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    <p>Here is an annotated and edited transcript of my attempt to reproduce your problem:</p> <pre><code>&gt; library(Zelig) ZELIG (Versions 4.1-3, built: 2013-01-30) &gt; a.out &lt;- amelia(freetrade, idvars="country", m = 5) Error: could not find function "amelia" </code></pre> <p>The first problem I had is that you did not mention that we need to load the Amelia package. After correcting that I tried again to run the first line:</p> <pre><code>&gt; library(Amelia) ## (Version 1.7, built: 2013-02-10) &gt; a.out &lt;- amelia(freetrade, idvars="country", m = 5) Error in amelia(freetrade, idvars = "country", m = 5) : object 'freetrade' not found </code></pre> <p>This fails because you did not say how to get the freetrade data. Guessing here:</p> <pre><code>&gt; data(freetrade) &gt; a.out &lt;- amelia(freetrade, m = 5) Amelia Error Code: 38 The following variable(s) are characters: country You may have wanted to set this as a ID variable to remove it from the imputation model or as an ordinal or nominal variable to be imputed. Please set it as either and try again. </code></pre> <p>Your example does not work because the <code>amelia</code> function needs to be told what to do with character variables. So I modified your example in order to get one that would actually run:</p> <pre><code>&gt; a.out &lt;- amelia(freetrade, idvars="country", m = 5) &gt; z.out.imp &lt;- zelig(tariff ~ polity + pop + gdp.pc + year + country, + data = a.out$imputations, model = "ls") </code></pre> <p>Running <code>summary</code> on the result gives me the combined model statistics"</p> <pre><code># This part works just fine. &gt; summary(z.out.imp) Model: ls Number of multiply imputed data sets: 5 Combined results: Call: lm(formula = formula, weights = weights, model = F, data = data) Coefficients: Value Std. Error t-stat p-value (Intercept) 3.294715e+03 6.425487e+02 5.1275725 1.330807e-05 polity 2.761343e-01 3.354271e-01 0.8232319 4.145813e-01 pop -6.443769e-08 5.526885e-08 -1.1658953 2.659143e-01 gdp.pc 4.549885e-04 1.354139e-03 0.3359984 7.382138e-01 year -1.599422e+00 3.306932e-01 -4.8365739 2.649602e-05 countryIndonesia -7.396526e+01 4.112206e+01 -1.7986760 1.009329e-01 countryKorea -9.673542e+01 5.036909e+01 -1.9205317 8.713903e-02 countryMalaysia -9.271187e+01 4.998836e+01 -1.8546690 9.424041e-02 countryNepal -8.863525e+01 4.920061e+01 -1.8015072 9.990792e-02 countryPakistan -4.789370e+01 4.362907e+01 -1.0977475 2.960914e-01 countryPhilippines -8.548672e+01 4.662372e+01 -1.8335456 9.533829e-02 countrySriLanka -8.446560e+01 4.939918e+01 -1.7098586 1.170170e-01 countryThailand -8.026702e+01 4.741244e+01 -1.6929529 1.213329e-01 For combined results from datasets i to j, use summary(x, subset = i:j). For separate results, use print(summary(x), subset = i:j). </code></pre> <p>In short, the only thing in your example that works for me is the one thing you claim does not work for you. Please post the code and output showing exactly what you did and exactly what happened, because at the moment I don't have enough information to help solve the problem.</p>
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    1. POdegrees of freedom, t-statistic, and f-values of combined multiply imputed data
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    1. CO@MarkMiller No, the estimates are not the same because a) multiple imputations will differ across runs, and b) the imputations were done differently. The imputations in my attempted reproduction are as close to the OP's example I could get to run. The imputations in the documentation were done differently than the OP's example. But my final result is the combined model statistics the OP was asking for.
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    2. COThese do not appear to me to be close to the estimates from page 46 of the document linked by the original poster.
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    3. COAlso @MarkMiller the example in the documentation doesn't include country in the model, while the OP here does. There is no reason to expect my answer to reproduce the example in the manual -- it is based on the OP's example, not on the example from the manual!
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