我希望有人能指出我正确的方向。首先，我不是统计学家。我是一名软件开发人员，我的任务是尝试使用 R 重现 SPSS 因子分析的结果(使用 PC 提取和方差最大旋转)。我在过去一周只接触过 R，所以我试图找到我的方式。
我确实发现 2010 年的这篇文章非常有帮助:https://stats.stackexchange.com/questions/612/is-psychprincipal-function-still-pca-when-using-rotation
我能够毫无问题地重现组件矩阵值，但是我需要计算“旋转”的组件矩阵值。同样，我需要使用最大方差旋转。创建组件矩阵值的最后一行是:
pfa.eigen$vectors [ , 1:factors ] *
diag ( sqrt (pfa.eigen$values [ 1:factors ] ), factors, factors )
如果有人可以帮助我使用正确的语法来创建旋转的组件矩阵值，我将永远感激不尽!
好的 - 在这里 - 这是我正在使用的数据(120 列，31 行):
-36 -30 -30 -30 -25 -48 -33 -30 -20 21 -1 4 0 -27 -11 25 10 10 38 46 -4 10 -21 -15 -2 -14 -6 -5 -13 37 -16 -26 -25 -18 -14 -23 -20 -20 -14 -19 -17 -9 -12 32 -14 -22 -14 -27 -19 14 -17 -1 -25 -22 -3 0 29 3 43 59 53 35 -21 60 12 -35 -9 -29 -2 -25 15 45 35 40 -17 22 7 -11 14 -18 11 15 6 6 15 20 -5 8 3 -11 4 -2 -18 13 31 -16 1 -8 -8 3 -15 -18 28 -3 -13 -9 7 -2 2 12 28 29 21 18 64 -10 13 -1 10 10
-32 -30 -28 -33 -30 -37 -33 -27 -21 13 0 25 -13 -26 6 25 20 4 33 50 -8 11 -23 -15 -21 -17 8 -15 -16 35 -17 -22 -23 -23 -13 -26 -18 -22 -22 -26 -15 -15 -18 24 -17 -25 -24 -23 -20 12 -13 -11 -21 -25 -7 22 50 -15 47 46 45 37 -19 51 6 -30 -12 -34 -5 -29 17 37 25 41 -6 17 1 -9 14 -10 17 26 2 34 18 22 7 15 14 -16 -9 -10 -16 17 14 -19 2 -15 -8 -7 -23 -15 38 -6 -14 0 12 -7 -3 3 24 24 49 27 45 -8 22 -15 29 53
-26 -19 -22 -19 -15 -40 -28 -23 -20 23 1 10 -1 -18 -7 11 18 11 23 33 -7 21 -22 -17 -2 -11 18 -2 1 63 -12 -23 -19 -13 -13 -24 -20 -16 -20 -17 -16 -10 -10 19 -11 -21 -6 -15 -13 12 -7 -10 -14 -19 0 7 8 -4 40 35 29 35 -10 47 3 -32 -5 -26 -8 -22 3 24 16 8 -11 18 9 -5 13 4 4 21 1 5 26 4 -4 17 21 -16 10 8 -16 12 10 -18 3 -10 -13 -1 -16 -17 44 14 -14 -7 8 -9 8 6 17 8 18 4 36 0 10 -6 -6 26
-24 -20 -29 -22 -22 -41 -22 -26 -18 15 -2 11 2 -9 1 15 10 10 26 27 -7 17 -17 -12 -2 -12 16 -2 -8 42 -11 -23 -20 -11 -13 -20 -18 -22 -18 -21 -19 -8 -6 17 -7 -15 -8 -14 -15 9 -9 -2 -19 -15 -5 2 16 -6 41 44 30 29 -18 57 -4 -25 -6 -25 -6 -20 3 22 20 4 -11 11 5 -7 11 -3 11 15 3 9 30 2 -3 18 25 -14 8 -7 -14 3 0 -19 2 -13 -11 -5 -19 -18 39 4 -13 0 2 -10 9 1 32 22 28 30 39 -7 2 -6 22 36
-25 -26 -28 -23 -13 -38 -26 -21 -14 4 -3 -11 -15 -21 -4 13 18 7 18 42 -11 -1 -15 -12 -9 -14 4 -5 -8 46 -16 -17 -17 -20 -11 -20 -19 -13 -12 -12 -14 -14 -17 18 -6 -16 -7 -16 -14 13 -15 -9 -11 -18 -1 -3 32 -13 45 59 37 32 -16 43 4 -26 -7 -21 -8 -22 21 43 26 27 -8 10 6 -7 4 -13 11 6 5 13 1 13 0 7 14 -13 -1 0 -11 15 22 -11 3 -3 -11 2 -12 -15 37 2 -11 -4 7 1 6 11 32 27 6 27 41 -10 16 -7 1 13
32 2 43 41 33 61 1 23 -13 19 17 16 -2 -13 18 -9 16 -12 -5 -10 18 21 -9 -5 -12 -12 -4 -14 -13 -1 -8 -15 -13 -16 -10 -14 -11 -14 -12 -13 -6 8 -8 -4 6 -4 9 -10 -10 20 -2 6 -10 27 4 -8 -12 -13 -10 -15 -14 55 -10 52 7 -17 5 -13 -4 -14 -9 -13 -8 -11 8 6 3 3 7 -3 1 6 -16 6 12 -9 -12 42 26 -13 -5 -6 -9 -8 -8 -7 -3 -9 -5 -7 -8 -8 6 -1 -8 -8 -2 -11 -10 -16 -11 -7 8 -9 -12 8 9 -2 -8 15
18 14 18 38 34 12 45 47 -21 -5 4 -12 35 44 -7 -26 -10 -10 -16 -13 18 7 21 17 -13 -16 -22 -22 -14 -18 -17 -3 38 -4 1 29 0 -11 -9 -17 -4 28 0 15 43 35 41 35 17 -17 -18 -16 -21 27 10 -19 -22 -11 -18 -20 -18 -21 36 -26 -15 38 2 30 -11 24 -5 -8 -13 0 6 -5 -3 5 -4 -11 -5 -21 -24 -24 -17 -21 -9 -13 11 -14 -6 -12 -10 -15 -5 -14 -14 -13 -4 -13 -13 14 -24 -22 48 27 -16 -7 8 -13 14 -5 20 23 -12 -1 -7 -15 -16 1
19 20 19 11 27 34 42 29 -9 39 20 16 31 20 9 -13 2 -2 17 -3 26 51 45 30 -21 -13 -18 -21 -20 -22 -18 -22 -22 -22 -18 -21 -20 -22 -18 -22 -14 -11 1 15 36 23 27 23 30 -5 -8 -6 -4 10 0 -18 0 -16 -11 -5 3 -27 1 -26 -20 -23 9 -3 -10 -4 -1 -6 -3 -7 25 40 36 39 48 6 9 -13 -19 -23 -17 7 -24 27 67 -23 -13 -13 -18 -8 23 -17 -19 -4 11 -20 -20 -17 -9 -16 8 -10 -3 -21 7 -19 -12 -7 -22 -15 -11 -7 4 -7 -19 -19
1 24 32 22 27 42 46 44 -10 19 36 7 16 -2 0 7 6 -14 -10 -10 15 29 24 13 -12 -12 -13 -16 -14 -14 -13 -20 -10 -17 -11 -18 -13 -11 -7 -15 -7 19 10 7 40 38 36 40 45 -5 -17 -7 -12 13 -2 -5 -12 -13 -10 -15 -16 -13 -2 -16 -12 -21 -1 -18 -14 -15 -11 -13 -16 -10 8 4 10 12 20 2 0 -10 -16 -16 -9 -2 -11 30 72 -13 -5 -8 -8 -14 -8 0 -13 -7 4 -9 -12 -11 -14 -11 20 1 -6 -10 -5 -14 -14 -10 -2 -3 -14 -6 -2 -11 -14 16
-36 -22 -58 -46 -77 -49 -47 -57 -19 -20 -37 -12 -10 -16 -19 -16 -19 15 -16 -25 -21 -25 -14 10 44 27 50 54 47 -36 41 56 41 51 -10 46 43 53 48 45 43 -44 2 -37 -39 -26 -40 -31 -25 29 -6 -6 35 -36 8 -1 7 5 -1 -12 -25 -24 19 -32 34 3 4 -18 32 -10 13 -4 4 -19 -8 -18 -7 2 -18 19 18 30 43 37 -24 11 37 -42 -33 36 21 47 -5 -13 -8 34 34 48 -2 3 31 -23 -27 23 -4 -1 4 41 13 21 -20 -24 -27 -15 -21 10 -9 24 -10 5
33 16 17 40 32 28 16 21 -10 -11 -22 -12 -17 -16 -15 -23 -16 2 -15 -18 9 -12 -4 -9 6 -6 9 3 -2 44 45 27 29 18 44 19 30 24 14 5 19 33 24 -18 12 -1 14 -7 -13 9 15 6 19 38 11 -22 -20 -9 -17 -22 -15 -15 -5 -17 -1 -20 19 -21 -1 -15 -6 -17 -12 -14 -4 -15 -15 -10 -13 2 -13 -14 -14 -19 -26 -22 9 10 -16 7 9 3 -5 10 -8 10 16 -1 -7 2 -5 -15 6 8 -19 -18 6 8 -13 -4 -11 -10 -16 -4 -14 3 -5 14 -14 -21
10 30 22 11 37 36 36 18 -4 -3 38 0 -11 -5 -10 -2 7 -12 -7 -8 4 0 0 -9 -18 -7 -16 -14 -9 -14 -15 -16 -13 -20 -7 -16 -13 -9 -14 -15 -12 1 -9 6 48 28 35 39 35 -8 5 -14 6 7 6 5 -8 -11 -6 -11 -5 -15 -13 -17 -11 -13 6 2 -3 -2 -5 -9 -9 -7 6 -8 -1 -11 -7 -3 -6 -8 20 -4 0 14 -10 17 -1 -11 -7 -7 -5 -9 -10 -8 -12 -11 -5 -10 -10 98 -10 -7 64 34 -3 -15 -1 -8 -13 -8 2 -8 -13 3 -1 -2 -4 -4
2 -2 -4 6 10 14 -4 -8 22 -1 4 13 -6 18 -16 7 10 0 1 -2 4 10 -11 -10 -7 -5 -11 -11 -6 -4 -9 -10 -14 1 -11 -9 -14 -8 -7 -12 -7 -17 -12 0 -2 -6 1 8 -4 -16 6 -16 14 3 -10 36 36 -10 -11 7 6 -16 -9 -22 -13 58 -11 59 10 38 -3 -8 2 -8 -12 0 4 -10 -4 -1 -4 11 18 40 8 3 3 12 -9 -8 -4 -9 -7 -4 -11 -4 -15 -9 -10 -4 8 6 0 0 12 5 2 -6 -4 -9 2 -6 3 -3 -11 -2 2 4 7 16
9 -3 -1 8 29 -9 3 14 4 -12 -10 -3 -4 9 -16 -1 5 0 -8 -5 12 -9 -9 -9 0 5 10 4 4 -15 -3 -4 2 6 -3 20 -10 -6 -8 6 2 -17 -7 8 10 -9 12 -6 -13 -11 -8 -13 1 22 11 -5 -11 -13 -7 -9 -7 -14 -7 -15 -12 64 13 46 21 42 -2 -4 1 -3 2 -15 -8 -11 -17 -3 4 -15 10 -6 -11 -3 -2 -10 -17 7 -1 -5 13 -13 -10 4 -7 -7 -6 1 28 -9 -15 11 -7 13 -3 -2 4 12 6 7 7 -6 -3 3 -4 3 18 -4
-15 -18 -19 -21 -11 -23 -18 -25 11 14 4 12 11 9 -18 21 21 -4 32 16 -7 34 -14 -16 -18 -23 -6 -15 -16 -6 -13 -21 -21 -19 -14 -22 -19 -20 -19 -22 -18 -25 -21 24 10 -19 12 -19 -10 3 -18 -15 -14 -14 -12 38 76 -14 26 33 45 -21 -16 -20 -13 15 -9 31 -2 23 -7 8 14 3 -10 11 5 -9 19 16 2 25 38 41 30 25 8 25 41 -17 -8 -9 -15 0 -3 -21 -16 -18 -12 -10 -2 7 37 3 -9 6 -1 -17 -3 -12 10 21 12 4 22 5 2 -6 -2 8
62 -5 15 14 29 22 5 10 -14 14 -3 16 15 27 -11 -5 27 -13 1 2 17 6 -8 -13 -10 -8 -9 -13 -11 -15 -8 -13 -12 -11 -9 -10 -11 -11 -12 -14 -6 -15 -11 3 17 -11 14 -11 -9 -12 -9 -3 -4 32 -1 11 -11 -9 -4 -13 -14 -12 -7 -15 -4 41 0 54 1 38 -9 -9 -2 -5 -7 7 -2 -9 5 6 10 5 -12 0 40 -9 -8 56 -4 -9 -5 -9 -8 -7 -4 -14 -11 -8 -7 -9 -7 1 -10 -6 0 7 -2 -13 -6 -14 -5 9 14 -2 -11 5 5 -3 11 3
12 12 15 8 49 42 7 15 -15 -8 51 7 -12 -5 -11 -1 29 -13 -4 -7 15 4 -7 -10 -6 -8 -8 -10 -9 -12 -10 -9 -11 -12 -8 -11 -8 -7 -11 -9 -8 -15 -5 10 1 22 4 48 15 -8 3 -9 -5 13 3 21 -12 -11 -12 -13 -12 -12 -11 -13 -7 -12 3 0 -7 -1 -4 -7 -8 -5 3 -2 -1 -10 -1 0 -3 -2 -5 5 20 3 -8 38 -12 -7 -7 -7 -1 -11 -8 -2 -9 -7 -6 -8 -4 100 -13 -5 18 40 -5 -10 -6 -4 -9 -6 -1 -9 -6 3 -4 0 -5 7
29 -7 -19 -16 -8 -14 -10 -14 -10 -9 7 -10 0 11 -9 -5 -3 0 -6 -8 -5 3 -6 1 -2 -8 -8 -10 0 -8 5 -2 27 4 21 29 12 10 4 0 3 7 -2 -8 -12 35 -2 27 34 -14 -4 -11 9 -9 11 18 -11 -4 -11 -11 -11 -12 -2 -13 -9 49 20 25 0 33 -11 -9 -7 -6 -10 -8 -9 -9 -8 -3 -6 -6 -16 -10 -17 -13 -1 9 -10 2 0 -2 -2 -6 -12 3 -3 -4 -6 -2 -2 70 -10 4 26 20 -3 -5 -1 -5 -4 2 6 -5 -9 3 -2 3 6 -6
-1 7 -14 2 -4 -49 17 2 -14 -25 20 -25 -10 8 -18 -8 -31 6 -16 -17 -5 -26 14 46 21 30 -13 34 22 -24 11 43 15 39 64 53 17 8 18 32 17 -4 19 -18 11 41 16 29 45 -28 -11 -19 45 -10 11 -14 -22 18 -22 -17 -26 -28 9 -27 -23 -17 17 -14 0 1 -1 -9 -11 -7 -9 -23 -15 -10 -18 -9 -17 -19 -12 -10 -34 -21 16 -41 -28 19 -2 -4 28 -15 -16 0 -15 15 -12 -14 0 25 -24 -12 57 50 -6 26 47 10 -1 -10 -10 -15 -8 -1 -10 7 -15 -23
0 -17 -20 -18 -28 -12 -14 -19 59 -11 -10 -12 -9 8 -9 25 -9 13 -7 -6 -4 -11 -5 -2 -2 0 -4 -4 2 -7 45 1 8 11 -4 -1 7 -8 3 8 0 -1 9 -9 -10 -11 -9 -1 -9 -8 15 12 19 2 4 1 -9 -1 2 -4 -2 -10 -6 -14 -8 45 19 20 7 24 -7 -3 -2 -5 -8 -13 0 -6 -10 29 -2 -4 -9 -10 -9 -5 7 -14 -7 10 -5 -3 16 31 5 -4 2 -7 -5 23 21 -5 35 11 -6 -11 -5 0 -12 -2 2 10 -2 -2 -10 -4 -7 -3 7 -4
-13 -4 -23 -6 5 -31 -7 -9 33 -20 -16 -16 -16 11 -18 12 -16 0 -15 -17 -13 -15 -14 -12 30 49 -13 26 30 -17 6 41 -9 40 -7 6 -1 8 31 24 2 -18 18 -19 1 -7 -1 -13 -9 -18 -16 -15 22 -14 -6 10 11 46 -10 -10 -14 -20 -15 -20 -15 46 -11 49 0 40 -12 -11 -9 -18 -12 -20 -12 -17 -18 -8 -14 -1 22 27 -19 -10 26 -28 -17 32 14 26 30 1 -11 24 -5 17 -11 22 38 -14 -13 -3 10 8 -8 23 12 16 2 -16 -15 -11 -13 8 -11 -4 -6 -10
47 24 54 52 38 62 54 34 -17 16 -2 9 18 -5 32 -18 0 -16 -10 -15 4 13 3 -2 -15 -12 1 -18 -18 -17 -9 -17 -16 -17 -11 -19 -11 -14 -17 -15 -13 18 14 -8 -5 24 8 7 -4 1 36 24 -13 6 -5 -12 -10 -12 -12 -15 -16 51 7 36 49 -20 -4 -21 -14 -19 -14 -15 -14 -13 2 4 -8 8 18 6 17 -9 -11 -17 12 -9 -16 23 20 -16 -8 -8 -11 -12 -12 9 -5 -11 61 -13 -10 -11 -15 -5 -13 -16 -5 -14 -12 -15 -10 -11 -6 2 -14 -2 -1 -1 -9 -14
15 44 57 25 22 50 36 37 25 -4 14 1 3 6 72 -16 0 -12 -10 -4 18 -9 55 15 -13 -10 -15 -19 -13 -17 -17 -19 -14 -23 -11 -20 -16 -9 -12 -16 -11 27 5 9 6 14 9 20 5 16 23 19 -16 31 -10 -14 -12 43 -15 -14 -11 -2 -6 -12 -4 -24 -2 -18 -9 -19 0 -12 -9 -1 40 11 15 35 3 -5 -7 -13 5 -22 4 10 -18 -29 -8 -11 -13 -10 -7 -6 7 1 -5 -11 43 -8 -6 -11 -16 -12 -16 -14 -3 -15 -12 -12 -11 -10 -13 -13 -18 -2 1 -3 0 -13
-34 23 -8 -12 -28 -19 -15 -10 39 -20 -21 -18 -15 -23 4 3 -31 10 -18 -20 -9 -31 21 4 17 20 2 21 11 -23 16 12 32 14 -11 -1 42 39 28 48 26 26 21 -22 -10 0 -5 -2 13 22 10 39 -19 -8 1 -32 -22 33 -16 -12 -3 -21 28 -21 13 -38 4 -34 1 -29 11 -10 -6 -11 22 -4 4 21 -5 5 -7 -6 15 -26 -19 11 7 -47 -24 26 -3 -4 36 26 8 36 47 17 3 44 2 -19 -23 1 -17 -31 0 14 -5 -2 -5 -7 -21 -1 -18 -3 -12 9 -5 -12
-11 0 -8 -10 -27 -8 -11 0 -14 -17 -31 -12 -16 -18 -4 -20 -35 17 -15 -28 -13 -25 -3 11 27 31 38 34 50 -27 22 60 37 40 28 53 34 51 26 16 37 26 10 -24 -13 -11 -24 -9 -8 12 32 7 4 -10 -4 -21 -13 4 -18 -25 -22 -10 4 -20 29 -16 -5 -26 6 -30 13 -5 -9 -14 4 -20 -14 -5 -28 15 -1 -10 26 -11 -31 -2 14 -38 -26 12 18 37 -3 -7 -5 13 17 47 7 5 16 -24 -25 24 -24 -25 1 23 11 26 -13 -15 -13 -14 -11 -1 -10 13 -16 -10
15 23 50 21 25 40 33 39 -14 -5 -16 7 43 43 56 -20 -20 -5 -16 -14 -1 -20 37 37 -17 -15 -6 -16 -18 -15 -6 -14 21 -13 -6 -7 10 7 0 1 1 41 14 -6 -5 17 -15 16 11 -15 11 57 -20 10 -13 -21 -16 8 -18 -17 -18 38 83 10 31 -21 -6 -24 -6 -21 -8 -2 -16 7 22 -11 -7 41 -6 -14 -11 -17 -20 -26 -13 -13 -10 -27 -17 4 -7 -13 4 -14 -18 16 10 -17 39 3 -16 -14 -15 -1 -16 -17 -8 -6 -16 -19 -12 2 -13 20 -16 -13 -3 -14 -15 -15
22 6 19 8 -5 38 -14 -8 21 -31 -31 -18 -5 2 -13 -29 -26 16 -20 -25 -9 -26 -8 11 23 26 17 26 18 60 12 50 11 25 86 42 30 17 48 41 22 20 7 -13 -20 -18 -23 -23 -16 -17 36 10 29 0 -3 -19 -20 -8 -27 -27 -27 -11 29 -20 -12 19 -8 8 9 5 -3 -15 -10 -20 -12 -21 -8 -7 -22 -7 -23 -22 -22 -13 -32 -28 25 -18 -28 40 0 -2 48 18 -7 0 -2 18 -13 18 2 -17 4 1 -18 -22 8 33 -7 9 -15 -22 -25 -17 -21 6 -6 14 -12 -22
16 -19 8 -8 -16 15 -17 -16 43 10 20 28 -1 5 -11 14 41 -9 9 -3 -7 1 -10 -11 -3 -5 -6 -13 -13 -13 -13 -15 -10 -13 -10 -15 -12 -13 -11 -12 2 -15 -9 -12 -11 -16 -16 -10 -9 -13 -3 -6 7 -7 11 64 -8 -8 -4 -12 -10 -14 -6 -15 -8 40 -7 46 14 33 -8 -12 -2 -10 -4 5 -1 -9 7 17 13 31 -11 34 52 3 -7 19 -16 -7 -4 -8 6 -7 -2 15 -13 -8 -4 -11 18 8 -12 0 1 26 4 -15 -8 -13 -5 -6 2 -6 -9 15 2 9 28 -4
-30 23 12 10 -15 1 13 23 33 10 -7 -5 6 -3 55 14 -16 -1 9 22 1 -14 40 1 -6 7 -2 -11 -12 -15 -11 -23 -8 -26 -18 -29 1 7 -6 1 3 15 15 2 -17 -16 -10 -20 -15 35 21 22 -23 8 -3 -29 -19 27 0 -4 16 -9 -7 -11 -5 -38 0 -35 -1 -27 27 15 18 39 30 14 16 43 8 -2 -2 -15 12 -34 15 24 -17 -44 -24 -1 -11 -8 2 16 43 6 25 2 15 20 -14 -21 -17 2 -22 -21 8 -17 -19 1 -17 -2 -18 -9 7 -4 5 17 16 -13
-25 -19 -31 -24 -41 -39 -22 -30 10 -29 -21 -22 -15 9 -21 1 -23 -3 -17 -22 -20 -17 -19 -2 45 34 -1 55 54 -18 52 62 34 57 10 46 48 45 41 65 25 -26 -5 -22 -19 -4 -19 -13 3 -23 -17 -12 65 -19 4 11 -20 16 -21 -17 -22 -23 7 -23 -18 37 -4 34 9 46 -6 -16 -14 -16 -20 -15 -19 -22 -19 -7 -19 6 -17 12 -24 -21 15 -35 -22 36 27 42 21 -2 -12 13 -4 43 -15 3 56 -10 -13 -6 -3 -10 6 43 12 35 -14 -16 -18 -14 -18 9 -14 -3 -4 -27
-34 -25 -37 -30 -31 -43 -34 -31 -21 15 -2 3 -4 -23 -4 19 15 6 25 40 -15 3 -20 -14 -3 -1 8 0 -5 29 -15 -18 -19 -10 -12 -18 -19 -13 -12 -18 -14 -7 -14 13 -24 -20 -16 -21 -16 12 -12 -3 -21 -24 -10 -3 24 -2 37 38 43 38 -18 58 28 -26 -15 -22 -2 -23 8 25 20 42 -10 17 9 -14 4 -10 13 7 -1 1 20 15 -8 13 3 -10 11 4 -13 11 15 -9 8 -6 -1 3 -6 -18 32 8 -19 -11 2 0 6 18 34 36 30 28 39 -2 15 -6 25 11
这是我正在运行的 spss 脚本:
FILE HANDLE X /NAME='\spsswin\auto\matrix.nrm' LRECL=155.
DATA LIST FILE=X /VAR1 TO VAR120 1-155.
DO REPEAT XTEMP=VAR1 TO VAR120.
COMPUTE XTEMP=XTEMP+200.
END REPEAT.
LIST.
CORRELATIONS VARIABLES=VAR001 TO VAR120
/PRINT=TWOTAIL NOSIG.
FACTOR VARIABLES=VAR001 TO VAR120
/CRITERIA=FACTORS(5),ITERATE(200)
/EXTRACTION=PC
/ROTATION=VARIMAX.
这是我能够重现的 SPSS 输出 - 未旋转的组件矩阵分数:
Component Matrixa
Component
1 2 3 4 5
var001 .075 -.751 -.232 .261 -.427
var002 .217 -.839 .358 .009 .154
var003 -.059 -.881 .253 .211 -.199
var004 .044 -.867 .119 .095 -.292
var005 -.193 -.855 -.147 .002 -.208
var006 -.025 -.831 .125 .370 -.242
var007 .000 -.946 .102 -.100 .023
var008 -.036 -.930 .204 -.054 -.064
var009 .295 -.002 .034 .458 .478
var010 -.875 -.126 .078 .131 .006
var011 -.498 -.514 -.381 -.094 .249
var012 -.762 -.136 -.113 .451 -.106
var013 -.320 -.604 .150 -.043 -.063
var014 .165 -.610 -.305 -.003 .074
var015 -.222 -.420 .727 .143 .086
var016 -.540 .542 -.174 .052 .380
var017 -.791 .019 -.413 .331 -.061
var018 .267 .739 .255 -.240 -.022
var019 -.848 .440 -.034 -.030 .108
var020 -.834 .463 .086 -.176 .063
var021 -.308 -.818 -.050 .070 -.003
var022 -.767 -.226 -.276 .055 .002
var023 .084 -.714 .500 -.081 .404
var024 .428 -.493 .274 -.388 .146
var025 .825 .487 -.043 -.057 .037
var026 .833 .316 -.006 -.061 .195
var027 .291 .651 .290 .175 -.328
var028 .788 .531 -.023 -.135 .036
var029 .817 .465 -.080 -.079 .039
var030 -.410 .433 .098 -.235 -.475
var031 .795 .286 .006 .067 -.122
var032 .919 .261 -.080 -.111 -.088
var033 .827 -.013 .103 -.153 -.119
var034 .895 .334 -.171 -.116 -.042
var035 .614 -.033 -.076 -.335 -.361
var036 .866 .101 -.191 -.254 -.173
var037 .905 .161 .192 -.043 -.071
var038 .882 .240 .231 .034 .015
var039 .923 .268 .074 -.043 -.002
var040 .905 .262 .072 -.060 .099
var041 .916 .195 .156 .062 -.017
var042 .146 -.560 .594 -.176 -.276
var043 .637 -.350 .459 -.104 .046
var044 -.904 -.024 -.072 -.238 .059
var045 -.193 -.744 -.307 -.241 .135
var046 .145 -.811 -.142 -.393 .085
var047 -.215 -.762 -.302 -.238 .129
var048 .034 -.796 -.219 -.290 .162
var049 .140 -.666 -.206 -.435 .297
var050 -.279 .365 .666 .221 .104
var051 .334 -.252 .551 .353 -.239
var052 .132 -.229 .824 .213 -.140
var053 .764 .158 -.441 -.015 .001
var054 .004 -.811 .034 .177 -.178
var055 .386 -.114 -.386 -.102 -.025
var056 -.296 .250 -.715 .405 .078
var057 -.593 .528 -.200 .047 .104
var058 .440 .085 .374 -.052 .487
var059 -.737 .634 .033 -.142 -.018
var060 -.731 .610 .019 -.226 .041
var061 -.784 .526 .096 -.134 .139
var062 -.574 .181 .365 -.043 -.534
var063 .470 -.350 .350 -.139 -.185
var064 -.670 .362 .250 -.155 -.414
var065 -.031 .224 .580 .201 -.410
var066 .329 -.042 -.690 .286 -.012
var067 .356 -.369 -.116 -.123 .054
var068 .181 -.135 -.769 .314 .087
var069 .547 .471 -.221 .397 .083
var070 .280 -.107 -.783 .222 .108
var071 -.171 .575 .528 -.185 .298
var072 -.655 .613 .208 -.309 .040
var073 -.644 .690 .121 -.081 .141
var074 -.679 .364 .314 -.272 .126
var075 -.052 -.613 .612 .132 .360
var076 -.857 -.006 .186 .063 .206
var077 -.664 -.153 .285 .072 .459
var078 -.088 -.502 .712 .085 .320
var079 -.801 -.221 .105 .083 .179
var080 .196 .054 -.089 .622 .132
var081 -.629 .295 .047 .289 -.041
var082 -.381 .659 -.286 .372 .032
var083 .038 .506 .046 .263 .417
var084 -.173 .587 -.562 .309 .033
var085 -.758 .115 -.156 .443 -.006
var086 -.599 .362 .238 .204 .525
var087 .661 .594 -.192 -.059 -.064
var088 -.683 -.212 -.414 .213 -.338
var089 -.671 -.269 -.096 -.105 .008
var090 .903 .305 .039 -.018 .042
var091 .492 .640 -.112 -.104 -.213
var092 .618 .559 -.039 .038 -.013
var093 .746 .029 -.005 -.052 .122
var094 -.131 .536 .369 -.013 .067
var095 -.514 .310 .477 -.063 .404
var096 .764 .039 .272 .287 .129
var097 .327 .430 .703 .099 .035
var098 .805 .397 .091 -.025 .108
var099 -.050 -.504 .601 .224 -.056
var100 .422 .386 .446 .080 .181
var101 .707 .315 -.351 .281 .150
var102 -.017 -.353 -.518 -.095 .112
var103 -.642 .527 -.044 -.101 -.224
var104 .243 .633 .185 .334 -.218
var105 .110 -.438 -.606 -.411 .254
var106 -.057 -.221 -.765 -.232 .176
var107 -.150 .692 .225 .171 -.086
var108 .815 .467 -.019 -.218 -.085
var109 .264 .293 -.374 -.615 .166
var110 .435 .752 -.017 -.262 .026
var111 -.622 .507 -.122 -.394 -.101
var112 -.764 .406 .002 -.212 -.055
var113 -.750 .259 -.285 -.185 -.239
var114 -.643 .267 .136 -.346 -.269
var115 -.719 .591 .065 -.301 -.039
var116 .458 .074 -.525 .496 -.043
var117 -.873 .244 .110 -.001 -.120
var118 .600 .244 .180 .349 .086
var119 -.479 .446 -.127 .276 .106
var120 -.690 .387 -.146 -.009 -.128
Extraction Method: Principal Component Analysis.
a 5 components extracted.
这是我用来重现这些值的 R 代码:
pfa.eigen <- eigen(cor(my.data))
pfa.eigen$values
factors <- 5
pfa.eigen$vectors[ ,1:factors] %*% diag(
sqrt(pfa.eigen$values[1:factors]), factors, factors)
以下是我需要重现的来自 SPSS 的“旋转”分量矩阵分数:
Rotated Component Matrixa
Component
1 2 3 4 5
var001 -.202 -.636 -.192 .208 -.590
var002 -.039 -.907 -.144 .091 .223
var003 -.347 -.880 .001 .099 -.149
var004 -.223 -.850 -.110 .025 -.280
var005 -.428 -.654 -.354 -.012 -.311
var006 -.338 -.804 .013 .258 -.275
var007 -.253 -.852 -.355 -.026 .007
var008 -.287 -.883 -.222 -.056 -.019
var009 .165 -.041 .025 .629 .318
var010 -.878 .086 .106 -.106 .064
var011 -.608 -.120 -.585 .033 .021
var012 -.847 .102 .140 .205 -.191
var013 -.460 -.506 -.111 -.127 -.002
var014 -.033 -.433 -.498 .194 -.153
var015 -.344 -.596 .392 -.056 .390
var016 -.368 .729 -.002 .054 .302
var017 -.812 .377 -.079 .178 -.268
var018 .523 .466 .366 -.277 .199
var019 -.658 .631 .141 -.217 .169
var020 -.602 .596 .189 -.389 .219
var021 -.542 -.606 -.313 .065 -.092
var022 -.799 .119 -.223 -.045 -.120
var023 -.109 -.787 -.121 .025 .538
var024 .342 -.620 -.246 -.208 .261
var025 .921 .228 .081 .134 .010
var026 .877 .084 -.023 .195 .147
var027 .438 .316 .651 -.029 -.122
var028 .916 .269 .081 .050 .040
var029 .911 .227 .036 .126 -.004
var030 -.192 .384 .343 -.534 -.244
var031 .812 .017 .152 .179 -.136
var032 .955 .008 -.016 .093 -.129
var033 .804 -.282 .004 -.002 -.073
var034 .953 .121 -.070 .118 -.127
var035 .641 -.197 -.107 -.249 -.322
var036 .890 -.078 -.185 -.031 -.241
var037 .905 -.186 .162 .091 .003
var038 .889 -.119 .223 .166 .089
var039 .949 -.041 .093 .143 .009
var040 .932 -.027 .045 .162 .096
var041 .901 -.141 .174 .211 .014
var042 .031 -.798 .222 -.326 .042
var043 .524 -.649 .092 -.011 .219
var044 -.801 .260 -.139 -.379 .110
var045 -.355 -.439 -.660 -.063 -.051
var046 -.022 -.656 -.633 -.167 -.009
var047 -.381 -.452 -.659 -.067 -.054
var048 -.146 -.575 -.661 -.059 .007
var049 .018 -.474 -.720 -.122 .157
var050 -.186 .122 .690 -.044 .432
var051 .180 -.573 .519 .171 -.028
var052 .033 -.596 .635 -.034 .232
var053 .749 .118 -.298 .274 -.252
var054 -.266 -.743 -.154 .144 -.234
var055 .339 -.042 -.375 .107 -.231
var056 -.310 .586 -.249 .471 -.327
var057 -.415 .705 .079 -.055 .060
var058 .444 -.130 .069 .134 .586
var059 -.469 .728 .259 -.365 .127
var060 -.454 .721 .181 -.409 .181
var061 -.548 .641 .208 -.320 .282
var062 -.451 .087 .546 -.480 -.205
var063 .379 -.590 .096 -.137 -.013
var064 -.469 .333 .447 -.540 -.121
var065 .016 -.098 .747 -.155 -.066
var066 .216 .152 -.397 .504 -.432
var067 .247 -.353 -.321 .060 -.045
var068 .040 .156 -.508 .558 -.393
var069 .553 .354 .142 .522 -.106
var070 .159 .166 -.560 .511 -.369
var071 .055 .368 .420 -.277 .608
var072 -.360 .628 .277 -.512 .287
var073 -.381 .735 .306 -.261 .293
var074 -.463 .387 .238 -.462 .386
var075 -.249 -.723 .115 .121 .537
var076 -.814 .169 .123 -.126 .307
var077 -.681 -.015 .033 .007 .544
var078 -.236 -.663 .227 .022 .574
var079 -.829 -.001 -.003 -.065 .218
var080 .058 .032 .166 .647 -.052
var081 -.559 .387 .324 .045 .002
var082 -.251 .780 .227 .260 -.103
var083 .118 .456 .187 .333 .368
var084 -.071 .779 -.037 .335 -.249
var085 -.772 .349 .168 .227 -.106
var086 -.502 .436 .233 .131 .592
var087 .798 .411 .064 .087 -.129
var088 -.747 .116 -.123 .020 -.506
var089 -.683 -.012 -.189 -.201 -.005
var090 .934 .016 .078 .182 .029
var091 .669 .443 .185 -.071 -.192
var092 .730 .333 .184 .141 -.027
var093 .712 -.151 -.100 .178 .065
var094 .045 .354 .452 -.159 .299
var095 -.372 .261 .309 -.181 .648
var096 .664 -.269 .245 .402 .150
var097 .426 -.003 .685 -.043 .382
var098 .871 .109 .129 .155 .126
var099 -.226 -.683 .348 .043 .175
var100 .495 .060 .415 .088 .370
var101 .677 .243 -.097 .532 -.118
var102 -.113 -.069 -.592 .120 -.180
var103 -.418 .614 .253 -.354 -.107
var104 .348 .367 .599 .165 -.110
var105 .043 -.112 -.885 -.038 -.064
var106 -.090 .170 -.797 .071 -.212
var107 .033 .527 .549 -.036 .080
var108 .944 .191 .068 -.055 -.052
var109 .452 .376 -.493 -.329 .069
var110 .680 .552 .141 -.167 .097
var111 -.347 .648 .015 -.533 .006
var112 -.545 .548 .132 -.419 .074
var113 -.584 .510 -.050 -.373 -.242
var114 -.436 .316 .189 -.606 -.033
var115 -.429 .674 .202 -.510 .149
var116 .328 .140 -.137 .651 -.411
var117 -.736 .378 .266 -.303 .030
var118 .557 -.019 .312 .408 .085
var119 -.381 .569 .192 .157 .044
var120 -.528 .558 .131 -.213 -.103
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a Rotation converged in 20 iterations.
最佳答案
如果在 principal() 包中使用 psych，则可以指定参数:rotate"none", "varimax", "quatimax", "promax", "oblimin", "simplimax", and "cluster" are possible rotations/transformations of the solution.
引用:Psych on Cran
这可能对你有帮助。为时已晚，但以供将来引用:data (df)> install.packages ("psych")> library ("psych")> rotatedmatrix <- principal(df, nfactors = 4, rotate = "varimax", scores = TRUE)
要获得因子载荷:> loadings <- as.data.frame(unclass(rotatedmatrix$loadings))
要将因子载荷导出到文本文件:> capture.output(print(rotatedmatrix), file=""filename.txt")
关于用 R 再现 SPSS 因子分析，我们在Stack Overflow上找到一个类似的问题：https://stackoverflow.com/questions/18139292/