检查线性混合效应模型的正态性假设
Checking the normality assumption of a linear mixed effects model
我有以下 LME 代码：
IDRTlme <- lme(Score ~ Group*Condition, random = ~1|ID, data=IDRT)
我想检查正态性假设，所以我完成了以下测试：
shapiro.test(resid(IDRTlme))
这是对 lme 的输出进行 Shapiro 测试的正确方法吗？如果不是，那么非常感谢任何帮助？
如有任何帮助，将不胜感激！
IDRT 数据如下：
structure(list(ID = c("1993", "1993", "1993", "1993", "1993",
"1993", "1997", "1997", "1997", "1997", "1997", "1997", "19998",
"19998", "19998", "19998", "19998", "19998", "3122", "3122",
"3122", "3122", "3122", "3122", "3152", "3152", "3152", "3152",
"3152", "3152", "3182", "3182", "3182", "3182", "3182", "3182",
"330", "330", "330", "330", "330", "330", "354", "354", "354",
"354", "354", "354", "363", "363", "363", "363", "363", "363",
"369", "369", "369", "369", "369", "369", "370", "370", "370",
"370", "370", "370", "375", "375", "375", "375", "375", "375",
"377", "377", "377", "377", "377", "377", "378", "378", "378",
"378", "378", "378", "379", "379", "379", "379", "379", "379",
"380", "380", "380", "380", "380", "380", "381", "381", "381",
"381", "381", "381", "3862", "3862", "3862", "3862", "3862",
"3862", "3872", "3872", "3872", "3872", "3872", "3872", "388",
"388", "388", "388", "388", "388", "390", "390", "390", "390",
"390", "390", "392", "392", "392", "392", "392", "392", "393",
"393", "393", "393", "393", "393", "394", "394", "394", "394",
"394", "394", "395", "395", "395", "395", "395", "395", "396",
"396", "396", "396", "396", "396", "399", "399", "399", "399",
"399", "399", "5512", "5512", "5512", "5512", "5512", "5512",
"382", "382", "382", "382", "382", "382", "1001", "1001", "1001",
"1001", "1001", "1001", "1002", "1002", "1002", "1002", "1002",
"1002", "1003", "1003", "1003", "1003", "1003", "1003", "1004",
"1004", "1004", "1004", "1004", "1004", "1005", "1005", "1005",
"1005", "1005", "1005", "1006", "1006", "1006", "1006", "1006",
"1006", "1007", "1007", "1007", "1007", "1007", "1007", "1008",
"1008", "1008", "1008", "1008", "1008", "1009", "1009", "1009",
"1009", "1009", "1009", "1012", "1012", "1012", "1012", "1012",
"1012", "1013", "1013", "1013", "1013", "1013", "1013", "1014",
"1014", "1014", "1014", "1014", "1014", "1015", "1015", "1015",
"1015", "1015", "1015", "1016", "1016", "1016", "1016", "1016",
"1016", "1017", "1017", "1017", "1017", "1017", "1017", "1020",
"1020", "1020", "1020", "1020", "1020", "1021", "1021", "1021",
"1021", "1021", "1021", "1024", "1024", "1024", "1024", "1024",
"1024", "1025", "1025", "1025", "1025", "1025", "1025", "1026",
"1026", "1026", "1026", "1026", "1026", "1027", "1027", "1027",
"1027", "1027", "1027", "1088", "1088", "1088", "1088", "1088",
"1088", "1192", "1192", "1192", "1192", "1192", "1192", "1422",
"1422", "1422", "1422", "1422", "1422", "1492", "1492", "1492",
"1492", "1492", "1492", "1592", "1592", "1592", "1592", "1592",
"1592", "1602", "1602", "1602", "1602", "1602", "1602", "1642",
"1642", "1642", "1642", "1642", "1642", "171", "171", "171",
"171", "171", "171", "1722", "1722", "1722", "1722", "1722",
"1722", "1732", "1732", "1732", "1732", "1732", "1732", "174",
"174", "174", "174", "174", "174", "175", "175", "175", "175",
"175", "175", "1752", "1752", "1752", "1752", "1752", "1752",
"1762", "1762", "1762", "1762", "1762", "1762", "1782", "1782",
"1782", "1782", "1782", "1782", "1802", "1802", "1802", "1802",
"1802", "1802", "182", "182", "182", "182", "182", "182", "184",
"184", "184", "184", "184", "184", "1852", "1852", "1852", "1852",
"1852", "1852", "186", "186", "186", "186", "186", "186", "187",
"187", "187", "187", "187", "187", "188", "188", "188", "188",
"188", "188", "1892", "1892", "1892", "1892", "1892", "1892",
"190", "190", "190", "190", "190", "190", "192", "192", "192",
"192", "192", "192", "1924", "1924", "1924", "1924", "1924",
"1924", "193", "193", "193", "193", "193", "193", "195", "195",
"195", "195", "195", "195", "196", "196", "196", "196", "196",
"196", "197", "197", "197", "197", "197", "197", "1982", "1982",
"1982", "1982", "1982", "1982", "1992", "1992", "1992", "1992",
"1992", "1992", "19922", "19922", "19922", "19922", "19922",
"19922", "1999", "1999", "1999", "1999", "1999", "1999", "19992",
"19992", "19992", "19992", "19992", "19992", "199924", "199924",
"199924", "199924", "199924", "199924", "199945", "199945", "199945",
"199945", "199945", "199945", "199949", "199949", "199949", "199949",
"199949", "199949", "199951", "199951", "199951", "199951", "199951",
"199951", "199952", "199952", "199952", "199952", "199952", "199952",
"199j2", "199j2", "199j2", "199j2", "199j2", "199j2", "490",
"490", "490", "490", "490", "490", "181", "181", "181", "181",
"181", "181", "3812", "3812", "3812", "3812", "3812", "3812",
"199950", "199950", "199950", "199950", "199950", "199950", "191",
"191", "191", "191", "191", "191"), Condition = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
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4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
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6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
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4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("neutral",
"neutral_social", "no_money", "positive_social", "selfharm",
"win_money"), class = "factor"), Score = c(0.221611076, 0.206888887611111,
0.2319999696, 0.228521740956522, 0.206187486625, 0.220866648533333,
0.227608773956522, 0.241291721625, 0.24412006376, 0.238473741684211,
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0.254225776967742, 0.250256428333333, 0.256172385758621, 0.258117654705882,
0.218822224977778, 0.219707332097561, 0.216555555666667, 0.2150000135625,
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0.251739304826087, 0.24411129062963, 0.23662084537931, 0.195000145,
0.213087154478261, 0.186750114, 0.20812016488, 0.231142997714286,
0.21812013628, 0.252904755761905, 0.241294103411765, 0.24183999076,
0.240500022916667, 0.254666725666667, 0.247681823681818, 0.225026962054054,
0.210073992074074, 0.224115325961538, 0.222111026527778, 0.217529331970588,
0.218382267529412, 0.241200140457143, 0.237963155333333, 0.245565362434783,
0.245676636735294, 0.23380016328, 0.234291831625, 0.259823546764706,
0.2422999461, 0.251066668933333, 0.22864999775, 0.262578926526316,
0.233666698111111, 0.26059517397619, 0.24753120528125, 0.252969611787879,
0.266944355416667, 0.251853599804878, 0.257299917925, 0.240636370454545,
0.250892877571429, 0.269588218, 0.240586206793103, 0.232749988583333,
0.250000008888889, 0.258468075, 0.251829781361702, 0.257641009794872,
0.249021266404255, 0.254190473309524, 0.241840915295455, 0.282767379023256,
0.275352863647059, 0.274970496323529, 0.288102498358974, 0.274428497,
0.259230674769231, 0.26825004825, 0.217374995375, 0.247444417777778,
0.230684167421053, 0.161000013333333, 0.207999997555556, 0.254157882,
0.2496999265, 0.2209999565, 0.25166670475, 0.26030000445, 0.242944452611111,
0.306541830291667, 0.277034636172414, 0.27975015334375, 0.281909249090909,
0.31016016016, 0.279727430060606, 0.231699951633333, 0.226371377,
0.235916574833333, 0.228586147758621, 0.230285636214286, 0.240481412037037,
0.242555611666667, 0.25845010875, 0.231857257142857, 0.249205210333333,
0.247047696761905, 0.232548459967742, 0.373833229125, 0.331785661785714,
0.3506665866, 0.326083252791667, 0.357047535047619, 0.372882268,
0.282382425088235, 0.264577012615385, 0.278575868333333, 0.2825625985,
0.27254177125, 0.275424350333333, 0.256368611868421, 0.261394081606061,
0.259485006242424, 0.268589887692308, 0.241216395324324, 0.25932275083871,
0.240428498821429, 0.238733259866667, 0.23574991225, 0.244956472608696,
0.248870880516129, 0.2653124034375, 0.248142810047619, 0.264461517307692,
0.261307716153846, 0.238578984631579, 0.277941198882353, 0.229461559923077,
0.27259369190625, 0.273410173461538, 0.26809083330303, 0.284166589375,
0.282264646352941, 0.265302954272727, 0.258647063117647, 0.2565000355,
0.254529392058824, 0.232392856107143, 0.22980002155, 0.245047626047619,
0.279326019043478, 0.263157850868421, 0.286130366043478, 0.275814745185185,
0.281524956225, 0.265363541636364, 0.229149919825, 0.2269999161875,
0.214912963956522, 0.22574996955, 0.213696906212121, 0.221666583407407,
0.295230801230769, 0.294124990708333, 0.2932499945, 0.270708332333333,
0.271035696714286, 0.279705889, 0.276954639909091, 0.285567663756757,
0.278821510928571, 0.288344901206897, 0.21375000475, 0.270428637085714
), Group = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
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2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L), .Label = c("HC", "SH"), class = "factor")), row.names = c(NA,
-576L), class = c("tbl_df", "tbl", "data.frame"))
Is this the correct way to undertake the Shapiro test on the output of an lme
是的，结果表明，如果数据呈正态分布，观察到这些数据的概率极低（小于 < 2.2e-16）。
直方图和QQ图在这里也很有用：
...这都说明虽然它们是对称的，但与正态分布时的预期相比，这些残差的尾部非常短。
我有以下 LME 代码：
IDRTlme <- lme(Score ~ Group*Condition, random = ~1|ID, data=IDRT)
我想检查正态性假设，所以我完成了以下测试：
shapiro.test(resid(IDRTlme))
这是对 lme 的输出进行 Shapiro 测试的正确方法吗？如果不是，那么非常感谢任何帮助？
如有任何帮助，将不胜感激！
IDRT 数据如下：
structure(list(ID = c("1993", "1993", "1993", "1993", "1993",
"1993", "1997", "1997", "1997", "1997", "1997", "1997", "19998",
"19998", "19998", "19998", "19998", "19998", "3122", "3122",
"3122", "3122", "3122", "3122", "3152", "3152", "3152", "3152",
"3152", "3152", "3182", "3182", "3182", "3182", "3182", "3182",
"330", "330", "330", "330", "330", "330", "354", "354", "354",
"354", "354", "354", "363", "363", "363", "363", "363", "363",
"369", "369", "369", "369", "369", "369", "370", "370", "370",
"370", "370", "370", "375", "375", "375", "375", "375", "375",
"377", "377", "377", "377", "377", "377", "378", "378", "378",
"378", "378", "378", "379", "379", "379", "379", "379", "379",
"380", "380", "380", "380", "380", "380", "381", "381", "381",
"381", "381", "381", "3862", "3862", "3862", "3862", "3862",
"3862", "3872", "3872", "3872", "3872", "3872", "3872", "388",
"388", "388", "388", "388", "388", "390", "390", "390", "390",
"390", "390", "392", "392", "392", "392", "392", "392", "393",
"393", "393", "393", "393", "393", "394", "394", "394", "394",
"394", "394", "395", "395", "395", "395", "395", "395", "396",
"396", "396", "396", "396", "396", "399", "399", "399", "399",
"399", "399", "5512", "5512", "5512", "5512", "5512", "5512",
"382", "382", "382", "382", "382", "382", "1001", "1001", "1001",
"1001", "1001", "1001", "1002", "1002", "1002", "1002", "1002",
"1002", "1003", "1003", "1003", "1003", "1003", "1003", "1004",
"1004", "1004", "1004", "1004", "1004", "1005", "1005", "1005",
"1005", "1005", "1005", "1006", "1006", "1006", "1006", "1006",
"1006", "1007", "1007", "1007", "1007", "1007", "1007", "1008",
"1008", "1008", "1008", "1008", "1008", "1009", "1009", "1009",
"1009", "1009", "1009", "1012", "1012", "1012", "1012", "1012",
"1012", "1013", "1013", "1013", "1013", "1013", "1013", "1014",
"1014", "1014", "1014", "1014", "1014", "1015", "1015", "1015",
"1015", "1015", "1015", "1016", "1016", "1016", "1016", "1016",
"1016", "1017", "1017", "1017", "1017", "1017", "1017", "1020",
"1020", "1020", "1020", "1020", "1020", "1021", "1021", "1021",
"1021", "1021", "1021", "1024", "1024", "1024", "1024", "1024",
"1024", "1025", "1025", "1025", "1025", "1025", "1025", "1026",
"1026", "1026", "1026", "1026", "1026", "1027", "1027", "1027",
"1027", "1027", "1027", "1088", "1088", "1088", "1088", "1088",
"1088", "1192", "1192", "1192", "1192", "1192", "1192", "1422",
"1422", "1422", "1422", "1422", "1422", "1492", "1492", "1492",
"1492", "1492", "1492", "1592", "1592", "1592", "1592", "1592",
"1592", "1602", "1602", "1602", "1602", "1602", "1602", "1642",
"1642", "1642", "1642", "1642", "1642", "171", "171", "171",
"171", "171", "171", "1722", "1722", "1722", "1722", "1722",
"1722", "1732", "1732", "1732", "1732", "1732", "1732", "174",
"174", "174", "174", "174", "174", "175", "175", "175", "175",
"175", "175", "1752", "1752", "1752", "1752", "1752", "1752",
"1762", "1762", "1762", "1762", "1762", "1762", "1782", "1782",
"1782", "1782", "1782", "1782", "1802", "1802", "1802", "1802",
"1802", "1802", "182", "182", "182", "182", "182", "182", "184",
"184", "184", "184", "184", "184", "1852", "1852", "1852", "1852",
"1852", "1852", "186", "186", "186", "186", "186", "186", "187",
"187", "187", "187", "187", "187", "188", "188", "188", "188",
"188", "188", "1892", "1892", "1892", "1892", "1892", "1892",
"190", "190", "190", "190", "190", "190", "192", "192", "192",
"192", "192", "192", "1924", "1924", "1924", "1924", "1924",
"1924", "193", "193", "193", "193", "193", "193", "195", "195",
"195", "195", "195", "195", "196", "196", "196", "196", "196",
"196", "197", "197", "197", "197", "197", "197", "1982", "1982",
"1982", "1982", "1982", "1982", "1992", "1992", "1992", "1992",
"1992", "1992", "19922", "19922", "19922", "19922", "19922",
"19922", "1999", "1999", "1999", "1999", "1999", "1999", "19992",
"19992", "19992", "19992", "19992", "19992", "199924", "199924",
"199924", "199924", "199924", "199924", "199945", "199945", "199945",
"199945", "199945", "199945", "199949", "199949", "199949", "199949",
"199949", "199949", "199951", "199951", "199951", "199951", "199951",
"199951", "199952", "199952", "199952", "199952", "199952", "199952",
"199j2", "199j2", "199j2", "199j2", "199j2", "199j2", "490",
"490", "490", "490", "490", "490", "181", "181", "181", "181",
"181", "181", "3812", "3812", "3812", "3812", "3812", "3812",
"199950", "199950", "199950", "199950", "199950", "199950", "191",
"191", "191", "191", "191", "191"), Condition = structure(c(1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L,
2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L,
6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L,
4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L, 1L, 2L, 3L, 4L, 5L, 6L), .Label = c("neutral",
"neutral_social", "no_money", "positive_social", "selfharm",
"win_money"), class = "factor"), Score = c(0.221611076, 0.206888887611111,
0.2319999696, 0.228521740956522, 0.206187486625, 0.220866648533333,
0.227608773956522, 0.241291721625, 0.24412006376, 0.238473741684211,
0.2352000951, 0.233545574272727, 0.260041663875, 0.265705879882353,
0.254225776967742, 0.250256428333333, 0.256172385758621, 0.258117654705882,
0.218822224977778, 0.219707332097561, 0.216555555666667, 0.2150000135625,
0.218574478382979, 0.216552615184211, 0.237181836863636, 0.243347841782609,
0.236708313208333, 0.239999993733333, 0.240576936653846, 0.243055529055556,
0.425774382064516, 0.355654037846154, 0.431166807733333, 0.382972372944444,
0.5007601452, 0.48425012805, 0.226071425785714, 0.234166675111111,
0.247500022333333, 0.26374999275, 0.249636368363636, 0.192642842,
0.2230799676, 0.233000015052632, 0.246157909736842, 0.232739137565217,
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Is this the correct way to undertake the Shapiro test on the output of an lme
是的，结果表明，如果数据呈正态分布，观察到这些数据的概率极低（小于 < 2.2e-16）。
直方图和QQ图在这里也很有用：
...这都说明虽然它们是对称的，但与正态分布时的预期相比，这些残差的尾部非常短。