>>> Hi Sivan, >>>> >>>> Typically, the length of the chain indicated how much >>>> information is >>>> in the missing part of your data. So, lower chain lengths are >>>> generally better and 4-6 are fairly short chains. Chains in large, >>>> complicated datasets can easily reach into the hundreds or >>>> thousands. >>>> >>>> Cheers, >>>> matt. >>>> >>>> On Wed, Mar 17, 2010 at 9:08 PM, Sivan Rotenberg >>>> <sivanrotenberg(a)gmail.com> wrote: >>>>> >>>>> Hi Matt, >>>>> >>>>> Thank you very much! I have another question. Is there an >>>>> optimum number >>>>> of >>>>> chains per imputation? I run about 20 sets and when I did it >>>>> for my data >>>>> I >>>>> got chain lengths between 4-6, is that ok? Does the chain >>>>> length provide >>>>> any >>>>> additional information? >>>>> >>>>> Thank you, >>>>> Sivan >>>>> >>>>> On Wed, Mar 17, 2010 at 8:59 PM, Matt Blackwell >>>>> <blackwel(a)fas.harvard.edu> >>>>> wrote: >>>>>> >>>>>> Hi Sivan, >>>>>> >>>>>> I think they problem is that you may have most likely set the >>>>>> the >>>>>> polynomials of time to intersect with the cross-section. >>>>>> Unfortunately >>>>>> this adds many parameters to the model (2 for each cross-section >>>>>> unit). If you uncheck this box (in the TSCS menu), then it >>>>>> should run. >>>>>> >>>>>> Cheers, >>>>>> matt. >>>>>> >>>>>> On Wed, Mar 17, 2010 at 8:30 PM, Sivan Rotenberg >>>>>> <sivanrotenberg(a)gmail.com> wrote: >>>>>>> >>>>>>> Hi, >>>>>>> >>>>>>> I am trying to impute my data file that has participants >>>>>>> over three >>>>>>> days of >>>>>>> sampling and two predictors. I set up my data in SPSS with >>>>>>> like this: >>>>>>> >>>>>>> ID Time predictor1 predictor2 measure 1 measure 2 >>>>>>> measure 3 >>>>>>> measure >>>>>>> 4 >>>>>>> 101.0 1.0 10.0 8:58 1.097 -0.492 >>>>>>> 9.680 -0.095 >>>>>>> 101.0 2.0 10.0 9:00 2.399 0.299 >>>>>>> 13.202 -0.801 >>>>>>> 101.0 3.0 10.0 8:15 1.636 -0.316 >>>>>>> 20.445 -0.931 >>>>>>> 102.0 1.0 27.0 7:07 3.903 0.473 >>>>>>> 31.106 -0.916 >>>>>>> 102.0 2.0 27.0 7:21 3.797 0.473 >>>>>>> 31.994 -0.966 >>>>>>> 102.0 3.0 27.0 7:34 2.829 0.227 >>>>>>> 29.497 -0.958 >>>>>>> >>>>>>> I tried using the time series cross sectional option using >>>>>>> time as ts >>>>>>> and ID >>>>>>> as cs. I put the polynomials time as 1 (although I'm really >>>>>>> not sure >>>>>>> about >>>>>>> that) and used an EM prior of 4. At the bottom of my >>>>>>> AmeliaView it says >>>>>>> I >>>>>>> have 381 observations and 9 variables, which does not exceed >>>>>>> the >>>>>>> p(p+3)/2 >>>>>>> formula yet I am still getting error code 34: >>>>>>> >>>>>>> Amelia Error Code: 34 >>>>>>> The number of observations in too low to estimate the number >>>>>>> of >>>>>>> parameters. You can either remove some variables, reduce >>>>>>> the order of the time polynomial, or increase the empirical >>>>>>> prior. >>>>>>> >>>>>>> You have recieved an error. You can close this window and >>>>>>> reset >>>>>>> various options to correct the error. >>>>>>> >>>>>>> I'm not sure what I'm doing wrong! Any help would be greatly >>>>>>> appreciated! >>>>>>> >>>>>>> Thank you, >>>>>>> Sivan >>>>>>> >>>>> >>>>> >>>> - >>>> 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 >>> >>> - >>> 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 >>> >>> >> - >> 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 >> > > > > -- > Cyrus Samii > Political Science > Columbia University > cds81(a)columbia.edu > > Burundi Survey: www.columbia.edu/~cds81/burundisurvey/ > ISERP Statistical Consulting: iserp.columbia.edu/statistical- > consulting > Evidence in peacebuilding initiative: peacebuildingsurveys.org > >