Я надеюсь, что кто-то может указать мне в правильном направлении. Во-первых, я не статистик. Я разработчик программного обеспечения, перед которым была поставлена задача попытаться воспроизвести результаты факторного анализа SPSS (используя извлечение ПК и вращение варимакс) с помощью R. Я столкнулся с R только на прошлой неделе, поэтому я пытаюсь найти мой путь вокруг.
Я нашел очень полезный пост 2010 года: https://stats.stackexchange.com/questions/612/is-psychprincipal-function-still-pca-when-using-rotation
Мне удалось без проблем воспроизвести значения матрицы компонентов, однако мне нужно рассчитать «повернутые» значения матрицы компонентов. Опять же, мне нужно использовать вращение варимакс. Последняя строка для создания значений матрицы компонентов:
pfa.eigen$vectors [ , 1:factors ] *
diag ( sqrt (pfa.eigen$values [ 1:factors ] ), factors, factors )
Если кто-нибудь может помочь мне с правильным синтаксисом для создания значений матрицы повернутых компонентов, я буду бесконечно благодарен!
Итак, вот данные, которые я использую (120 столбцов, 31 строка):
-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
Вот скрипт spss, который я запускаю:
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.
Вот вывод из SPSS, который я могу воспроизвести - неперевернутые оценки матрицы компонентов:
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.
Вот код R, который я использую для воспроизведения этих значений:
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)
Вот «повернутые» оценки матрицы компонентов из SPSS, которые мне нужно воспроизвести:
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.
