Re: [amelia] Error and too many time variables used

Hi Matt, Thank you for your suggestion and clarification on how I got that many time variables. However, when I set "intercs=FALSE”, amelia appeared to impute my data poorly, as seen in the attached images of the overimpute plot and a couple of samples of the tscsPlots. I also tried with lags and leads, but the overimpute plot also showed that it had poor accuracy. In contrast, when I explored imputing only few participants at a time (e.g., n=5) with “intercs=TRUE”, amelia seemed to do a good job with the imputation, indicated by having short 95% bands and usually good alignment of the points in the overimpute plot. Do you think it would be appropriate to select five participants randomly at a time (i.e., 260 participants / 5 = 52 times) to run amelia? However, this approach may be scrutinized for not imputing using all study’s participants together though. What would you suggest? [cid:06083E65-E672-4A0E-B0CA-8036D7108D58][cid:59E67800-05A8-4BCB-B1D5-288B7C0A60A1][cid:935C3DB0-E3EE-4B1C-9EF1-317C04700D53] Best regards, Lawrence On Jun 23, 2017, at 10:07 PM, Matt Blackwell <mblackwell@gov.harvard.edu<mailto:mblackwell@gov.harvard.edu>> wrote: Hi Lawrence, What's happening here is that "polytime = 2" adds a "time" and "time^2" as additional regressors to the imputation matrix. Then, "intercs = TRUE" adds a dummy variable for each cross-sectional unit *and then interacts the two time variables with each dummy variable*. Thus, if you have 260 participants x 2 time variables, that's 520 variables that you're adding to the imputation matrix. The idea behind what you are doing is to capture participant-specific time trends, but there appears to not be enough data per respondent to estimate these trends well. Thus, you could probably fix the problem by estimating a global time trend with "intercs = FALSE". Hope that helps! Cheers, Matt ~~~~~~~~~~~ Matthew Blackwell Assistant Professor of Government Harvard University url: http://www.mattblackwell.org<http://www.mattblackwell.org/> On Thu, Jun 22, 2017 at 5:24 PM, Lawrence Chen <lawrence.m.chen@mail.mcgill.ca<mailto:lawrence.m.chen@mail.mcgill.ca>> wrote: Hi, I’m new to using Amelia. I’m trying to impute missing data for a time-series cross-sectional data, but I'm having trouble running amelia() the way I think I should. I would greatly appreciate some guidance. I created a data.frame() that has 8 time points each for 260 participants and a single score column for which I’m trying to impute some missing data. The data frame has 2080 (i.e., 8*260) rows by 3 columns (“month”, “ID”, “score”). With this, I tried to run the following command: ``` a.out <- amelia(data, ts="month", cs="ID", polytime=2, intercs=TRUE, p2s=2) ``` It reported (which I terminated part way through after receiving errors): amelia starting beginning prep functions Variables used: score time.1 time.2 time.3 time.4 time.5 time.6 time.7 time.8 time.9 time.10 time.11 time.12 time.13 time.14 time.15 time.16 time.17 time.18 time.19 time.20 time.21 time.22 time.23 time.24 time.25 time.26 time.27 time.28 time.29 time.30 time.31 time.32 time.33 time.34 time.35 time.36 time.37 time.38 time.39 time.40 time.41 time.42 time.43 time.44 time.45 time.46 time.47 time.48 time.49 time.50 time.51 time.52 time.53 time.54 time.55 time.56 time.57 time.58 time.59 time.60 time.61 time.62 time.63 time.64 time.65 time.66 time.67 time.68 time.69 time.70 time.71 time.72 time.73 time.74 time.75 time.76 time.77 time.78 time.79 time.80 time.81 time.82 time.83 time.84 time.85 time.86 time.87 time.88 time.89 time.90 time.91 time.92 time.93 time.94 time.95 time.96 time.97 time.98 time.99 time.100 time.101 time.102 time.103 time.104 time.105 time.106 time.107 time.108 time.109 time.110 time.111 time.112 time.113 time.114 time.115 time.116 time.117 time.118 time.119 time.120 time.... <truncated> running bootstrap -- Imputation 1 -- setting up EM chain indicies 1(300713)! 2 error: inv_sympd(): matrix seems singular (216)! 3 error: inv_sympd(): matrix seems singular (208)! Warning message: In amelia.prep(x = x, m = m, idvars = idvars, empri = empri, ts = ts, : You have a small number of observations, relative to the number, of variables in the imputation model. Consider removing some variables, or reducing the order of time polynomials to reduce the number of parameters. I don’t understand the error. I also don’t understand how it determined the `time.x` variables—I know it has something to do with my number of participants but I don’t understand how. The warning message suggests I have too many variables because of this. When I tried using the “freetrade" dataset, it used way fewer `time.x` variables (i.e., 26) even though there were only 19 time points in the data set and didn’t have problems. Could someone explain to me about the error or what may be the problem and what I should do to correct it? Also, when using time series data, do I use amelia() differently whether the time variable is treated as chronological time (e.g., January, February, March, …) or time of onset (e.g., one month since birth, two months since birth, etc.)? Please advise. Best regards, Lawrence -- Amelia mailing list served by HUIT [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia Amelia mailing list Amelia@lists.gking.harvard.edu<mailto:Amelia@lists.gking.harvard.edu> To unsubscribe from this list or get other information: https://lists.gking.harvard.edu/mailman/listinfo/amelia