SPSS 요인 분석을 재현 R

Question

나는 누군가가 올바른 방향으로 나를 가리킬 수 있기를 바랍니다. 우선, 나는 통계가 아니다. 나는 R을 사용하여 SPSS의 요인 분석 (PC 추출 & varimax rotation 사용) 결과를 재현하려고하는 소프트웨어 개발자입니다. 지난 주 R에 노출되었을 뿐이므로 발견하려고 노력하고 있습니다. 내 주위에. 그러나 나는 '회전'구성 요소 매트릭스 값을 계산해야 https://stats.stackexchange.com/questions/612/is-psychprincipal-function-still-pca-when-using-rotation

나는 문제없이 구성 요소 매트릭스 값을 재현 할 수 있었다 :

나는 매우 도움이되었다 2010 년이 게시물을 발견했다. 다시 말하면, 나는 varimax rotation을 사용할 필요가있다. 구성 요소 매트릭스 가치를 창출 할 수있는 마지막 줄은 다음과 같습니다 사람이 회전 구성 요소의 행렬 값을 만드는 데 사용할 올바른 구문을 도와 수 있다면

pfa.eigen$vectors [ , 1:factors ] * 
    diag (sqrt (pfa.eigen$values [ 1:factors ]), factors, factors) 

, 나는 영원히 감사 할 것이다!

확인은 - 간다 - 여기에 내가 (120 열, 31 행) 사용하고 데이터의 : 여기

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. 

을 : 여기

-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 
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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 
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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 
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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 
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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 
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-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 

내가 실행하고있어 SPSS 스크립트입니다 여기

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. 

내가 연구에 사용하는 R 코드 : 회전되지 않은 구성 요소 매트릭스 점수 - 내가 재현 할 수있어 SPSS의 출력은 이 값을 eproduce : 여기

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) 

이있는 내가 재현 할 필요가 SPSS에서 구성 요소 매트릭스 점수 '회전'

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. 

Answer 1

당신이 psych 패키지에 principal()를 사용하는 경우를, 당신은 인수를 지정할 수 있습니다

rotate "none", "varimax", "quatimax", "promax", "oblimin", "simplimax", and "cluster" are possible rotations/transformations of the solution.

참조 : Psych on Cran

도움이 될 것입니다.

data (df)

> install.packages ("psych")

> library ("psych")

> rotatedmatrix <- principal(df, nfactors = 4, rotate = "varimax", scores = TRUE)

얻으려면 요인 적재량 :

> loadings <- as.data.frame(unclass(rotatedmatrix$loadings))

그것은 너무 늦었지만 향후 참조를 위해입니다

텍스트 파일로 요인 부하량을 내보내려면 :

> capture.output(print(rotatedmatrix), file=""filename.txt")