嵌套For循环并将结果存储在？Vector？中？
我已经编写了一个嵌套for循环，它实现了我想要的功能。但是，我在循环中的数据存储不足。我已经尝试过实现不同的温度向量，但我知道我只是没有在这口井周围绞尽脑汁。这些循环将生成96个值。。。一个完整的j循环（全部4次迭代）将产生最终成为一个矩阵的12个值。因此，每次迭代最终都会创建我需要的八个表中的一个。也许向量不是最好的（也许我可以得到循环来制作我的独立矩阵？——然后扔进Kable）。很明显，我现在拥有的（用I索引到向量中）不是一个解决方案。非常感谢您的帮助嵌套For循环并将结果存储在？Vector？中？,r,R,我已经编写了一个嵌套for循环，它实现了我想要的功能。但是，我在循环中的数据存储不足。我已经尝试过实现不同的温度向量，但我知道我只是没有在这口井周围绞尽脑汁。这些循环将生成96个值。。。一个完整的j循环（全部4次迭代）将产生最终成为一个矩阵的12个值。因此，每次迭代最终都会创建我需要的八个表中的一个。也许向量不是最好的（也许我可以得到循环来制作我的独立矩阵？——然后扔进Kable）。很明显，我现在拥有的（用I索引到向量中）不是一个解决方案。非常感谢您的帮助 subgroup_years_aggr
subgroup_years_aggregated_indicators <- structure(list(Group = 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, 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, 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, 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, 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, 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), indicator = c("1", "2", "3", "4",
"5", "6", "7", "8", "1", "1", "2", "2", "3", "3", "4", "4", "5",
"5", "6", "6", "7", "7", "8", "8", "1", "1", "2", "2", "3", "3",
"4", "4", "5", "5", "6", "6", "7", "7", "8", "8", "1", "1", "2",
"2", "3", "3", "4", "4", "5", "5", "6", "6", "7", "7", "8", "8",
"1", "1", "2", "2", "3", "3", "4", "4", "5", "5", "6", "6", "7",
"7", "8", "8", "1", "1", "1", "1", "2", "2", "2", "2", "3", "3",
"3", "3", "4", "4", "4", "4", "5", "5", "5", "5", "6", "6", "6",
"6", "7", "7", "7", "7", "8", "8", "8", "8", "1", "1", "1", "2",
"2", "2", "3", "3", "3", "4", "4", "4", "5", "5", "5", "6", "6",
"6", "7", "7", "7", "8", "8", "8", "1", "1", "2", "2", "3", "3",
"4", "4", "5", "5", "6", "6", "7", "7", "8", "8", "1", "2", "3",
"4", "5", "6", "7", "8", "1", "1", "2", "2", "3", "3", "4", "4",
"5", "5", "6", "6", "7", "7", "8", "8", "1", "1", "1", "1", "2",
"2", "2", "2", "3", "3", "3", "3", "4", "4", "4", "4", "5", "5",
"5", "5", "6", "6", "6", "6", "7", "7", "7", "7", "8", "8", "8",
"8", "1", "1", "1", "1", "2", "2", "2", "2", "3", "3", "3", "3",
"4", "4", "4", "4", "5", "5", "5", "5", "6", "6", "6", "6", "7",
"7", "7", "7", "8", "8", "8", "8", "1", "1", "2", "2", "3", "3",
"4", "4", "5", "5", "6", "6", "7", "7", "8", "8", "1", "1", "1",
"1", "2", "2", "2", "2", "3", "3", "3", "3", "4", "4", "4", "4",
"5", "5", "5", "5", "6", "6", "6", "6", "7", "7", "7", "7", "8",
"8", "8", "8", "1", "1", "2", "2", "3", "3", "4", "4", "5", "5",
"6", "6", "7", "7", "8", "8", "1", "1", "2", "2", "3", "3", "4",
"4", "5", "5", "6", "6", "7", "7", "8", "8", "1", "1", "2", "2",
"3", "3", "4", "4", "5", "5", "6", "6", "7", "7", "8", "8", "1",
"1", "1", "1", "2", "2", "2", "2", "3", "3", "3", "3", "4", "4",
"4", "4", "5", "5", "5", "5", "6", "6", "6", "6", "7", "7", "7",
"7", "8", "8", "8", "8", "1", "1", "1", "1", "2", "2", "2", "2",
"3", "3", "3", "3", "4", "4", "4", "4", "5", "5", "5", "5", "6",
"6", "6", "6", "7", "7", "7", "7", "8", "8", "8", "8", "1", "1",
"1", "1", "2", "2", "2", "2", "3", "3", "3", "3", "4", "4", "4",
"4", "5", "5", "5", "5", "6", "6", "6", "6", "7", "7", "7", "7",
"8", "8", "8", "8", "1", "1", "1", "2", "2", "2", "3", "3", "3",
"4", "4", "4", "5", "5", "5", "6", "6", "6", "7", "7", "7", "8",
"8", "8", "1", "1", "1", "2", "2", "2", "3", "3", "3", "4", "4",
"4", "5", "5", "5", "6", "6", "6", "7", "7", "7", "8", "8", "8",
"1", "1", "2", "2", "3", "3", "4", "4", "5", "5", "6", "6", "7",
"7", "8", "8", "1", "1", "2", "2", "3", "3", "4", "4", "5", "5",
"6", "6", "7", "7", "8", "8", "1", "1", "2", "2", "3", "3", "4",
"4", "5", "5", "6", "6", "7", "7", "8", "8", "1", "1", "1", "1",
"2", "2", "2", "2", "3", "3", "3", "3", "4", "4", "4", "4", "5",
"5", "5", "5", "6", "6", "6", "6", "7", "7", "7", "7", "8", "8",
"8", "8", "1", "1", "2", "2", "3", "3", "4", "4", "5", "5", "6",
"6", "7", "7", "8", "8", "1", "1", "2", "2", "3", "3", "4", "4",
"5", "5", "6", "6", "7", "7", "8", "8", "1", "1", "2", "2", "3",
"3", "4", "4", "5", "5", "6", "6", "7", "7", "8", "8", "1", "1",
"2", "2", "3", "3", "4", "4", "5", "5", "6", "6", "7", "7", "8",
"8", "1", "1", "2", "2", "3", "3", "4", "4", "5", "5", "6", "6",
"7", "7", "8", "8", "1", "2", "3", "4", "5", "6", "7", "8", "1",
"1", "1", "1", "2", "2", "2", "2", "3", "3", "3", "3", "4", "4",
"4", "4", "5", "5", "5", "5", "6", "6", "6", "6", "7", "7", "7",
"7", "8", "8", "8", "8", "1", "1", "1", "1", "2", "2", "2", "2",
"3", "3", "3", "3", "4", "4", "4", "4", "5", "5", "5", "5", "6",
"6", "6", "6", "7", "7", "7", "7", "8", "8", "8", "8", "1", "1",
"2", "2", "3", "3", "4", "4", "5", "5", "6", "6", "7", "7", "8",
"8", "1", "1", "1", "1", "2", "2", "2", "2", "3", "3", "3", "3",
"4", "4", "4", "4", "5", "5", "5", "5", "6", "6", "6", "6", "7",
"7", "7", "7", "8", "8", "8", "8", "1", "1", "1", "1", "2", "2",
"2", "2", "3", "3", "3", "3", "4", "4", "4", "4", "5", "5", "5",
"5", "6", "6", "6", "6", "7", "7", "7", "7", "8", "8", "8", "8",
"1", "1", "2", "2", "3", "3", "4", "4", "5", "5", "6", "6", "7",
"7", "8", "8", "1", "1", "1", "1", "2", "2", "2", "2", "3", "3",
"3", "3", "4", "4", "4", "4", "5", "5", "5", "5", "6", "6", "6",
"6", "7", "7", "7", "7", "8", "8", "8", "8", "1", "2", "3", "4",
"5", "6", "7", "8"), ObservationOrder = c(1, 1, 1, 1, 1, 1, 1,
1, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,
3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 2, 1, 2, 1, 2, 1, 2, 1,
2, 1, 2, 1, 2, 1, 2, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,
3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 3, 4, 1, 3, 4, 1, 3,
4, 1, 3, 4, 1, 3, 4, 1, 3, 4, 1, 3, 4, 1, 3, 4, 3, 4, 3, 4, 3,
4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 3, 3, 3, 3, 3, 3, 3, 1, 2,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,
2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 3, 4, 3, 4, 3, 4,
3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 2, 1, 2, 1,
2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,
1, 2, 1, 2, 1, 2, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,
2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3,
1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3,
1, 2, 3, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4,
3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3, 4, 3,
4, 3, 4, 3, 4, 3, 4, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1,
2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,
2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1,
1, 1, 1, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
1, 2, 3, 4, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,
2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,
4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4,
1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 3, 4, 1,
2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2,
3, 4, 1, 2, 3, 4, 1, 1, 1, 1, 1, 1, 1, 1), score = c(3.5, 3.5,
2, 3, 3.5, 4, 3, 4, 2, 3, 2.5, 3, 1.5, 1.5, 0.5, 3, 2, 4, 2.5,
4, 2.5, 3.5, 3, 3.5, 3.5, 3, 2.5, 2.5, 2.5, 2, 2, 3, 3.5, 3.5,
3.5, 3.5, 3, 3, 3, 2.5, 2, 2.5, 2.5, 2.5, 1.5, 2, 1, 2, 1.5,
2.5, 2.5, 3, 1.5, 2.5, 3, 2.5, 2, 1.66666666666667, 3, 1.33333333333333,
2, 1, 1.5, 1.33333333333333, 2, 2, 2.5, 2.33333333333333, 2,
1.33333333333333, 2.5, 2.33333333333333, 2.5, 1, 4, 3.5, 3, 1,
2.5, 3, 2, 1.5, 2, 1.5, 2.5, 1.5, 3, 3, 2.5, 1.5, 4, 4, 3, 2,
3.5, 3.5, 2.5, 1.5, 3.5, 4, 3, 2, 3.5, 3.5, 1.5, 3, 1.33333333333333,
2, 2.5, 2.66666666666667, 1.5, 2.5, 1.33333333333333, 3, 2, 1.66666666666667,
2.5, 2.5, 2.66666666666667, 3, 3, 2.33333333333333, 2.5, 2, 1.66666666666667,
3.5, 3, 1.66666666666667, 2.5, 3, 3.5, 4, 2, 3, 2.5, 4, 3, 3,
3, 3, 3.5, 3, 3.5, 3.5, 2, 2.5, 2, 1.5, 1.5, 3, 1.5, 3, 3, 1.5,
3.5, 0.5, 1, 1, 1, 1.5, 3, 1, 3, 0.5, 1.5, 0, 2.5, 1, 2.5, 0.5,
2, 3.5, 3, 0.5, 3, 2.5, 2.5, 1.5, 2.5, 3, 2.5, 1, 2.5, 3, 3.5,
1, 3.5, 3, 3, 2, 3, 3, 3, 1, 3.5, 3, 3, 1.5, 3.5, 3.5, 2, 2.5,
2.5, 2, 2, 3, 2.5, 2.5, 2, 2.5, 2, 2.5, 1.5, 2, 2.5, 1.5, 2,
3, 3, 3, 2, 4, 3, 2.5, 2.5, 3.5, 2, 2, 2, 3.5, 2, 3, 3.5, 1.5,
3, 1, 2.5, 1, 3, 1, 3.5, 2, 3, 2, 2.5, 1, 3, 1.5, 1.5, 1, 3,
2.5, 1, 1.5, 2, 1.5, 2, 1, 2.5, 1, 2, 0.5, 2.5, 2, 2.5, 1.5,
3.5, 2, 3, 3, 3, 2, 1.5, 1, 2.5, 1.5, 2, 0.5, 3, 2.5, 3.5, 4,
2, 3, 2.5, 2.5, 2.5, 4, 3.5, 2.5, 3, 3, 2.5, 1.5, 3, 3, 2.5,
3, 1, 2, 1, 3, 2.5, 2.5, 3.5, 3.5, 3.5, 3, 3, 2, 3, 2.5, 2, 3,
3, 2, 2, 2.5, 3, 3, 3, 4, 4, 3.5, 3, 3, 1, 4, 1.5, 1.5, 3.5,
4, 3, 1.5, 2.5, 3, 2.5, 2, 3, 2.5, 2, 3, 3, 3, 2.5, 3, 3, 4,
2.5, 3.5, 3, 3, 2, 3, 2.5, 2.5, 3, 2.5, 3.5, 3.5, 3.5, 2, 3,
2, 2.5, 2, 2.5, 2.5, 2.5, 1.5, 3, 1.5, 3.5, 1.5, 3.5, 2, 2.5,
2.5, 3.5, 2.5, 2.5, 3, 4, 2, 2.5, 2.5, 2.5, 2.5, 3, 1.5, 2.5,
3, 2.5, 3, 3.5, 4, 3, 3, 3, 3, 2, 2, 2, 1.5, 2.5, 3, 3, 4, 3.5,
3, 3, 3.5, 3, 3, 2.5, 4, 2, 3, 2.5, 3, 2.5, 3, 3.5, 3.5, 1.5,
2, 1.5, 2, 1.5, 1.5, 2, 1, 2, 1, 1, 1, 2.5, 2.5, 2, 2.5, 2, 1,
3, 1.5, 1.5, 2, 1.5, 3, 1.5, 2.5, 3.5, 2.5, 2.5, 2, 2.5, 2, 2,
1, 2, 3, 2.5, 3, 3.5, 2, 2.5, 3.5, 2, 2, 2, 1.5, 3, 3, 3, 3,
3, 3.5, 2, 1.5, 2.5, 2, 3, 2.5, 3.5, 3, 2.5, 1.5, 2, 3, 2.5,
3.5, 2.5, 2, 2, 2, 2.5, 3.5, 2.5, 4, 3, 4, 3, 4, 3, 3, 3.5, 3,
3.5, 3, 2.5, 1, 3, 3.5, 3.5, 3.5, 3, 3, 3.5, 3, 3, 3.5, 3, 1,
2.5, 2, 3, 2, 2, 1, 2.5, 1.5, 2, 2, 3, 0, 1.5, 1.66666666666667,
3.5, 1.5, 2.5, 3, 3, 1.5, 2.5, 3, 3, 1, 2.5, 1.66666666666667,
2.5, 1.5, 3, 2.66666666666667, 2.5, 1.5, 1, 0.5, 1.5, 1.5, 2,
2, 3, 1, 3, 2.5, 2.5, 1.5, 2, 2, 1, 0.5, 1.5, 1, 2, 1, 0, 0.5,
1, 0.5, 1.5, 1, 0.5, 0.5, 0.5, 1, 2, 2.5, 2, 2.5, 2, 2.5, 3,
1.5, 3, 2, 3, 2, 2.5, 1.5, 2, 0.5, 2.5, 1, 3, 2, 2, 1.5, 3.5,
0.5, 4, 0.5, 4, 1.5, 3, 0.5, 3, 0, 3, 1.5, 1, 2.5, 2, 1.5, 2,
2, 4, 2.5, 3, 2.5, 3, 2.5, 2.5, 3, 3.5, 2.5, 3, 3.5, 3.5, 3.5,
3.5, 4, 3.5, 2.5, 3, 3.5, 2, 3, 3, 2, 2, 3.5, 2, 2.5, 3.5, 3,
3, 2.5, 3.5, 2.5, 3, 3.5, 3, 3, 3.5, 3, 3, 3, 2.5, 2.5, 2.5,
3, 3.5, 3, 3, 2, 3, 2, 2.5, 2.5, 3, 3, 2, 1, 2, 1, 3.5, 1, 2,
1.5, 3.5, 2, 3.5, 2.5, 3, 2.5, 3.5, 3, 2.5, 2, 3, 1.5, 2.5, 2,
3, 2.5, 0, 3, 1.5, 2, 1, 1.5, 2, 3.5, 1, 3.5, 1.5, 3.5, 1, 2.5,
1.5, 3, 2, 2, 3.5, 2.5, 1, 1, 4, 1, 1.5, 2, 2, 1, 1, 1.5, 2.5,
1.5, 1, 0, 2.5, 2, 2.5, 3, 2.5, 2.5, 1.5, 0.5, 2, 1.5, 2.5, 1.5,
3.5, 3, 1, 2, 2.5, 3.5, 1.5, 2, 2.5, 3, 2, 1.5, 2, 2.5, 1.5,
2, 2.5, 3, 1.5, 1.5, 2.5, 2.5, 2.5, 2, 2, 3, 1, 2, 2.5, 2, 0.5,
1, 3.5, 2.5, 2.5, 3.5, 1.5, 1, 2.5, 2, 2, 3.5, 3.5, 3.5, 3, 3.5,
2, 3.5, 3, 2.5, 2, 2, 1, 1.5, 3, 1, 1, 1.5, 2, 1, 1, 1.5, 1.5,
1, 0, 1, 1.5, 2, 0.5, 1.5, 2.5, 2.5, 1.5, 2, 1.5, 1.5, 0.5, 1,
3, 2, 0, 1, 2.5, 2, 2, 3, 2.5, 3, 2.5, 2.5)), class = c("tbl_df",
"tbl", "data.frame"), row.names = c(NA, -840L))
subgroups\u years\u aggregated\u indicators如果我理解目标的话，这里有一个dplyr
获取所有相关估计值的方法。est\u 1\u 2
是第1组和第2组之间的d，等等
res <- subgroup_years_aggregated_indicators %>%
group_by(indicator, ObservationOrder) %>%
summarise(est_1_2 = cohen.d(score[which(Group == 1)], score[which(Group == 2)], pooled=T,hedges.correction = T,na.rm=T)$estimate,
est_2_3 = cohen.d(score[which(Group == 2)], score[which(Group == 3)], pooled=T,hedges.correction = T,na.rm=T)$estimate,
est_1_3 = cohen.d(score[which(Group == 1)], score[which(Group == 3)], pooled=T,hedges.correction = T,na.rm=T)$estimate) %>%
split(., .$indicator)
> res
$`1`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 1 1 -0.347 0.416 0.191
2 1 2 0.798 -1.58 -0.532
3 1 3 -0.0298 0.309 0.307
4 1 4 -0.0940 0.309 0.188
$`2`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 2 1 0.597 0.156 0.819
2 2 2 0.0666 -0.0408 0.0256
3 2 3 -0.413 0.0478 -0.251
4 2 4 0.178 0.246 0.510
$`3`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 3 1 -0.412 0.550 0.118
2 3 2 0.361 -0.263 0.0294
3 3 3 0.353 0.769 1.09
4 3 4 0.305 -0.102 0.189
$`4`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 4 1 -0.288 0.153 -0.126
2 4 2 0.919 -1.05 -0.340
3 4 3 -0.0631 0.341 0.348
4 4 4 -0.0559 0.766 0.689
$`5`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 5 1 -0.524 0.773 0.572
2 5 2 1.18 -0.715 0.293
3 5 3 0.134 0.460 0.670
4 5 4 -0.361 1.08 0.744
$`6`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 6 1 -0.147 0.549 0.460
2 6 2 0.993 -1.64 -0.196
3 6 3 -0.0934 0.489 0.473
4 6 4 -0.543 1.04 0.248
$`7`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 7 1 -0.273 0.763 0.740
2 7 2 0.799 -0.932 -0.0997
3 7 3 -0.643 0.870 0.506
4 7 4 -0.147 0.845 0.790
$`8`
# A tibble: 4 x 5
# Groups: indicator [1]
indicator ObservationOrder est_1_2 est_2_3 est_1_3
<chr> <dbl> <dbl> <dbl> <dbl>
1 8 1 0.266 0.106 0.419
2 8 2 0.830 -0.824 0.0903
3 8 3 0.626 0.0371 0.409
4 8 4 -0.220 0.863 0.647
res%
分组依据（指标、观察顺序）%>%
总结（est_1_2=cohen.d（分数[其中（组==1）]、分数[其中（组==2）]、集合=T、对冲校正=T、na.rm=T）$估算，
est_2_3=cohen.d（分数[其中（组==2）]，分数[其中（组==3）]，集合=T，套期保值校正=T，na.rm=T）$估计，
est_1_3=cohen.d（分数[哪个（组==1）]，分数[哪个（组==3）]，集合=T，对冲校正=T，na.rm=T）$估计值）%>%
分割（，.$指标）
>res
$`1`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 1 1 -0.347 0.416 0.191
2 1 2 0.798 -1.58 -0.532
3 1 3 -0.0298 0.309 0.307
4 1 4 -0.0940 0.309 0.188
$`2`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 2 1 0.597 0.156 0.819
2 2 2 0.0666 -0.0408 0.0256
3 2 3 -0.413 0.0478 -0.251
4 2 4 0.178 0.246 0.510
$`3`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 3 1 -0.412 0.550 0.118
2 3 2 0.361 -0.263 0.0294
3 3 3 0.353 0.769 1.09
4 3 4 0.305 -0.102 0.189
$`4`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 4 1 -0.288 0.153 -0.126
2 4 2 0.919 -1.05 -0.340
3 4 3 -0.0631 0.341 0.348
4 4 4 -0.0559 0.766 0.689
$`5`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 5 1 -0.524 0.773 0.572
2 5 2 1.18 -0.715 0.293
3 5 3 0.134 0.460 0.670
4 5 4 -0.361 1.08 0.744
$`6`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 6 1 -0.147 0.549 0.460
2 6 2 0.993 -1.64 -0.196
3 6 3 -0.0934 0.489 0.473
4 6 4 -0.543 1.04 0.248
$`7`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 7 1 -0.273 0.763 0.740
2 7 2 0.799 -0.932 -0.0997
3 7 3 -0.643 0.870 0.506
4 7 4 -0.147 0.845 0.790
$`8`
#一个tibble:4x5
#分组：指标[1]
指标观测顺序est_1_2 est_2_3 est_1_3
1 8 1 0.266 0.106 0.419
2 8 2 0.830 -0.824 0.0903
3 8 3 0.626 0.0371 0.409
4 8 4 -0.220 0.863 0.647
这里有一种方法，首先准备一个包含参数化的数据帧，供以后计算，然后使用purr:：pmap
将这些参数输入到一个函数中，该函数计算每行的cohen.d估计器：
library(dplyr)
library(purrr)
library(effsize)
param_df <- subgroup_years_aggregated_indicators %>%
distinct(ind = indicator, obs_order = ObservationOrder) %>%
arrange(ind, obs_order) %>%
inner_join(
tibble(group_a = c(1,2,1), group_b = c(2,3,3)),
by = character()
)
result <- param_df %>%
mutate(
cohen = pmap(
.,
.f = function(ind, obs_order, group_a, group_b) {
tmp_d <- subgroup_years_aggregated_indicators %>%
filter(
indicator == ind,
ObservationOrder== obs_order,
Group == group_a
) %>%
pull(score)
tmp_f <- subgroup_years_aggregated_indicators %>%
filter(
indicator == ind,
ObservationOrder== obs_order,
Group == group_b
) %>%
pull(score)
tmp_cohen <- cohen.d(tmp_d, tmp_f, pooled = T, hedges.correction = T, na.rm = T)
return(tmp_cohen$estimate)
}
)
)
库（dplyr）
图书馆（purrr）
库（大小）
参数df%
不同（ind=指标，obs_顺序=观测顺序）%>%
安排（ind、obs\U订单）%>%
内螺纹联接(
tibble（组a=c（1,2,1），组b=c（2,3,3）），
by=字符（）
)
结果%
变异(
科恩=pmap(
.,
.f=功能（ind、obs、a组、b组）{
tmp_d%
滤器(
指标==ind，
观测顺序==观测顺序，
组==组a
) %>%
拉（得分）
tmp_f%
滤器(
指标==ind，
观测顺序==观测顺序，
组==组b
) %>%
拉（得分）
tmp_cohen仍在试图让我的头脑明白到底需要什么才能让这个怪物冷静下来我知道“hg10.x[[I]]”不起作用：）所以，我写了它，但我知道它错了。谢谢你的帮助！是的。（I-1）*4+j似乎能解决它：）我的数学大脑在思考这一点时死掉了。有没有办法让它填充八个独立的矩阵？如果没有，我可以从这里开始工作。我希望你可以使用建议的dplyr
方法之一（我认为Dave找到了一种比我更优雅的方法）我没有道歉，而是在R中粘贴了输出而不是输入。我现在编辑了答案。这对我帮助很大。我知道tidyverse，但不是很好。我现在可以将其应用于计算。再次感谢！
library(dplyr)
library(purrr)
library(effsize)
param_df <- subgroup_years_aggregated_indicators %>%
distinct(ind = indicator, obs_order = ObservationOrder) %>%
arrange(ind, obs_order) %>%
inner_join(
tibble(group_a = c(1,2,1), group_b = c(2,3,3)),
by = character()
)
result <- param_df %>%
mutate(
cohen = pmap(
.,
.f = function(ind, obs_order, group_a, group_b) {
tmp_d <- subgroup_years_aggregated_indicators %>%
filter(
indicator == ind,
ObservationOrder== obs_order,
Group == group_a
) %>%
pull(score)
tmp_f <- subgroup_years_aggregated_indicators %>%
filter(
indicator == ind,
ObservationOrder== obs_order,
Group == group_b
) %>%
pull(score)
tmp_cohen <- cohen.d(tmp_d, tmp_f, pooled = T, hedges.correction = T, na.rm = T)
return(tmp_cohen$estimate)
}
)
)