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    <p>From the SAS docs: <a href="http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glm_sect038.htm" rel="nofollow">http://support.sas.com/documentation/cdl/en/statug/63033/HTML/default/viewer.htm#statug_glm_sect038.htm</a></p> <p>"for an analysis with the MANOVA or REPEATED statement, or with the MANOVA option in the PROC GLM statement, PROC GLM uses an observation if values are nonmissing for all dependent variables and all the variables used in independent effects."</p> <p>To check, see if R produces the same value as SAS does if you restrict it to complete cases.</p> <p>e.g.</p> <pre><code>Fit &lt;- manova(formula = cbind(Y1, Y2, Y3, Y4, Y5) ~ A + Rep:A + B + A: B, data = Data[complete.cases(Data),]) summary.aov(Fit) summary(Fit, test="Wilks") summary.manova(Fit) </code></pre> <p>The results I get for this are:</p> <pre><code>&gt; Fit &lt;- manova(formula = cbind(Y1, Y2, Y3, Y4, Y5) ~ A + Rep:A + B + A: B, + data = Data[complete.cases(Data),]) &gt; summary.aov(Fit) Response Y1 : Df Sum Sq Mean Sq F value Pr(&gt;F) A 11 3976.4 361.49 302.8417 &lt; 2.2e-16 *** B 15 122.1 8.14 6.8188 5.124e-13 *** A:Rep 12 22.2 1.85 1.5507 0.104296 A:B 164 283.3 1.73 1.4470 0.002253 ** Residuals 357 426.1 1.19 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Response Y2 : Df Sum Sq Mean Sq F value Pr(&gt;F) A 11 9221.1 838.28 297.6565 &lt; 2e-16 *** B 15 3260.7 217.38 77.1867 &lt; 2e-16 *** A:Rep 12 76.8 6.40 2.2711 0.00874 ** A:B 164 1482.6 9.04 3.2100 &lt; 2e-16 *** Residuals 357 1005.4 2.82 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Response Y3 : Df Sum Sq Mean Sq F value Pr(&gt;F) A 11 1248016 113456 1098.0101 &lt; 2.2e-16 *** B 15 53776 3585 34.6960 &lt; 2.2e-16 *** A:Rep 12 3781 315 3.0491 0.0004171 *** A:B 164 96762 590 5.7101 &lt; 2.2e-16 *** Residuals 357 36888 103 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Response Y4 : Df Sum Sq Mean Sq F value Pr(&gt;F) A 11 1.21607 0.110552 1482.0020 &lt; 2.2e-16 *** B 15 0.03765 0.002510 33.6438 &lt; 2.2e-16 *** A:Rep 12 0.00312 0.000260 3.4806 7.067e-05 *** A:B 164 0.09905 0.000604 8.0961 &lt; 2.2e-16 *** Residuals 357 0.02663 0.000075 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Response Y5 : Df Sum Sq Mean Sq F value Pr(&gt;F) A 11 107541 9776.5 1077.0236 &lt; 2e-16 *** B 15 3503 233.5 25.7242 &lt; 2e-16 *** A:Rep 12 305 25.4 2.7966 0.00115 ** A:B 164 6581 40.1 4.4209 &lt; 2e-16 *** Residuals 357 3241 9.1 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 &gt; summary(Fit, test="Wilks") Df Wilks approx F num Df den Df Pr(&gt;F) A 11 0.00022 153.064 55 1637.5 &lt; 2.2e-16 *** B 15 0.02663 25.580 75 1695.0 &lt; 2.2e-16 *** A:Rep 12 0.69568 2.225 60 1656.7 3.73e-07 *** A:B 164 0.01175 3.093 820 1770.2 &lt; 2.2e-16 *** Residuals 357 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 &gt; summary.manova(Fit) Df Pillai approx F num Df den Df Pr(&gt;F) A 11 3.4175 70.086 55 1785 &lt; 2.2e-16 *** B 15 2.2866 20.056 75 1785 &lt; 2.2e-16 *** A:Rep 12 0.3457 2.210 60 1785 4.49e-07 *** A:B 164 2.8037 2.779 820 1785 &lt; 2.2e-16 *** Residuals 357 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 </code></pre>
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