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    <p>Please try below:</p> <pre><code>model1 &lt;- lme( Y ~ GROUP + X1, random = ~ GROUP | person, correlation = corCompSymm(form = ~ day | person), na.action = na.exclude, data = df1, method = "REML" ) summary(model1) </code></pre> <p>I think <code>random = ~ groupvar | subjvar</code> option with <code>R</code> <code>lme</code> provides similar result of <code>repeated / subject = subjvar group = groupvar</code> option with <code>SAS/MIXED</code> in this case.</p> <p><strong>Edit:</strong></p> <p>SAS/MIXED</p> <p><img src="https://i.stack.imgur.com/7KMOS.gif" alt="SAS/MIXED covariance matrix"></p> <p>R (a revised model2)</p> <pre><code>model2 &lt;- lme( Y ~ GROUP + X1, random = list(person = pdDiag(form = ~ GROUP - 1)), correlation = corCompSymm(form = ~ day | person), weights = varIdent(form = ~ 1 | GROUP), na.action = na.exclude, data = df1, method = "REML" ) summary(model2) </code></pre> <p><img src="https://i.stack.imgur.com/dmHxk.gif" alt="R covariance matrix"></p> <p>So, I think these covariance structures are very similar (&sigma;<sub>g1</sub> = &tau;<sub>g</sub><sup>2</sup> + &sigma;<sub>1</sub>).</p> <p><strong>Edit 2:</strong> </p> <p>Covariate estimates (SAS/MIXED):</p> <pre><code>Variance person GROUP TEST 8789.23 CS person GROUP TEST 125.79 Variance person GROUP CONTROL 82775 CS person GROUP CONTROL 33297 </code></pre> <p>So </p> <pre><code>TEST group diagonal element = 125.79 + 8789.23 = 8915.02 CONTROL group diagonal element = 33297 + 82775 = 116072 </code></pre> <p>where diagonal element = &sigma;<sub>k1</sub> + &sigma;<sub>k</sub><sup>2</sup>.</p> <p>Covariate estimates (R lme):</p> <pre><code>Random effects: Formula: ~GROUP - 1 | person Structure: Diagonal GROUP1TEST GROUP2CONTROL Residual StdDev: 14.56864 184.692 93.28885 Correlation Structure: Compound symmetry Formula: ~day | person Parameter estimate(s): Rho -0.009929987 Variance function: Structure: Different standard deviations per stratum Formula: ~1 | GROUP Parameter estimates: 1TEST 2CONTROL 1.000000 3.068837 </code></pre> <p>So </p> <pre><code>TEST group diagonal element = 14.56864^2 + (3.068837^0.5 * 93.28885 * -0.009929987) + 93.28885^2 = 8913.432 CONTROL group diagonal element = 184.692^2 + (3.068837^0.5 * 93.28885 * -0.009929987) + (3.068837 * 93.28885)^2 = 116070.5 </code></pre> <p>where diagonal element = &tau;<sub>g</sub><sup>2</sup> + &sigma;<sub>1</sub> + &sigma;<sub>g</sub><sup>2</sup>.</p>
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    1. POConverting Repeated Measures mixed model formula from SAS to R
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    1. POConverting Repeated Measures mixed model formula from SAS to R
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    1. COI'm pretty sure that `random = ~ GROUP | person` doesn't change anything from the original code because each person is only in one group. What that syntax would do is allow the covariance between levels of group to differ within an individual.
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    2. COI still think the random effect isn't right. `random = list(person = pdDiag(form = ~ GROUP - 1))` is still allowing the covariance between levels of group to differ within an individual, but is forcing them not to be correlated.
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    3. COAlso, since R actually parameterizes the model in terms of the correlation and the variances, it's hard to see how your matrix matches the R code you wrote. Not that it's necessarily wrong, but it would help if you used R's parameterization to explain.
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