Table S2. Analysis of Total Variance Explained with the Analysis of Principal Components

Table S2. Analysis of total variance explained with the analysis of principal components.

Component / Initial values / Sums extraction squared loads / Sums of rotation of squared loads
Total / Variance (%) / Accumulated (%) / Total / Variance (%) / Accumulated (%) / Total / Variance (%) / Accumulated (%)
1 / 23.278 / 38.161 / 38.161 / 23.278 / 38.161 / 38.161 / 18.546 / 30.403 / 30.403
2 / 12.093 / 19.825 / 57.986 / 12.093 / 19.825 / 57.986 / 12.595 / 20.648 / 51.050
3 / 7.873 / 12.906 / 70.892 / 7.873 / 12.906 / 70.892 / 11.171 / 18.314 / 69.364
4 / 3.231 / 5.297 / 76.189 / 3.231 / 5.297 / 76.189 / 3.119 / 5.112 / 74.476
5 / 2.326 / 3.813 / 80.002 / 2.326 / 3.813 / 80.002 / 2.125 / 3.484 / 77.960
6 / 1.288 / 2.111 / 82.114 / 1.288 / 2.111 / 82.114 / 2.096 / 3.436 / 81.397
7 / 1.265 / 2.074 / 84.188 / 1.265 / 2.074 / 84.188 / 1.538 / 2.521 / 83.917
8 / 1.241 / 2.035 / 86.222 / 1.241 / 2.035 / 86.222 / 1.406 / 2.305 / 86.222
9 / .989 / 1.622 / 87.844
10 / .868 / 1.424 / 89.267
11 / .692 / 1.134 / 90.402
12 / .626 / 1.026 / 91.428
13 / .562 / .922 / 92.350
14 / .506 / .830 / 93.180
15 / .433 / .710 / 93.890
16 / .408 / .669 / 94.559
17 / .401 / .658 / 95.217
18 / .381 / .625 / 95.842
19 / .297 / .486 / 96.328
20 / .255 / .417 / 96.746
21 / .250 / .410 / 97.156
22 / .211 / .345 / 97.501
23 / .186 / .305 / 97.806
24 / .180 / .295 / 98.101
25 / .148 / .243 / 98.344
26 / .130 / .214 / 98.558
27 / .112 / .183 / 98.740
28 / .105 / .173 / 98.913
29 / .090 / .148 / 99.061
30 / .083 / .136 / 99.197
31 / .068 / .112 / 99.309
32 / .065 / .106 / 99.415
33 / .052 / .085 / 99.500
34 / .043 / .071 / 99.571
35 / .037 / .061 / 99.632
36 / .034 / .056 / 99.688
37 / .032 / .052 / 99.740
38 / .028 / .045 / 99.786
39 / .023 / .038 / 99.824
40 / .020 / .033 / 99.857
41 / .017 / .028 / 99.885
42 / .016 / .027 / 99.912
43 / .010 / .017 / 99.929
44 / .010 / .017 / 99.945
45 / .008 / .014 / 99.959
46 / .007 / .012 / 99.971
47 / .006 / .011 / 99.981
48 / .005 / .008 / 99.989
49 / .004 / .006 / 99.995
50 / .002 / .004 / 99.999
51 / .000 / .000 / 100.000
52 / .000 / .000 / 100.000
53 / 2.781E-05 / 0.000 / 100.000
54 / 1.486E-05 / 0.000 / 100.000
55 / 3.818E-07 / 0.000 / 100.000
56 / 4.854E-08 / 0.000 / 100.000
57 / 1.091E-17 / 0.000 / 100.000
58 / -9.620E-19 / 0.000 / 100.000
59 / -6.559E-18 / 0.000 / 100.000
60 / -1.627E-17 / 0.000 / 100.000
61 / -3.612E-17 / 0.000 / 100.000

A sample of 61 valiables and 131 cases was analyzed, the sample values were normalized in a scale of 0 to 1.0. The first test performed on the sample was the Principal components analysis (PCA) using was the IBM SPSS statistics v22, (https://www.ibm.com/analytics/mx/es/technology/spss/).

The following graph shows the data sample with the first three main components from table S2.

Coefficient score of the components matrix.

Variables / Component
1 / 2 / 3 / 4 / 5 / 6 / 7 / 8
CP14 / -.005 / .082 / -.001 / -.004 / -.003 / -.020 / .008 / -.010
CP18 / -.024 / -.002 / .114 / -.012 / -.003 / .052 / .018 / -.140
CP26 / -.005 / .082 / -.001 / -.004 / -.004 / -.016 / .007 / -.008
CP27 / -.004 / .081 / -.002 / -.003 / -.003 / -.013 / .006 / -.007
CP28 / -.029 / -.009 / .073 / .002 / -.065 / .025 / .000 / .276
CP29 / .022 / .001 / .059 / .038 / -.021 / -.096 / .033 / -.264
CP30 / -.018 / -.002 / .061 / .001 / .327 / -.033 / -.057 / -.104
CP31 / -.024 / -.002 / .114 / -.012 / -.003 / .052 / .018 / -.140
CP32 / -.005 / .082 / -.001 / -.004 / -.003 / -.020 / .008 / -.010
CP33 / -.005 / .081 / -.001 / -.005 / -.004 / -.015 / .006 / -.008
CP34 / -.029 / -.007 / .074 / .002 / -.065 / .025 / .001 / .274
CP35 / -.002 / .000 / -.030 / .019 / .494 / -.088 / -.198 / -.055
CP36 / -.002 / .013 / .022 / -.016 / -.153 / -.080 / .636 / -.045
CP37 / -.026 / -.004 / .106 / -.008 / -.022 / .045 / .013 / -.020
CP38 / -.024 / -.002 / .114 / -.012 / -.003 / .051 / .018 / -.140
CP39 / -.004 / .073 / -.003 / -.003 / .003 / -.053 / .009 / -.009
CP40 / -.006 / .075 / -.001 / .001 / .001 / -.040 / .008 / -.014
CP41 / -.008 / -.003 / -.011 / -.018 / .231 / -.016 / .316 / .027
CP49 / -.005 / .082 / -.001 / -.004 / -.003 / -.020 / .008 / -.010
CP50 / -.005 / .082 / -.001 / -.004 / -.003 / -.020 / .008 / -.010
CNNP19 / .083 / -.010 / -.022 / .040 / -.069 / -.205 / -.092 / -.071
CNNP27 / -.005 / .082 / -.001 / -.004 / -.003 / -.020 / .008 / -.010
CNNP28 / -.024 / -.002 / .114 / -.012 / -.003 / .052 / .018 / -.140
CNNP34 / .066 / -.008 / -.030 / .033 / -.078 / -.174 / -.058 / .001
CNP39 / .049 / -.005 / -.007 / .078 / -.055 / -.048 / .073 / -.009
DNP9 / -.018 / .008 / -.007 / .348 / .012 / -.121 / -.029 / -.013
ENNP23 / -.004 / -.010 / .010 / .077 / .031 / .195 / -.140 / .072
ENNP25 / .006 / -.013 / .001 / .083 / -.010 / .138 / -.028 / .127
AP10 / .054 / -.013 / -.016 / .142 / .058 / -.150 / .030 / -.050
AP19 / .025 / -.012 / -.008 / .145 / .114 / -.100 / .127 / .043
AP42 / -.026 / -.008 / .063 / .004 / -.017 / -.004 / .054 / .293
ANP5 / -.022 / -.018 / .003 / .365 / .007 / -.126 / -.025 / .015
ANNP30 / -.010 / .059 / .000 / -.016 / .020 / .005 / .021 / .018
BC45 / .059 / -.002 / -.020 / -.028 / .014 / -.024 / .020 / .021
1A5 / -.020 / .041 / .101 / -.015 / .013 / .049 / .021 / -.236
1A8 / -.005 / .078 / -.001 / -.003 / -.005 / .001 / .005 / -.006
1A9 / -.005 / .078 / -.001 / -.003 / -.005 / .000 / .005 / -.006
1A10 / -.004 / -.002 / .004 / .129 / -.041 / .182 / -.053 / -.115
1B6 / .004 / -.006 / -.010 / .071 / -.064 / .198 / .138 / .004
1C9 / .055 / -.008 / -.002 / -.070 / -.083 / .122 / -.021 / -.075
1C11 / .056 / -.006 / -.011 / -.041 / -.016 / .041 / .047 / -.040
1D4 / -.014 / -.022 / .011 / -.084 / -.062 / .477 / -.092 / -.036
2A3 / .054 / -.006 / -.008 / -.072 / -.037 / .095 / -.018 / -.027
2A4 / .052 / -.007 / -.009 / -.056 / -.074 / .082 / -.044 / .008
2A8 / .050 / -.009 / -.018 / -.073 / .013 / .084 / .022 / .034
2A9 / .058 / -.004 / -.021 / -.037 / .025 / -.005 / .034 / -.004
2B7 / .063 / -.004 / -.027 / -.028 / .012 / -.029 / -.021 / .010
2C1 / .055 / -.006 / -.014 / -.023 / .026 / -.023 / -.054 / .008
2C2 / .011 / .009 / .026 / .017 / -.085 / -.057 / -.064 / .289
2C5 / .073 / -.003 / -.013 / .021 / -.074 / -.139 / -.095 / -.048
2C8 / .017 / -.008 / .045 / .026 / -.072 / -.108 / .066 / .136
2C9 / -.024 / -.002 / .114 / -.012 / -.003 / .051 / .018 / -.140
2E2 / .066 / .003 / -.020 / -.006 / .024 / -.101 / .025 / -.025
2E3 / .031 / .014 / -.028 / -.020 / .063 / -.058 / .037 / .297
2E4 / .063 / .000 / -.023 / -.008 / .039 / -.065 / -.021 / .011
3C7 / .044 / .011 / -.001 / -.004 / .064 / -.067 / -.129 / .012
3C8 / .063 / -.003 / -.019 / -.006 / .021 / -.046 / .004 / -.051
3D8 / .051 / -.005 / -.011 / .049 / -.078 / .011 / .090 / -.105
3E2 / .016 / .020 / .007 / -.036 / .071 / .048 / -.135 / .124
3E3 / .010 / .015 / .017 / -.062 / .029 / .106 / .003 / .100
4D1 / .035 / .000 / .011 / -.038 / .038 / .060 / .064 / -.120
Extraction method: main component analysis.
Rotation method: Varimax with Kaiser normalization.

Coefficient score of the components matrix by k-means using the Matlab software (https://www.mathworks.com/help/stats/kmeans.html).

Extraction method: main component analysis.

Rotation method: Varimax with Kaiser normalization.

Colors in the variables shows the different clusters identified.