## [1] 100 23
## IDAQ_1 IDAQ_3 IDAQ_4 IDAQ_5 IDAQ_6 IDAQ_7 IDAQ_9 IDAQ_10 IDAQ_11 IDAQ_12
## 1 3 3 1 1 5 5 5 1 5 1
## 2 4 1 3 3 2 2 2 1 3 4
## 3 3 2 2 4 3 3 3 1 2 1
## 4 1 2 4 2 4 4 1 5 1 1
## 5 1 1 2 1 3 4 5 3 2 4
## 6 4 2 4 5 2 1 2 5 3 3
## IDAQ_14 IDAQ_15 IDAQ_16 IDAQ_17 IDAQ_18 IDAQ_19 IDAQ_20 IDAQ_22 IDAQ_23
## 1 5 1 5 3 1 3 5 3 1
## 2 4 3 4 4 1 3 1 2 3
## 3 2 4 4 4 1 4 1 4 2
## 4 5 3 5 1 1 1 5 2 4
## 5 2 4 4 1 3 1 2 3 4
## 6 3 1 3 3 1 3 1 2 1
## IDAQ_24 IDAQ_25 IDAQ_26 IDAQ_27
## 1 3 3 5 5
## 2 3 1 2 1
## 3 2 1 2 3
## 4 4 4 4 5
## 5 1 4 2 2
## 6 3 2 4 2
## Minimum Rank Factor Analysis Output:
##
## Factor Structure Matrix:
##
## IDAQ_1 IDAQ_3 IDAQ_4 IDAQ_5 IDAQ_6
## IDAQ_1 0.78422440 -0.09411032 -0.014641633 0.303967523 -0.275951861
## IDAQ_3 -0.09411032 0.65923785 0.484256842 -0.150305164 0.205446543
## IDAQ_4 -0.01464163 0.48425684 0.745463476 0.001286507 0.226425347
## IDAQ_5 0.30396752 -0.15030516 0.001286507 0.722380150 -0.268504432
## IDAQ_6 -0.27595186 0.20544654 0.226425347 -0.268504432 0.803996741
## IDAQ_7 0.06868662 -0.03974926 -0.079906239 -0.209274606 0.103070140
## IDAQ_9 -0.11715846 0.30012590 0.268400843 -0.339826192 0.281278591
## IDAQ_10 0.08930328 -0.02165727 0.003697077 0.109336487 -0.119403842
## IDAQ_11 -0.05852119 0.36746284 0.215147544 0.012259063 0.223999343
## IDAQ_12 0.45772876 -0.12491449 -0.046984080 0.397923605 -0.289035533
## IDAQ_14 0.23744093 -0.04363815 0.022572238 0.025466848 -0.040314005
## IDAQ_15 -0.27929423 0.10239678 0.007603907 -0.445921478 0.114245133
## IDAQ_16 0.31715617 -0.24763087 -0.155097959 0.058109511 0.038107098
## IDAQ_17 0.51677493 -0.10901050 -0.014811249 0.287793818 -0.447984479
## IDAQ_18 -0.07306737 0.31803302 0.345645165 -0.186330763 0.092975880
## IDAQ_19 0.50928570 -0.03046108 -0.031702993 0.328427123 -0.452084170
## IDAQ_20 -0.42222393 0.20193623 0.047967335 -0.295954614 0.507042141
## IDAQ_22 0.18837316 -0.08195516 -0.093162318 0.078728142 -0.080465821
## IDAQ_23 -0.25542274 0.34970123 0.293335653 -0.112869175 0.216531242
## IDAQ_24 0.09566385 -0.07368083 -0.051595202 0.182712721 -0.003553009
## IDAQ_25 -0.46964317 0.23898083 0.055769261 -0.371168521 0.442812557
## IDAQ_26 0.31224292 -0.20866213 -0.104696356 0.068414583 -0.105737797
## IDAQ_27 -0.19740277 0.38962094 0.320281324 -0.305511042 0.482487884
## IDAQ_7 IDAQ_9 IDAQ_10 IDAQ_11 IDAQ_12
## IDAQ_1 0.0686866209 -0.11715846 0.0893032753 -0.058521190 0.45772876
## IDAQ_3 -0.0397492573 0.30012590 -0.0216572672 0.367462841 -0.12491449
## IDAQ_4 -0.0799062391 0.26840084 0.0036970769 0.215147544 -0.04698408
## IDAQ_5 -0.2092746058 -0.33982619 0.1093364865 0.012259063 0.39792360
## IDAQ_6 0.1030701397 0.28127859 -0.1194038424 0.223999343 -0.28903553
## IDAQ_7 0.7001974035 0.18169150 -0.1946127849 -0.095763870 -0.18081682
## IDAQ_9 0.1816915048 0.74949282 -0.0672879081 0.120131300 -0.21590105
## IDAQ_10 -0.1946127849 -0.06728791 0.5077967807 -0.040584621 0.13671722
## IDAQ_11 -0.0957638700 0.12013130 -0.0405846212 0.723863398 -0.16361493
## IDAQ_12 -0.1808168227 -0.21590105 0.1367172226 -0.163614935 0.64121619
## IDAQ_14 0.1719216431 -0.24017536 0.0384291311 -0.077122735 0.16977995
## IDAQ_15 0.0912856689 0.24550609 -0.0733170064 -0.032495003 -0.33489479
## IDAQ_16 0.1214608729 -0.10908091 -0.0356340941 -0.063389429 0.10215892
## IDAQ_17 -0.0588739096 -0.11775701 0.2279954245 -0.157219474 0.41949846
## IDAQ_18 -0.0003659451 0.24975473 0.0290180621 0.283823322 -0.01879822
## IDAQ_19 -0.0439775949 -0.02772731 0.0346212054 0.030755884 0.32629780
## IDAQ_20 0.2078704423 0.20093628 -0.1550890996 0.248456878 -0.38427142
## IDAQ_22 0.0415410825 0.06102951 -0.0008805267 -0.175186754 0.18224448
## IDAQ_23 -0.1412113204 0.17056461 0.0199599325 0.233219666 -0.06576360
## IDAQ_24 0.0151520326 -0.11020520 0.2082936470 -0.076767089 0.10499656
## IDAQ_25 0.0661172822 0.20201721 -0.0395727131 0.167000376 -0.43984379
## IDAQ_26 0.0889640399 0.07379297 0.2247186408 -0.007522726 0.11713687
## IDAQ_27 0.0573547131 0.43178369 0.0889410228 0.193723377 -0.26136104
## IDAQ_14 IDAQ_15 IDAQ_16 IDAQ_17 IDAQ_18
## IDAQ_1 0.23744093 -0.279294228 0.31715617 0.51677493 -0.0730673742
## IDAQ_3 -0.04363815 0.102396781 -0.24763087 -0.10901050 0.3180330244
## IDAQ_4 0.02257224 0.007603907 -0.15509796 -0.01481125 0.3456451653
## IDAQ_5 0.02546685 -0.445921478 0.05810951 0.28779382 -0.1863307625
## IDAQ_6 -0.04031400 0.114245133 0.03810710 -0.44798448 0.0929758797
## IDAQ_7 0.17192164 0.091285669 0.12146087 -0.05887391 -0.0003659451
## IDAQ_9 -0.24017536 0.245506089 -0.10908091 -0.11775701 0.2497547340
## IDAQ_10 0.03842913 -0.073317006 -0.03563409 0.22799542 0.0290180621
## IDAQ_11 -0.07712274 -0.032495003 -0.06338943 -0.15721947 0.2838233217
## IDAQ_12 0.16977995 -0.334894794 0.10215892 0.41949846 -0.0187982200
## IDAQ_14 0.84521116 -0.196836358 0.35948318 0.24247911 -0.3358610391
## IDAQ_15 -0.19683636 0.533174308 -0.15628912 -0.23446308 0.1098627094
## IDAQ_16 0.35948318 -0.156289119 0.64745548 0.30842669 -0.1447137633
## IDAQ_17 0.24247911 -0.234463081 0.30842669 0.92785257 -0.0525688762
## IDAQ_18 -0.33586104 0.109862709 -0.14471376 -0.05256888 0.7878806451
## IDAQ_19 0.07126861 -0.236617652 0.15851952 0.56007639 -0.0210347161
## IDAQ_20 -0.06512358 0.035184710 -0.12506615 -0.37904353 0.0593462370
## IDAQ_22 0.13596058 -0.038031210 0.11463677 0.13169597 -0.1384778762
## IDAQ_23 -0.14142799 0.066572275 -0.18318193 -0.18368897 0.2841822551
## IDAQ_24 0.19611132 -0.005626330 0.04370943 0.14880200 -0.2489399144
## IDAQ_25 -0.17841524 0.234822830 -0.14861921 -0.56654154 0.1006728165
## IDAQ_26 0.30220462 -0.078376160 0.26087529 0.40236972 -0.1875325346
## IDAQ_27 -0.09610608 0.271539858 -0.01599894 -0.16328475 0.2565274486
## IDAQ_19 IDAQ_20 IDAQ_22 IDAQ_23 IDAQ_24
## IDAQ_1 0.50928570 -0.42222393 0.1883731629 -0.255422739 0.095663854
## IDAQ_3 -0.03046108 0.20193623 -0.0819551612 0.349701230 -0.073680829
## IDAQ_4 -0.03170299 0.04796733 -0.0931623177 0.293335653 -0.051595202
## IDAQ_5 0.32842712 -0.29595461 0.0787281422 -0.112869175 0.182712721
## IDAQ_6 -0.45208417 0.50704214 -0.0804658215 0.216531242 -0.003553009
## IDAQ_7 -0.04397759 0.20787044 0.0415410825 -0.141211320 0.015152033
## IDAQ_9 -0.02772731 0.20093628 0.0610295085 0.170564609 -0.110205199
## IDAQ_10 0.03462121 -0.15508910 -0.0008805267 0.019959933 0.208293647
## IDAQ_11 0.03075588 0.24845688 -0.1751867539 0.233219666 -0.076767089
## IDAQ_12 0.32629780 -0.38427142 0.1822444844 -0.065763600 0.104996564
## IDAQ_14 0.07126861 -0.06512358 0.1359605820 -0.141427988 0.196111325
## IDAQ_15 -0.23661765 0.03518471 -0.0380312101 0.066572275 -0.005626330
## IDAQ_16 0.15851952 -0.12506615 0.1146367738 -0.183181935 0.043709426
## IDAQ_17 0.56007639 -0.37904353 0.1316959709 -0.183688965 0.148801995
## IDAQ_18 -0.02103472 0.05934624 -0.1384778762 0.284182255 -0.248939914
## IDAQ_19 0.92510344 -0.37024516 0.0117454542 -0.298154804 0.108565801
## IDAQ_20 -0.37024516 0.73639386 -0.0840900958 0.276391161 -0.029866031
## IDAQ_22 0.01174545 -0.08409010 0.4984351081 0.019707172 0.270893782
## IDAQ_23 -0.29815480 0.27639116 0.0197071721 0.584988390 -0.008798488
## IDAQ_24 0.10856580 -0.02986603 0.2708937823 -0.008798488 0.707128483
## IDAQ_25 -0.32667671 0.48606386 -0.2113612017 0.241653150 0.003118876
## IDAQ_26 0.30355805 -0.14781425 0.1781476157 -0.286332154 0.288789617
## IDAQ_27 -0.14690885 0.29214543 -0.0072896396 0.352582071 0.010514672
## IDAQ_25 IDAQ_26 IDAQ_27
## IDAQ_1 -0.469643166 0.312242917 -0.19740277
## IDAQ_3 0.238980827 -0.208662129 0.38962094
## IDAQ_4 0.055769261 -0.104696356 0.32028132
## IDAQ_5 -0.371168521 0.068414583 -0.30551104
## IDAQ_6 0.442812557 -0.105737797 0.48248788
## IDAQ_7 0.066117282 0.088964040 0.05735471
## IDAQ_9 0.202017214 0.073792971 0.43178369
## IDAQ_10 -0.039572713 0.224718641 0.08894102
## IDAQ_11 0.167000376 -0.007522726 0.19372338
## IDAQ_12 -0.439843793 0.117136874 -0.26136104
## IDAQ_14 -0.178415239 0.302204616 -0.09610608
## IDAQ_15 0.234822830 -0.078376160 0.27153986
## IDAQ_16 -0.148619206 0.260875288 -0.01599894
## IDAQ_17 -0.566541538 0.402369723 -0.16328475
## IDAQ_18 0.100672816 -0.187532535 0.25652745
## IDAQ_19 -0.326676712 0.303558049 -0.14690885
## IDAQ_20 0.486063863 -0.147814254 0.29214543
## IDAQ_22 -0.211361202 0.178147616 -0.00728964
## IDAQ_23 0.241653150 -0.286332154 0.35258207
## IDAQ_24 0.003118876 0.288789617 0.01051467
## IDAQ_25 0.713031787 -0.254191501 0.39549517
## IDAQ_26 -0.254191501 0.620393130 -0.02124161
## IDAQ_27 0.395495170 -0.021241605 0.83931783
##
##
## Optimal Communalities:
##
## X 1 0.7842
## X 2 0.6592
## X 3 0.7455
## X 4 0.7224
## X 5 0.8040
## X 6 0.7002
## X 7 0.7495
## X 8 0.5078
## X 9 0.7239
## X 10 0.6412
## X 11 0.8452
## X 12 0.5332
## X 13 0.6475
## X 14 0.9279
## X 15 0.7879
## X 16 0.9251
## X 17 0.7364
## X 18 0.4984
## X 19 0.5850
## X 20 0.7071
## X 21 0.7130
## X 22 0.6204
## X 23 0.8393
##
##
## Factor 1 Factor 2 Factor 3
## V 1 0.6480 -0.3038 -0.2012
## V 2 -0.4287 -0.5379 -0.0354
## V 3 -0.2859 -0.6021 -0.0466
## V 4 0.5295 -0.1597 0.2556
## V 5 -0.6180 0.0314 -0.3477
## V 6 -0.0872 0.2092 -0.4377
## V 7 -0.4245 -0.3138 -0.3028
## V 8 0.1656 -0.1726 -0.0372
## V 9 -0.3064 -0.3940 0.0017
## V 10 0.5747 -0.2434 0.1191
## V 11 0.3456 0.1093 -0.5241
## V 12 -0.3876 0.1100 -0.0727
## V 13 0.3321 0.0844 -0.4329
## V 14 0.7074 -0.3730 -0.1896
## V 15 -0.3435 -0.5386 0.1941
## V 16 0.5947 -0.4085 -0.0503
## V 17 -0.6085 0.1486 -0.2157
## V 18 0.2188 0.0245 -0.2514
## V 19 -0.4457 -0.3015 0.0821
## V 20 0.2127 0.0471 -0.3425
## V 21 -0.6852 0.1527 -0.0875
## V 22 0.4040 -0.0547 -0.4977
## V 23 -0.5553 -0.3547 -0.4050
## Warning in sqrt(d/k) * b: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d/k) * b: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d/k) * b: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in sqrt(d[i]/k) * b[, i]: Recycling array of length 1 in array-vector arithmetic is deprecated.
## Use c() or as.vector() instead.
## HULL METHOD - CAF INDEX
##
## q f g st
## 0 0.2509 253 0.0000
## 1 0.4010 230 3.8330
## 2* 0.4371 208
## 3 0.4742 187 1.6631
## 4 0.4947 167 0.0000
##
## Number of advised dimensions: 1
## * Value outside the convex Hull
##
## -----------------------------------------------
## Estimated time for the analysis: less than a minute
##
## Computing Parallel Analysis: Please wait
##
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|======= | 11%
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|============== | 22%
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|====================== | 33%
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|============================= | 44%
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|==================================== | 56%
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|=========================================== | 67%
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|=================================================== | 78%
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|========================================================== | 89%
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|=================================================================| 100%
##
## Parallel Analysis (PA) based on Minimum Rank Factor Analysis
##
## Adequacy of the Dispersion Matrix:
##
## Determinant of the matrix = 0.000475634672537
## Bartlett's statistic = 692.4 (df = 253; P = 0.000000)
## Kaiser-Meyer-Olkin (KMO) test = 0.74909 (fair)
##
## Implementation details:
##
## Correlation matrices analized: Pearson correlation matrices
## Number of random correlation matrices: 10
## Method to obtain random correlation matrices: Permutation of the raw data
##
## Item Real-data Mean of random 90 percentile of random
## % of variance % of variance % of variance
##
## 1 27.48** 9.99 10.50
## 2 11.63** 9.23 9.74
## 3 10.36** 8.62 9.28
## 4 7.37 7.90 8.30
## 5 6.67 7.45 7.77
## 6 5.43 6.91 7.10
## 7 4.59 6.36 6.50
## 8 4.22 5.84 6.05
## 9 3.49 5.44 5.57
## 10 3.28 5.05 5.28
## 11 3.04 4.64 4.86
## 12 2.58 4.13 4.23
## 13 2.06 3.79 4.02
## 14 1.64 3.24 3.45
## 15 1.44 2.80 3.08
## 16 1.33 2.42 2.56
## 17 1.17 2.00 2.26
## 18 0.99 1.60 1.83
## 19 0.72 1.17 1.42
## 20 0.24 0.71 1.01
## 21 0.16 0.42 0.60
## 22 0.12 0.28 0.38
## 23 0.00 0.00 0.00
##
## ** Advised number of factors: 3
##
## Computing time: 00:00:02