одностороннее t-тестирование с использованием бункеров в r - PullRequest
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одностороннее t-тестирование с использованием бункеров в r

0 голосов
/ 17 апреля 2020

Я хочу вычислить статистику t-критерия c переменных MarketCap, ExReturn на основе 5 группировок, предоставленных переменными ESG.quint.

Если возможно, я также хотел бы запустить регрессии с переменной Mkt-RF в качестве объясняющей переменной и ExReturn зависимой переменной. Если возможно, любые предложения о том, как запустить регрессию, основанную на квинтильных ячейках в прилагаемых данных, будут с благодарностью.

Заранее спасибо

Мои данные выглядят так: 

structure(list(ESG.quint = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 
4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5
), Date = structure(c(14640, 14668, 14699, 14729, 14760, 14790, 
14821, 14852, 14882, 14913, 14943, 14974, 15005, 15033, 15064, 
15094, 15125, 15155, 15186, 15217, 15247, 15278, 15308, 15339, 
15370, 15399, 15430, 15460, 15491, 15521, 15552, 15583, 15613, 
15644, 15674, 15705, 15736, 15764, 15795, 15825, 15856, 15886, 
15917, 15948, 15978, 16009, 16039, 16070, 16101, 16129, 16160, 
16190, 16220, 16251, 16282, 16311, 16343, 16374, 16402, 16435, 
16465, 16493, 16525, 16555, 16584, 16616, 16647, 16678, 16708, 
16738, 16769, 16800, 16829, 16860, 16891, 16920, 16952, 16982, 
17011, 17044, 17074, 17105, 17135, 17166, 17197, 17225, 17256, 
17284, 17317, 17347, 17378, 17409, 17439, 17470, 17500, 17531, 
17562, 17590, 17621, 17651, 17682, 17712, 17743, 17774, 17804, 
17835, 17865, 17896, 17927, 17955, 17986, 18016, 18047, 18077, 
18108, 18138, 18169, 18200, 18229, 18261, 18292, 14699, 14729, 
14760, 14790, 14821, 14852, 14882, 14913, 14943, 14974, 15005, 
15033, 15064, 15094, 15125, 15155, 15186, 15217, 15247, 15278, 
15308, 15339, 15370, 15399, 15430, 15460, 15491, 15521, 15552, 
15583, 15613, 15644, 15674, 15705, 15736, 15764, 15795, 15825, 
15856, 15886, 15917, 15948, 15978, 16009, 16039, 16070, 16101, 
16129, 16160, 16190, 16220, 16251, 16282, 16311, 16343, 16374, 
16402, 16435, 16465, 16493, 16525, 16555, 16584, 16616, 16647, 
16678, 16708, 16738, 16769, 16800, 16829, 16860, 16891, 16920, 
16952, 16982, 17011, 17044, 17074, 17105, 17135, 17166, 17197, 
17225, 17256, 17284, 17317, 17347, 17378, 17409, 17439, 17470, 
17500, 17531, 17562, 17590, 17621, 17651, 17682, 17712, 17743, 
17774, 17804, 17835, 17865, 17896, 17927, 17955, 17986, 18016, 
18047, 18077, 18108, 18138, 18169, 18200, 18229, 18261, 18292, 
14699, 14729, 14760, 14790, 14821, 14852, 14882, 14913, 14943, 
14974, 15005, 15033, 15064, 15094, 15125, 15155, 15186, 15217, 
15247, 15278, 15308, 15339, 15370, 15399, 15430, 15460, 15491, 
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16070, 16101, 16129, 16160, 16190, 16220, 16251, 16282, 16311, 
16343, 16374, 16402, 16435, 16465, 16493, 16525, 16555, 16584, 
16616, 16647, 16678, 16708, 16738, 16769, 16800, 16829, 16860, 
16891, 16920, 16952, 16982, 17011, 17044, 17074, 17105, 17135, 
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15736, 15764, 15795, 15825, 15856, 15886, 15917, 15948, 15978, 
16009, 16039, 16070, 16101, 16129, 16160, 16190, 16220, 16251, 
16282, 16311, 16343, 16374, 16402, 16435, 16465, 16493, 16525, 
16555, 16584, 16616, 16647, 16678, 16708, 16738, 16769, 16800, 
16829, 16860, 16891, 16920, 16952, 16982, 17011, 17044, 17074, 
17105, 17135, 17166, 17197, 17225, 17256, 17284, 17317, 17347, 
17378, 17409, 17439, 17470, 17500, 17531, 17562, 17590, 17621, 
17651, 17682, 17712, 17743, 17774, 17804, 17835, 17865, 17896, 
17927, 17955, 17986, 18016, 18047, 18077, 18108, 18138, 18169, 
18200, 18229, 18261, 18292, 14640, 14668, 14699, 14729, 14760, 
14790, 14821, 14852, 14882, 14913, 14943, 14974, 15005, 15033, 
15064, 15094, 15125, 15155, 15186, 15217, 15247, 15278, 15308, 
15339, 15370, 15399, 15430, 15460, 15491, 15521, 15552, 15583, 
15613, 15644, 15674, 15705, 15736, 15764, 15795, 15825, 15856, 
15886, 15917, 15948, 15978, 16009, 16039, 16070, 16101, 16129, 
16160, 16190, 16220, 16251, 16282, 16311, 16343, 16374, 16402, 
16435, 16465, 16493, 16525, 16555, 16584, 16616, 16647, 16678, 
16708, 16738, 16769, 16800, 16829, 16860, 16891, 16920, 16952, 
16982, 17011, 17044, 17074, 17105, 17135, 17166, 17197, 17225, 
17256, 17284, 17317, 17347, 17378, 17409, 17439, 17470, 17500, 
17531, 17562, 17590, 17621, 17651, 17682, 17712, 17743, 17774, 
17804, 17835, 17865, 17896, 17927, 17955, 17986, 18016, 18047, 
18077, 18108, 18138, 18169, 18200, 18229, 18261, 18292), class = "Date"), 
    ExReturn = c(NaN, -0.02, 0.05, 0.01, -0.05, -0.05, 0.03, 
    -0.02, 0.06, 0.01, -0.03, 0.01, 0, 0.01, -0.01, 0.01, -0.03, 
    -0.04, 0.01, -0.06, -0.03, -0.01, -0.02, -0.03, 0.03, 0.04, 
    0.02, 0, -0.03, -0.01, 0, 0.01, 0, -0.01, -0.02, 0.01, 0, 
    0.02, 0.01, -0.01, 0, -0.02, 0.02, 0.02, 0.02, 0.01, 0.02, 
    0, 0.03, 0.02, -0.01, 0.01, 0.01, 0.01, 0.01, 0, -0.04, -0.01, 
    -0.02, -0.01, 0.03, 0.05, 0.01, 0.01, 0.01, -0.01, 0.03, 
    -0.01, -0.04, 0.02, 0.03, 0.02, 0, -0.02, 0, 0.02, 0, -0.02, 
    0.04, 0.02, -0.02, -0.01, -0.01, -0.01, 0.02, 0.01, -0.02, 
    0.01, -0.02, -0.01, 0.01, -0.01, 0, 0.01, -0.02, 0, 0.01, 
    0, -0.01, -0.01, 0, 0, 0, 0, -0.03, -0.04, -0.05, -0.03, 
    0.03, 0, -0.03, 0, -0.04, -0.02, -0.01, -0.05, 0.02, 0.02, 
    -0.01, 0, 0.02, 0.07, -0.01, -0.07, 0, 0.01, -0.04, 0.01, 
    -0.01, -0.02, 0.02, 0.03, -0.01, -0.03, -0.01, -0.02, -0.04, 
    -0.03, -0.04, -0.04, 0.01, -0.01, 0.02, 0.06, 0.03, 0, -0.03, 
    -0.04, -0.03, 0.01, 0.01, 0.01, 0, -0.04, 0.01, 0.05, -0.02, 
    0.02, 0, 0, -0.01, 0.04, 0, 0.02, 0.02, 0.02, 0.01, 0.01, 
    0.03, 0, 0.01, 0.03, -0.02, 0.02, 0, -0.06, -0.02, 0, -0.02, 
    0.07, 0.03, 0.02, 0.01, 0.01, -0.02, 0.02, -0.04, -0.02, 
    0.01, 0.02, 0.01, -0.03, -0.01, 0, 0.01, 0.02, -0.04, 0.04, 
    0.01, 0, -0.01, -0.01, 0.06, 0.03, -0.02, 0.02, 0.02, 0.02, 
    0, 0.03, 0.01, 0, -0.01, -0.04, 0, 0, 0, -0.03, -0.01, -0.01, 
    -0.04, 0.03, -0.02, -0.01, -0.05, 0, -0.03, 0.05, 0.01, -0.02, 
    0.01, -0.03, -0.01, 0.01, -0.04, 0.01, 0, -0.01, 0.01, 0.03, 
    0.02, -0.01, -0.07, -0.04, 0.02, -0.05, 0.03, -0.01, -0.01, 
    0.01, 0.01, -0.02, 0, 0.01, -0.02, -0.05, -0.02, -0.04, -0.05, 
    -0.01, -0.03, -0.02, 0.01, 0, 0.03, -0.02, -0.04, -0.05, 
    0.03, 0.02, 0.01, 0, -0.03, 0, 0.01, 0.02, 0.03, 0, -0.02, 
    -0.02, 0.04, 0.02, 0, 0.03, 0.02, -0.02, 0.02, 0.04, -0.02, 
    -0.02, 0.01, -0.01, -0.01, -0.04, -0.01, -0.03, 0.01, 0, 
    0, 0.05, 0.02, 0.02, -0.01, -0.02, 0.02, -0.02, 0, 0.02, 
    0, 0.01, -0.02, -0.02, 0.01, 0.01, 0.02, -0.03, 0.04, 0.04, 
    0.01, 0, -0.01, 0.03, 0.05, 0, 0, 0.02, -0.02, 0.01, 0.01, 
    -0.02, -0.02, -0.03, -0.05, 0.01, 0.02, -0.01, 0, 0.02, -0.02, 
    0, 0.01, 0.01, -0.02, -0.06, -0.03, -0.03, 0.03, 0.02, -0.02, 
    0.02, -0.01, -0.03, -0.01, -0.03, 0.01, -0.01, 0.02, 0.04, 
    0.03, 0.04, 0, -0.07, -0.04, 0.03, -0.04, 0.03, 0.03, -0.02, 
    0.06, -0.01, -0.03, -0.01, 0.01, -0.02, -0.03, -0.01, -0.05, 
    -0.06, 0, -0.01, -0.02, 0.01, 0.01, 0, -0.01, -0.02, -0.01, 
    0.01, 0.02, 0.01, -0.02, -0.05, 0, 0, 0.02, 0.02, 0.01, 0, 
    -0.03, 0.06, 0.02, 0.03, 0.04, 0.01, -0.02, 0.02, 0.04, 0, 
    0, 0.03, -0.01, -0.02, -0.01, -0.01, -0.01, 0.03, -0.01, 
    0.02, 0.05, 0.02, 0, 0.03, -0.01, 0.02, 0, -0.02, -0.01, 
    0, -0.01, -0.04, -0.01, 0.02, -0.02, 0, -0.03, 0.04, 0.01, 
    -0.03, 0.03, 0.02, 0.03, 0.07, 0.01, 0.01, 0.05, 0, -0.02, 
    0.02, 0.01, -0.02, -0.01, -0.02, -0.01, -0.01, -0.01, -0.01, 
    -0.01, -0.01, -0.03, 0.01, -0.01, -0.02, -0.03, 0.01, -0.04, 
    0.03, 0.01, 0, 0.02, -0.02, 0, -0.01, -0.02, 0.01, 0.01, 
    0.03, 0.03, 0.01, NaN, -0.01, 0.05, 0.03, -0.05, -0.02, 0.03, 
    -0.04, 0.03, 0.04, 0.01, 0.05, 0.01, -0.01, 0, 0.01, 0, -0.05, 
    -0.01, -0.05, -0.02, 0, -0.01, 0, 0.05, 0.04, -0.03, 0, -0.02, 
    -0.02, 0.04, 0.01, 0.03, -0.01, 0, 0, 0.03, 0.05, 0.02, -0.02, 
    0.02, -0.04, 0.04, 0.03, 0.03, 0.03, -0.01, 0, 0.02, 0.04, 
    -0.02, 0.01, 0.04, -0.02, -0.03, -0.02, -0.03, 0, -0.01, 
    0.02, 0.04, 0.04, 0.04, -0.01, 0.03, -0.06, 0.05, -0.03, 
    -0.04, 0.01, 0.06, -0.01, -0.02, 0, 0.01, -0.01, 0.01, -0.05, 
    0.05, 0.01, 0.01, 0, 0, 0.03, 0.04, 0.01, 0.03, 0.04, 0.01, 
    0, 0.01, 0, 0.01, 0.01, -0.06, 0, 0.01, 0, 0, 0, 0.02, 0, 
    0.03, -0.02, -0.02, -0.05, -0.03, -0.04, 0.03, 0.03, -0.01, 
    0.03, -0.03, 0, 0.01, -0.02, -0.01, -0.01, 0.05, 0.02, -0.01
    ), MarketCap = c(6788.22, 7079.19, 4598.03, 5480.38, 4618.41, 
    3144.02, 3324.98, 1059.64, 2736.01, 2937.47, 2832.5, 2068.74, 
    2071.09, 2165.97, 499.1, 499.13, 1917.16, 547.45, 1484.01, 
    1697.49, 1249.31, 1161.33, 1014.06, 1329.12, 709.77, 1207.65, 
    1266.19, 1163.34, 1717.12, 1622.93, 1603.46, 1395.53, 1677.67, 
    1997.5, 1849.81, 1445.63, 1702.73, 1657.38, 1855.32, 1788.72, 
    2115.94, 559.09, 2257.38, 948.61, 2193.62, 2596.5, 2568.12, 
    2457.07, 2721.85, 2892.34, 2777.16, 2616.36, 2890.38, 441.5, 
    420.99, 447.4, 1177.14, 1169.72, 419.05, 3758.27, 1095.37, 
    920.31, 1214.23, 1143.88, 1299.86, 1220.71, 1354.93, 1463.34, 
    1461.72, 1479.1, 1695.54, 1712.11, 1865.37, 1475.08, 1790.13, 
    2070.56, 1697.21, 1764.14, 2143.54, 1585.71, 284.94, 1754.86, 
    1758.55, 332.08, 1861.99, 318.66, 508.72, 489.77, 540.44, 
    487.09, 523.27, 515.04, 2234.32, 2167.55, 2286.64, 1983.85, 
    1980.53, 1957.57, 2462.75, 2441.69, 1447.63, 3150.08, 2842.88, 
    3012.29, 2805.26, 2858.78, 2517.79, 1331.1, 1454.68, 1370.62, 
    1279.6, 1364.74, 1188.39, 1186.87, 1748.85, 1043.63, 1803.36, 
    1121.96, 1168.8, 1304.53, 1904.87, 15087.14, 15128.88, 15462.98, 
    12064.74, 11382.16, 12173.64, 13839.83, 14907.24, 15234.8, 
    3394.75, 1425.23, 1537.69, 2904.32, 2627.26, 1447.57, 2355.97, 
    2135.62, 589.37, 1092.05, 1160.68, 1230.05, 15709.47, 17374.01, 
    18782.76, 18295.05, 18072.22, 17696.88, 18229.11, 19062.95, 
    19615.57, 18219.92, 18073.59, 19173.5, 19440.97, 22220.9, 
    20922.23, 21032.48, 21236.53, 21185.24, 3509.02, 21386.35, 
    3998.69, 20270.13, 21039.94, 23770.39, 21881.7, 4073.81, 
    27958.84, 28380.43, 25958.89, 22945.52, 2765.33, 2648.91, 
    2896.46, 6382.79, 29981.53, 3168.61, 27979.53, 34249.42, 
    32883.61, 11551.23, 8342.21, 8952.77, 8644.4, 9287.62, 40886.59, 
    40080.95, 40670.72, 47796.27, 10494.65, 10433.29, 38241.41, 
    10317.05, 41601.58, 45025.44, 39949.46, 14114.66, 6308.74, 
    3349.05, 7610.54, 11270.68, 3380.04, 7450.19, 3490.88, 2027.67, 
    3495.45, 3222.32, 3410.21, 3623.97, 3469.06, 34136.77, 32879.43, 
    4596.94, 34581.46, 34082.17, 9002.01, 31303.14, 28786.33, 
    32599.15, 27933.34, 30509.43, 31684.25, 29589.68, 31171.28, 
    28951.03, 6052.68, 5859.24, 3518.83, 5948.66, 6341.79, 6072.73, 
    3476.95, 4145.74, 3047.36, 3096.5, 1903.47, 1816.71, 1820.89, 
    1351.2, 3821.15, 3360.74, 3262.87, 3210.14, 4457.25, 5425.24, 
    4776.65, 4491.22, 4174.87, 4418.8, 6048.79, 20445.68, 18526.54, 
    17461.55, 17890.85, 4362.38, 17307.84, 15428.93, 3314.68, 
    3532.51, 18068.2, 3330.51, 3542.03, 3462.4, 3650.96, 3563.79, 
    3505.06, 3424.6, 3540.22, 3751, 3596.29, 3514.44, 3942.06, 
    3642.68, 4109.78, 4161.59, 4742.38, 4353.45, 4961.53, 21247.01, 
    5201.95, 23794.32, 4684.82, 6049.06, 5807.53, 5164.62, 5902.54, 
    4750.89, 6884.88, 5431.41, 5849.91, 7501.92, 6860.27, 7068, 
    30388.5, 5407.61, 4631, 4415.45, 3884.95, 5100.84, 39647.43, 
    37441.64, 37383.27, 36502.04, 39119.49, 6522.88, 5984.22, 
    6918.81, 5568.73, 5065.15, 4325.44, 11756.63, 5205.61, 8133.07, 
    5488.42, 8273.11, 3472.92, 6004.93, 15686.87, 32232.23, 28277.19, 
    12610.94, 36023.4, 12274.91, 15014.91, 13034.7, 15982.93, 
    15312.29, 13459.36, 16042.47, 11498.78, 13271.44, 31860.03, 
    9540.96, 9910.91, 34386.61, 10316.95, 10150.8, 8495.03, 9360.87, 
    10714.83, 10129.79, 10564.92, 9402.63, 8946.72, 26736.45, 
    30257.4, 28732.22, 32068.05, 29084.77, 28659.03, 4483.56, 
    4485.35, 3761.74, 4794.95, 6013.84, 6035.03, 7413.99, 8331.07, 
    2839.16, 2857.72, 2540.6, 2240.77, 1694.99, 1669.42, 1586.78, 
    1902.91, 1914.53, 16758.77, 15916.99, 2728.67, 2547.86, 2638.9, 
    2680.63, 15555.74, 1919.77, 1519.11, 13704.87, 12944.16, 
    1703.85, 2225.82, 1712.69, 1616.88, 2460.8, 2372.24, 2311.44, 
    2396.14, 2193.23, 2641.63, 2482.57, 2304.77, 2712.54, 2759.91, 
    2645.78, 3081.96, 2719.04, 3145.42, 2945.8, 3367.14, 2849.49, 
    2510, 3295.8, 3254.44, 3479.56, 3719.37, 3455.17, 4710.82, 
    2344.73, 3653.37, 3953.61, 28903.71, 28922.77, 27692.12, 
    3707.19, 4207.75, 34384.67, 4785.6, 6137.78, 6423.01, 5053.05, 
    4046.56, 3867.73, 5437.4, 6003.28, 5021.78, 4909.94, 4669.88, 
    4992.02, 40555.71, 37086.69, 8224.15, 36028.97, 7798.67, 
    8348.44, 7222.55, 38931.85, 30796.85, 26329.29, 4381.6, 4228.62, 
    27583.44, 9664.65, 25599.68, 29271.86, 27920.87, 28051.37, 
    25648.48, 26053.7, 27517.01, 7915.78, 8788.53, 5288.95, 5683.22, 
    5429.89, 4757.46, 7120.78, 7215.78, 3715.18, 6013.15, 7125.3, 
    6993.55, 6875.77, 3567.06, 3497.77, 8755.85, 9500.49, 9425.31, 
    9860.46, 10252.78, 9539.14, 38373.95, 37667.55, 42190.76, 
    35818.49, 36246.08, 39513.58, 36879.16, 47732.1, 798.25, 
    911.04, 14278.97, 14340.61, 12705.01, 17154.06, 17779.49, 
    18208.17, 17057.87, 18233.44, 17534.44, 20779.09, 20310.52, 
    20193.67, 20428.55, 20132.77, 20480.14, 20107.82, 18613.66, 
    14881.71, 15837.15, 15710.01, 16394.13, 18256.12, 18673.35, 
    16380.95, 19761.84, 19032.73, 18717.18, 18574.95, 19255.01, 
    20160.57, 19693.94, 19175.72, 20724.11, 17713.94, 21933.27, 
    21903.08, 23220.59, 21509.95, 23905.71, 20778.7, 22626.24, 
    22672.35, 24550.68, 25587.29, 25860, 24622.41, 27513.46, 
    27495.09, 29747.2, 23125.05, 31383.91, 30304.73, 29035.68, 
    30017.85, 29919.38, 30423.94, 30657.97, 31516.5, 33268.1, 
    34263.61, 37510.74, 34184.53, 35720.48, 31594.21, 35234.87, 
    28653.16, 32661.46, 33081.23, 38941.89, 36873.59, 35641.02, 
    29892.49, 34284.08, 28870.38, 27755.92, 32157.47, 40125.62, 
    40538.85, 39059.46, 38196.38, 35432.56, 35548.39, 39348.29, 
    42679.34, 47525.78, 47463.58, 47884.13, 46630.1, 49177.75, 
    49531.08, 44739.25, 43026.4, 45953.22, 45237.85, 42918.32, 
    44040.73, 40993.36, 39747.62, 39476.1, 41576.47, 40470.87, 
    41477.54, 41589.63, 31626.84, 35741.59, 36547.22, 37007.11, 
    39630.74, 41660.94, 39956.68, 38687.28, 39236.93, 41001.33, 
    38993.45, 46127.37, 45333.08, 47329.05, 46803.12, 46405.4
    )), row.names = c(NA, -599L), groups = structure(list(ESG.quint = c(1, 
2, 3, 4, 5), .rows = list(1:121, 122:240, 241:359, 360:478, 479:599)), row.names = c(NA, 
-5L), class = c("tbl_df", "tbl", "data.frame"), .drop = TRUE), class = c("grouped_df", 
"tbl_df", "tbl", "data.frame")

1 Ответ

0 голосов
/ 20 апреля 2020

Я закончил с использованием функции фильтра 

desc <- CombData %>%
  group_by(ESG.quint, Date) %>%
  summarize_at(vars(ExReturn, MarketCap, `Mkt-RF`, BookMarketRatio), function(x) {mean(x, na.rm = TRUE)})
desc1 <- desc %>%
  filter(ESG.quint == 5)
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