# MixedModels.jl confidence intervals and repeated measure, how to get? **URL:** \ **Category:** Statistics\ **Tags:** question\ **Created:** [June 21, 2019, 6:34pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534 "2019-06-21T18:34:32Z")\ **Posts on this page:** 10\ **Page:** 1

**Author:** ![PharmCat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pharmcat/32/6953_2.png) [@PharmCat](https://discourse.julialang.org/u/PharmCat)\ **Post date:** [June 21, 2019, 6:34pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/1 "2019-06-21T18:34:32Z")

I have a data: ```julia julia> df 298×6 DataFrame │ Row │ Subject │ Period │ Seq │ Formulation │ logData │ Data │ │ │ Categorical… │ Categorical… │ Categorical… │ Categorical… │ Float64 │ Float64 │ ├─────┼──────────────┼──────────────┼──────────────┼──────────────┼─────────┼─────────┤ │ 1 │ 1 │ 1 │ BABA │ R │ 7.73454 │ 2285.96 │ │ 2 │ 1 │ 2 │ BABA │ T │ 7.57857 │ 1955.82 │ │ 3 │ 1 │ 3 │ BABA │ R │ 7.20485 │ 1345.94 │ │ 4 │ 1 │ 4 │ BABA │ T │ 7.95726 │ 2856.24 │ ⋮ │ 294 │ 77 │ 4 │ BABA │ T │ 7.76337 │ 2352.82 │ │ 295 │ 78 │ 1 │ BABA │ R │ 9.92199 │ 20373.5 │ │ 296 │ 78 │ 2 │ BABA │ T │ 9.52439 │ 13689.6 │ │ 297 │ 78 │ 3 │ BABA │ R │ 9.93232 │ 20585.0 │ │ 298 │ 78 │ 4 │ BABA │ T │ 10.1064 │ 24498.1 │ ``` I try to use MixedModels.jl: ```julia lm = LinearMixedModel(@formula(logData ~ 1 + Period + Seq + Formulation + (0 + Formulation|Subject)), df) fm1 = fit!(lm, REML=true) ``` How can i get confidence interval for formulation difference (contrast)? How can i fit repeated measure and use Period as repeated factor? ---

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\ **Post date:** [June 22, 2019, 8:31am UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/2 "2019-06-22T08:31:26Z")

Not sure I understand correctly but it looks as though there’s only two level to your variable so the reported coefficient estimate (and standard error) is the difference between `T` and `R`? ---

**Author:** ![PharmCat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pharmcat/32/6953_2.png) [@PharmCat](https://discourse.julialang.org/u/PharmCat)\ **Post date:** [June 22, 2019, 7:57pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/3 "2019-06-22T19:57:17Z")

Hello! Yes! I try to make CI for T-R difference. ---

**Author:** ![PharmCat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pharmcat/32/6953_2.png) [@PharmCat](https://discourse.julialang.org/u/PharmCat)\ **Post date:** [June 23, 2019, 2:21am UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/4 "2019-06-23T02:21:19Z")

Exactly I want to get intra-subject variance for T and R separately. But for code above i get results: ```julia inear mixed model fit by REML Formula: logData ~ 1 + Period + Seq + Formulation + ((0 + Formulation) | Subject) REML criterion at convergence: 535.1437908228327 Variance components: Column Variance Std.Dev. Corr. Subject Formulation: R 0.75000775 0.86602988 Formulation: T 0.66580315 0.81596762 1.00 Residual 0.15924154 0.39905079 Number of obs: 298; levels of grouping factors: 77 ``` Much higher than in SAS or SPSS. I suppose it is wrong fitting because repeated effect not included. May be this fitting more correct: ```julia lm = LinearMixedModel(@formula(logData ~ 1 + Period + Seq + Formulation + (0 + Formulation|Subject) + (0 + Formulation|Period)), df) ``` But this results far from SPSS: ![berep](https://global.discourse-cdn.com/julialang/original/3X/1/7/17dae7e7942e49428c8d3e82916da5a51eefa066.png) ---

**Author:** ![nilshg](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/nilshg/32/2283_2.png) [@nilshg](https://discourse.julialang.org/u/nilshg)\ **Post date:** [June 24, 2019, 9:53pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/5 "2019-06-24T21:53:12Z")

I note that the difference between the coefficients is the same at 0.085 for Julia and SPSS - might be due to different selection of base category for the categorical coding? Have a look at [Contrast coding categorical variables · StatsModels.jl](https://juliastats.github.io/StatsModels.jl/latest/contrasts/) Are you able to share your data? ---

**Author:** ![PharmCat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pharmcat/32/6953_2.png) [@PharmCat](https://discourse.julialang.org/u/PharmCat)\ **Post date:** [June 25, 2019, 8:20am UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/6 "2019-06-25T08:20:13Z")

Yep! ```julia Subject,Period,Seq,Formulation,logData,Data 1,1,BABA,R,7.734541,2285.96 1,2,BABA,T,7.578565,1955.82 1,3,BABA,R,7.204848,1345.94 1,4,BABA,T,7.957261,2856.24 2,1,ABAB,T,8.055704,3151.72 2,2,ABAB,R,7.859143,2589.3 2,3,ABAB,T,8.004011,2992.94 2,4,ABAB,R,7.788792,2413.4 3,1,ABAB,T,8.090935,3264.74 3,2,ABAB,R,8.088844,3257.92 3,3,ABAB,T,8.039332,3100.54 3,4,ABAB,R,8.037272,3094.16 4,1,ABAB,T,7.095363,1206.36 4,2,ABAB,R,7.175153,1306.56 4,3,ABAB,T,7.367153,1583.12 4,4,ABAB,R,7.207445,1349.44 5,1,BABA,R,8.263822,3880.9 5,2,BABA,T,8.898759,7322.88 5,3,BABA,R,8.396078,4429.66 5,4,BABA,T,8.108587,3322.88 6,1,BABA,R,6.885591,978.08 6,2,BABA,T,7.099235,1211.04 6,3,BABA,R,6.881288,973.88 6,4,BABA,T,7.048213,1150.8 7,1,ABAB,T,7.980728,2924.06 7,2,ABAB,R,7.736298,2289.98 7,3,ABAB,T,7.821755,2494.28 7,4,ABAB,R,8.083063,3239.14 8,1,BABA,R,7.793776,2425.46 8,2,BABA,T,8.217638,3705.74 8,3,BABA,R,7.544893,1891.06 8,4,BABA,T,9.102657,8979.12 9,1,BABA,R,8.249319,3825.02 9,2,BABA,T,8.578296,5315.04 9,3,BABA,R,8.66788,5813.16 9,4,BABA,T,9.348004,11475.9 10,1,BABA,R,8.321728,4112.26 10,2,BABA,T,8.248754,3822.86 10,3,BABA,R,7.807843,2459.82 10,4,BABA,T,8.437448,4616.76 11,1,ABAB,T,8.061581,3170.3 11,2,ABAB,R,7.726257,2267.1 11,4,ABAB,R,7.440335,1703.32 12,1,ABAB,T,8.005427,2997.18 12,2,ABAB,R,7.991179,2954.78 12,3,ABAB,T,8.56649,5252.66 12,4,ABAB,R,8.228054,3744.54 13,1,ABAB,T,7.628372,2055.7 13,2,ABAB,R,6.890914,983.3 13,3,ABAB,T,7.479469,1771.3 13,4,ABAB,R,8.099609,3293.18 14,1,BABA,R,7.371879,1590.62 14,2,BABA,T,7.040134,1141.54 14,3,BABA,R,7.121527,1238.34 14,4,BABA,T,7.159136,1285.8 15,1,ABAB,T,7.293358,1470.5 15,2,ABAB,R,7.023616,1122.84 15,3,ABAB,T,7.372859,1592.18 15,4,ABAB,R,7.469175,1753.16 16,1,BABA,R,7.542288,1886.14 16,2,BABA,T,7.638815,2077.28 16,3,BABA,R,7.69513,2197.62 16,4,BABA,T,7.693773,2194.64 17,1,ABAB,T,6.444386,629.16 17,2,ABAB,R,6.211283,498.34 17,3,ABAB,T,6.313077,551.74 17,4,ABAB,R,5.945892,382.18 18,1,BABA,R,6.141951,464.96 18,2,BABA,T,7.989506,2949.84 18,3,BABA,R,7.094716,1205.58 18,4,BABA,T,7.671342,2145.96 19,1,BABA,R,7.54394,1889.26 19,2,BABA,T,8.671996,5837.14 19,3,BABA,R,7.773106,2375.84 19,4,BABA,T,7.422649,1673.46 20,1,ABAB,T,6.676378,793.44 20,2,ABAB,R,7.06452,1169.72 20,4,ABAB,R,6.978027,1072.8 21,1,BABA,R,7.642898,2085.78 21,2,BABA,T,7.771995,2373.2 21,3,BABA,R,7.350516,1557 21,4,BABA,T,7.666353,2135.28 22,1,BABA,R,5.663169,288.06 22,2,BABA,T,5.736508,309.98 22,3,BABA,R,5.781299,324.18 22,4,BABA,T,5.728735,307.58 23,1,ABAB,T,6.263017,524.8 23,2,ABAB,R,5.921149,372.84 23,3,ABAB,T,6.25175,518.92 23,4,ABAB,R,6.404501,604.56 24,1,ABAB,T,8.677088,5866.94 24,3,ABAB,T,8.621153,5547.78 24,4,ABAB,R,8.386355,4386.8 25,1,ABAB,T,8.296162,4008.46 25,2,ABAB,R,7.548998,1898.84 25,3,ABAB,T,7.355782,1565.22 25,4,ABAB,R,8.491941,4875.32 26,1,ABAB,T,7.087958,1197.46 26,2,ABAB,R,5.801574,330.82 26,3,ABAB,T,7.151611,1276.16 26,4,ABAB,R,5.97843,394.82 27,1,BABA,R,9.534102,13823.18 27,2,BABA,T,8.938377,7618.82 27,3,BABA,R,9.158346,9493.34 27,4,BABA,T,9.096997,8928.44 28,1,BABA,R,6.846794,940.86 28,2,BABA,T,7.080616,1188.7 28,3,BABA,R,6.782215,882.02 28,4,BABA,T,7.111822,1226.38 29,1,BABA,R,7.684894,2175.24 29,2,BABA,T,7.883959,2654.36 29,3,BABA,R,8.081865,3235.26 29,4,BABA,T,8.017406,3033.3 30,1,ABAB,T,7.085818,1194.9 30,2,ABAB,R,6.717393,826.66 30,3,ABAB,T,6.414082,610.38 30,4,ABAB,R,6.387115,594.14 31,1,BABA,R,8.320857,4108.68 31,2,BABA,T,8.90917,7399.52 31,4,BABA,T,8.403267,4461.62 32,1,ABAB,T,6.674839,792.22 32,2,ABAB,R,6.907495,999.74 32,3,ABAB,T,7.072761,1179.4 32,4,ABAB,R,7.42593,1678.96 33,1,BABA,R,8.275254,3925.52 33,2,BABA,T,8.240053,3789.74 33,3,BABA,R,8.150127,3463.82 33,4,BABA,T,8.42872,4576.64 34,1,BABA,R,7.443418,1708.58 34,2,BABA,T,7.824382,2500.84 34,3,BABA,R,7.141483,1263.3 34,4,BABA,T,7.624824,2048.42 35,1,ABAB,T,6.84913,943.06 35,2,ABAB,R,6.645377,769.22 35,3,ABAB,T,6.743824,848.8 35,4,ABAB,R,7.084964,1193.88 36,1,ABAB,T,7.840085,2540.42 36,2,ABAB,R,7.645484,2091.18 36,3,ABAB,T,7.856962,2583.66 36,4,ABAB,R,7.597888,1993.98 37,1,ABAB,T,6.746929,851.44 37,2,ABAB,R,6.482924,653.88 37,3,ABAB,T,7.771194,2371.3 37,4,ABAB,R,7.150999,1275.38 38,1,BABA,R,8.7086,6054.76 38,2,BABA,T,8.898663,7322.18 38,3,BABA,R,8.81685,6746.98 38,4,BABA,T,8.872165,7130.7 39,1,ABAB,T,8.670024,5825.64 39,2,ABAB,R,8.773821,6462.82 39,3,ABAB,T,8.9093,7400.48 39,4,ABAB,R,8.731795,6196.84 40,1,BABA,R,7.432732,1690.42 40,2,BABA,T,7.164643,1292.9 40,3,BABA,R,7.328043,1522.4 40,4,BABA,T,6.972213,1066.58 41,1,BABA,R,7.931306,2783.06 41,2,BABA,T,7.046717,1149.08 41,3,BABA,R,6.777555,877.92 41,4,BABA,T,6.349873,572.42 42,1,ABAB,T,8.467805,4759.06 42,2,ABAB,R,8.671102,5831.92 42,4,ABAB,R,8.33201,4154.76 43,1,ABAB,T,8.594021,5399.28 43,2,ABAB,R,8.598939,5425.9 43,3,ABAB,T,8.376666,4344.5 43,4,ABAB,R,8.413396,4507.04 44,1,ABAB,T,8.632502,5611.1 44,2,ABAB,R,8.602295,5444.14 44,3,ABAB,T,8.4776,4805.9 44,4,ABAB,R,8.509294,4960.66 45,1,BABA,R,6.561992,707.68 45,2,BABA,T,8.211119,3681.66 45,3,BABA,R,9.823051,18454.26 45,4,BABA,T,6.911209,1003.46 46,1,ABAB,T,7.783491,2400.64 46,2,ABAB,R,7.258835,1420.6 46,3,ABAB,T,7.044626,1146.68 46,4,ABAB,R,8.518337,5005.72 47,1,BABA,R,6.180182,483.08 47,2,BABA,T,6.940513,1033.3 47,3,BABA,R,6.468537,644.54 47,4,BABA,T,6.515157,675.3 48,1,BABA,R,7.676511,2157.08 48,2,BABA,T,8.044742,3117.36 48,3,BABA,R,7.943122,2816.14 48,4,BABA,T,7.955215,2850.4 49,1,ABAB,T,9.565322,14261.54 49,2,ABAB,R,10.184506,26489.56 49,3,ABAB,T,10.065847,23525.66 49,4,ABAB,R,9.963818,21243.76 50,1,ABAB,T,7.347454,1552.24 50,2,ABAB,R,7.358398,1569.32 50,3,ABAB,T,7.644919,2090 50,4,ABAB,R,7.299784,1479.98 51,1,BABA,R,8.251779,3834.44 51,2,BABA,T,8.496942,4899.76 51,3,BABA,R,8.216872,3702.9 51,4,BABA,T,8.644182,5677.02 52,1,BABA,R,8.687091,5925.92 52,2,BABA,T,6.875129,967.9 52,3,BABA,R,6.68088,797.02 52,4,BABA,T,6.84522,939.38 53,1,ABAB,T,8.168622,3528.48 53,2,ABAB,R,7.61941,2037.36 53,3,ABAB,T,8.074549,3211.68 53,4,ABAB,R,7.974787,2906.74 54,1,BABA,R,6.842854,937.16 54,2,BABA,T,8.752733,6327.96 54,3,BABA,R,6.96122,1054.92 54,4,BABA,T,7.476484,1766.02 55,1,ABAB,T,8.142639,3437.98 55,2,ABAB,R,8.224646,3731.8 55,3,ABAB,T,8.483165,4832.72 55,4,ABAB,R,8.104776,3310.24 56,1,ABAB,T,6.918834,1011.14 56,2,ABAB,R,6.483138,654.02 56,3,ABAB,T,6.75528,858.58 56,4,ABAB,R,6.811377,908.12 57,1,BABA,R,6.91109,1003.34 57,2,BABA,T,8.46378,4739.94 57,3,BABA,R,6.54799,697.84 57,4,BABA,T,7.825852,2504.52 58,1,BABA,R,8.778994,6496.34 58,2,BABA,T,8.691039,5949.36 58,3,BABA,R,8.700078,6003.38 58,4,BABA,T,8.759939,6373.72 59,1,BABA,R,7.128961,1247.58 59,2,BABA,T,7.018294,1116.88 59,3,BABA,R,7.061969,1166.74 59,4,BABA,T,7.885472,2658.38 60,1,ABAB,T,10.432044,33929.62 60,2,ABAB,R,10.124366,24943.44 60,3,ABAB,T,9.857979,19110.22 60,4,ABAB,R,9.457605,12805.18 62,1,ABAB,T,7.73215,2280.5 62,2,ABAB,R,7.446865,1714.48 62,3,ABAB,T,8.302583,4034.28 62,4,ABAB,R,8.137618,3420.76 63,1,ABAB,T,8.12466,3376.72 63,2,ABAB,R,7.71548,2242.8 63,3,ABAB,T,7.449812,1719.54 63,4,ABAB,R,7.758897,2342.32 64,1,BABA,R,6.816013,912.34 64,2,BABA,T,7.651795,2104.42 64,3,BABA,R,7.630966,2061.04 64,4,BABA,T,7.310884,1496.5 65,1,BABA,R,8.283479,3957.94 65,2,BABA,T,8.681962,5895.6 65,3,BABA,R,8.675833,5859.58 65,4,BABA,T,8.531782,5073.48 66,1,ABAB,T,7.061077,1165.7 66,2,ABAB,R,7.129794,1248.62 66,3,ABAB,T,7.06363,1168.68 66,4,ABAB,R,7.170443,1300.42 67,1,BABA,R,7.087908,1197.4 67,2,BABA,T,7.020495,1119.34 68,1,BABA,R,7.444085,1709.72 68,2,BABA,T,7.836923,2532.4 68,3,BABA,R,7.365825,1581.02 68,4,BABA,T,7.940014,2807.4 69,1,ABAB,T,7.93696,2798.84 69,2,ABAB,R,7.805515,2454.1 69,4,ABAB,R,8.582014,5334.84 70,1,BABA,R,8.370645,4318.42 70,2,BABA,T,7.6883,2182.66 70,3,BABA,R,7.408021,1649.16 70,4,BABA,T,7.390379,1620.32 71,1,ABAB,T,6.153243,470.24 71,2,ABAB,R,5.33773,208.04 72,1,ABAB,T,7.648883,2098.3 72,2,ABAB,R,7.559955,1919.76 72,3,ABAB,T,7.943698,2817.76 72,4,ABAB,R,7.621195,2041 73,1,ABAB,T,8.804973,6667.32 73,2,ABAB,R,8.573543,5289.84 73,3,ABAB,T,8.895668,7300.28 73,4,ABAB,R,9.181101,9711.84 74,1,BABA,R,7.619116,2036.76 74,2,BABA,T,7.574579,1948.04 74,3,BABA,R,7.339265,1539.58 74,4,BABA,T,7.63971,2079.14 75,1,ABAB,T,6.642878,767.3 75,2,ABAB,R,6.953015,1046.3 75,3,ABAB,T,7.237318,1390.36 75,4,ABAB,R,8.012741,3019.18 76,1,ABAB,T,9.400754,12097.5 76,2,ABAB,R,9.448918,12694.42 76,3,ABAB,T,9.305581,10999.24 76,4,ABAB,R,9.149158,9406.52 77,1,BABA,R,7.017058,1115.5 77,2,BABA,T,7.016879,1115.3 77,3,BABA,R,7.013718,1111.78 77,4,BABA,T,7.76337,2352.82 78,1,BABA,R,9.921992,20373.54 78,2,BABA,T,9.524392,13689.6 78,3,BABA,R,9.932319,20585.02 78,4,BABA,T,10.106352,24498.14 ``` ---

**Author:** ![dmbates](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmbates/32/44_2.png) [@dmbates](https://discourse.julialang.org/u/dmbates)\ **Post date:** [June 25, 2019, 4:07pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/7 "2019-06-25T16:07:14Z")

Notice the estimate of the within-Subject correlation of the random effects from MixedModels is 1.00. This is an indication that the estimate of the covariance matrix is singular. I don’t know how SAS and SPSS formulate the model and estimate the parameters because their code is proprietary. Often, and I suspect it is the case here, the inverse of the covariance matrix is used in the calculation. Naturally this becomes problematic when trying to evaluate at a singular covariance matrix. The methods in the lme4 package for R and the MixedModels package for Julia were specifically designed to avoid this problem ---

**Author:** ![dmbates](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmbates/32/44_2.png) [@dmbates](https://discourse.julialang.org/u/dmbates)\ **Post date:** [June 25, 2019, 4:10pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/8 "2019-06-25T16:10:38Z")

You probably don’t want to try to fit a model with a term like `(0 + Formulation|Period)` in it because there are only 4 levels of `Period`. You can’t expect to estimate two variances and a covariance from 4 distinct levels of `Period`. ---

**Author:** ![PharmCat](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/pharmcat/32/6953_2.png) [@PharmCat](https://discourse.julialang.org/u/PharmCat)\ **Post date:** [June 26, 2019, 3:54am UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/9 "2019-06-26T03:54:11Z")

Douglas, thank you so much for reply! I understand that term `(0 + Formulation|Period)` not appropriate in this situation ☹ I try to fit `(0 + Formulation|Subject*Period)` - it leads to error. So I make `Subject*Period` manualy and call is SP: ```julia Linear mixed model fit by REML Formula: logData ~ 1 + Period + Seq + Formulation + ((0 + Formulation) | Subject) + ((0 + Formulation) | SP) REML criterion at convergence: 530.1445130337493 Variance components: Column Variance Std.Dev. Corr. SP Formulation: R 0.0903490313 0.30058116 Formulation: T 0.0056246077 0.07499738 0.95 Subject Formulation: R 0.7276018429 0.85299580 Formulation: T 0.6862586172 0.82840728 1.00 Residual 0.1117692954 0.33431915 Number of obs: 298; levels of grouping factors: 298, 77 Fixed-effects parameters: ───────────────────────────────────────────────────────── Estimate Std.Error z value P(>|z|) ───────────────────────────────────────────────────────── (Intercept) 7.62921 0.145582 52.4047 <1e-99 Period: 2 0.0446378 0.0636371 0.701443 0.4830 Period: 3 0.00659022 0.0644218 0.102298 0.9185 Period: 4 0.0727199 0.0639714 1.13676 0.2556 Seq: BABA 0.0204706 0.197223 0.103794 0.9173 Formulation: T 0.145464 0.0465012 3.12818 0.0018 ───────────────────────────────────────────────────────── ``` maybe this fitting wrong to… For SPSS code looke like this: ```julia MIXED logData BY Period Formulation Seq Subject /CRITERIA=CIN(90) MXITER(200) MXSTEP(20) SCORING(2) SINGULAR(0.000000000001) HCONVERGE(0, RELATIVE) LCONVERGE(0.0000000000001, RELATIVE) PCONVERGE(0, RELATIVE) /FIXED=Period Formulation Sequence | SSTYPE(3) /METHOD=REML /RANDOM=Formulation | SUBJECT(Subject) COVTYPE(CSH) /REPEATED=Formulation | SUBJECT(Subject*Period) COVTYPE(DIAG) /EMMEANS=TABLES(Formulation) COMPARE REFCAT(FIRST) ADJ(LSD). ``` Ok. So. Is it possible to get CI for difference T-R from MixedModels? ---

**Author:** ![dmbates](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/dmbates/32/44_2.png) [@dmbates](https://discourse.julialang.org/u/dmbates)\ **Post date:** [June 26, 2019, 3:38pm UTC](https://discourse.julialang.org/t/mixedmodels-jl-confidence-intervals-and-repeated-measure-how-to-get/25534/10 "2019-06-26T15:38:17Z")

I’m sorry but I haven’t used either SPSS or SAS in about 40 years and never did understand the REPEATED formulation in the first place. You will need to get someone who understands what the SPSS model means.