library(languageR)
head(dutchSpeakersDist)
## V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12
## 1 2.226 3.657 3.748 3.496 3.603 3.662 3.722 3.710 3.631 3.692 3.654 3.574
## 2 3.657 1.928 3.738 3.486 3.573 3.606 3.647 3.663 3.427 3.527 3.541 3.514
## 3 3.748 3.738 0.840 3.607 3.536 3.698 3.712 3.661 3.583 3.656 3.562 3.567
## 4 3.496 3.486 3.607 2.096 3.334 3.325 3.529 3.482 3.374 3.364 3.355 3.343
## 5 3.603 3.573 3.536 3.334 1.848 3.471 3.520 3.601 3.379 3.530 3.455 3.456
## 6 3.662 3.606 3.698 3.325 3.471 0.416 3.619 3.660 3.369 3.482 3.456 3.436
## V13 V14 V15 V16 V17 V18 V19 V20 V21 V22 V23 V24
## 1 3.729 3.807 3.741 3.586 3.624 3.690 3.634 3.706 3.662 3.658 3.614 3.765
## 2 3.575 3.574 3.543 3.614 3.555 3.659 3.575 3.751 3.631 3.530 3.381 3.793
## 3 3.595 3.763 3.679 3.599 3.667 3.611 3.692 3.724 3.649 3.552 3.537 3.749
## 4 3.453 3.389 3.374 3.393 3.394 3.362 3.439 3.545 3.398 3.401 3.330 3.617
## 5 3.491 3.651 3.518 3.469 3.389 3.475 3.567 3.569 3.430 3.463 3.338 3.740
## 6 3.422 3.522 3.489 3.494 3.482 3.292 3.471 3.567 3.467 3.515 3.275 3.784
## V25 V26 V27 V28 V29 V30 V31 V32 V33 V34 V35 V36
## 1 3.695 3.641 3.589 3.717 3.713 3.698 3.612 3.715 3.917 3.627 3.647 3.618
## 2 3.590 3.571 3.512 3.675 3.652 3.628 3.604 3.834 3.777 3.618 3.652 3.676
## 3 3.654 3.594 3.496 3.625 3.712 3.693 3.643 3.705 3.800 3.721 3.723 3.630
## 4 3.479 3.378 3.410 3.498 3.529 3.440 3.459 3.468 3.625 3.422 3.439 3.516
## 5 3.552 3.420 3.401 3.581 3.581 3.506 3.437 3.633 3.698 3.459 3.521 3.590
## 6 3.548 3.427 3.405 3.617 3.613 3.558 3.495 3.658 3.631 3.546 3.626 3.651
## V37 V38 V39 V40 V41 V42 V43 V44 V45 V46 V47 V48
## 1 3.708 3.802 3.814 3.894 3.666 3.613 3.630 3.773 3.723 3.803 3.796 3.634
## 2 3.562 3.668 3.748 3.737 3.590 3.557 3.614 3.611 3.596 3.694 3.501 3.469
## 3 3.699 3.756 3.794 3.823 3.553 3.591 3.610 3.662 3.731 3.596 3.631 3.562
## 4 3.440 3.570 3.564 3.568 3.433 3.366 3.436 3.484 3.438 3.363 3.457 3.332
## 5 3.712 3.624 3.653 3.690 3.462 3.435 3.470 3.505 3.442 3.624 3.542 3.353
## 6 3.636 3.599 3.680 3.644 3.353 3.483 3.465 3.483 3.525 3.349 3.437 3.394
## V49 V50 V51 V52 V53 V54 V55 V56 V57 V58 V59 V60
## 1 3.817 3.811 3.588 3.813 3.872 3.605 3.644 3.886 3.849 3.601 3.603 3.691
## 2 3.794 3.619 3.724 3.878 3.581 3.684 3.711 3.876 3.750 3.589 3.423 3.727
## 3 3.730 3.812 3.578 3.746 3.669 3.544 3.639 3.716 3.767 3.575 3.496 3.583
## 4 3.525 3.561 3.374 3.627 3.474 3.431 3.522 3.680 3.446 3.300 3.290 3.334
## 5 3.550 3.525 3.421 3.709 3.431 3.414 3.460 3.753 3.764 3.475 3.395 3.525
## 6 3.608 3.571 3.465 3.785 3.570 3.441 3.590 3.696 3.674 3.342 3.251 3.493
## V61 V62 V63 V64 V65 V66 V67 V68 V69 V70 V71 V72
## 1 3.653 3.728 3.508 3.873 3.740 3.696 3.623 3.611 3.667 3.632 3.667 3.700
## 2 3.548 3.695 3.469 3.750 3.667 3.693 3.645 3.692 3.584 3.557 3.563 3.684
## 3 3.584 3.734 3.537 3.807 3.643 3.663 3.606 3.757 3.786 3.752 3.622 3.710
## 4 3.405 3.501 3.362 3.630 3.512 3.501 3.496 3.451 3.446 3.380 3.359 3.408
## 5 3.434 3.646 3.427 3.631 3.450 3.494 3.498 3.505 3.540 3.488 3.444 3.484
## 6 3.489 3.506 3.363 3.711 3.634 3.626 3.592 3.563 3.616 3.557 3.431 3.504
## V73 V74 V75 V76 V77 V78 V79 V80 V81 V82 V83 V84
## 1 3.686 3.626 3.583 3.615 3.728 3.724 3.816 3.887 3.762 3.707 3.522 3.679
## 2 3.682 3.584 3.513 3.600 3.822 3.685 3.767 3.712 3.656 3.600 3.445 3.621
## 3 3.672 3.637 3.547 3.594 3.700 3.674 3.730 3.745 3.764 3.667 3.514 3.643
## 4 3.319 3.336 3.311 3.351 3.576 3.481 3.454 3.495 3.508 3.336 3.246 3.368
## 5 3.598 3.465 3.456 3.387 3.642 3.590 3.512 3.570 3.582 3.520 3.390 3.397
## 6 3.572 3.520 3.412 3.441 3.694 3.550 3.556 3.477 3.642 3.434 3.422 3.361
## V85 V86 V87 V88 V89 V90 V91 V92 V93 V94 V95 V96
## 1 3.674 3.696 3.738 4.162 3.688 3.678 3.581 3.594 3.589 3.991 3.805 3.624
## 2 3.721 3.778 3.823 3.942 3.449 3.650 3.583 3.545 3.487 3.809 3.719 3.518
## 3 3.568 3.696 3.599 3.958 3.488 3.644 3.582 3.614 3.637 3.860 3.837 3.608
## 4 3.444 3.525 3.443 3.886 3.830 3.462 3.347 3.293 3.315 3.595 3.496 3.285
## 5 3.495 3.569 3.642 3.936 3.640 3.443 3.415 3.413 3.413 3.763 3.688 3.408
## 6 3.535 3.647 3.487 3.874 3.376 3.504 3.541 3.482 3.444 3.727 3.545 3.393
## V97 V98 V99 V100 V101 V102 V103 V104 V105 V106 V107 V108
## 1 3.642 3.678 3.706 3.752 3.723 3.821 3.689 3.728 3.827 3.709 3.782 3.694
## 2 3.571 3.681 3.586 3.798 3.661 3.834 3.619 3.562 3.727 3.567 3.651 3.640
## 3 3.634 3.593 3.694 3.762 3.618 3.773 3.613 3.574 3.794 3.625 3.676 3.687
## 4 3.237 3.363 3.461 3.534 3.477 3.587 3.429 3.380 3.602 3.358 3.482 3.411
## 5 3.473 3.497 3.520 3.615 3.481 3.556 3.490 3.484 3.674 3.529 3.512 3.532
## 6 3.453 3.398 3.458 3.621 3.492 3.536 3.482 3.381 3.684 3.400 3.489 3.522
## V109 V110 V111 V112 V113 V114 V115 V116 V117 V118 V119 V120
## 1 3.655 3.717 3.729 3.701 3.689 3.717 3.566 3.608 3.719 3.739 3.860 3.526
## 2 3.539 3.550 3.561 3.648 3.595 3.586 3.352 3.560 3.810 3.738 3.846 3.519
## 3 3.546 3.757 3.613 3.801 3.598 3.606 3.439 3.672 3.664 3.588 3.661 3.602
## 4 3.294 3.504 3.386 3.383 3.423 3.364 3.137 3.314 3.412 3.623 3.567 3.387
## 5 3.469 3.485 3.486 3.528 3.530 3.531 3.377 3.472 3.638 3.772 3.562 3.418
## 6 3.334 3.592 3.543 3.596 3.466 3.490 3.276 3.448 3.593 3.607 3.552 3.500
## V121 V122 V123 V124 V125 V126 V127 V128 V129 V130 V131 V132
## 1 3.694 3.784 3.700 3.666 3.785 3.727 3.660 3.600 3.527 3.536 3.682 3.552
## 2 3.645 3.599 3.617 3.609 3.443 3.724 3.557 3.619 3.488 3.487 3.602 3.480
## 3 3.722 3.762 3.514 3.601 3.580 3.634 3.596 3.594 3.415 3.521 3.612 3.465
## 4 3.556 3.491 3.474 3.426 3.423 3.507 3.296 3.398 3.205 3.269 3.321 3.152
## 5 3.571 3.547 3.496 3.456 3.506 3.687 3.483 3.437 3.338 3.350 3.322 3.218
## 6 3.713 3.649 3.502 3.488 3.385 3.760 3.487 3.479 3.306 3.513 3.418 3.325
## V133 V134 V135 V136 V137 V138 V139 V140 V141 V142 V143 V144
## 1 3.735 3.635 3.600 3.525 3.643 3.722 3.754 3.866 3.651 3.740 3.754 3.686
## 2 3.719 3.518 3.552 3.554 3.499 3.641 3.709 3.949 3.645 3.789 3.705 3.614
## 3 3.656 3.549 3.604 3.544 3.598 3.635 3.850 3.822 3.592 3.801 3.718 3.624
## 4 3.509 3.290 3.345 3.248 3.386 3.484 3.648 3.750 3.375 3.638 3.486 3.481
## 5 3.520 3.409 3.392 3.343 3.358 3.608 3.660 3.899 3.514 3.614 3.639 3.439
## 6 3.635 3.378 3.433 3.377 3.410 3.617 3.759 3.955 3.528 3.756 3.610 3.501
## V145 V146 V147 V148 V149 V150 V151 V152 V153 V154 V155 V156
## 1 3.633 3.617 3.657 3.711 3.734 3.640 3.630 3.692 3.735 3.588 3.740 3.762
## 2 3.702 3.560 3.674 3.712 3.709 3.539 3.735 3.733 3.633 3.532 3.753 3.608
## 3 3.636 3.676 3.672 3.696 3.758 3.511 3.622 3.706 3.622 3.560 3.721 3.661
## 4 3.423 3.363 3.434 3.522 3.531 3.264 3.330 3.530 3.556 3.355 3.558 3.460
## 5 3.586 3.433 3.482 3.562 3.512 3.413 3.407 3.658 3.567 3.411 3.528 3.519
## 6 3.494 3.507 3.426 3.595 3.599 3.432 3.514 3.674 3.603 3.513 3.602 3.493
## V157 V158 V159 V160 V161 V162 V163 V164 V165
## 1 3.709 3.672 3.556 3.736 3.752 3.659 3.814 3.674 3.755
## 2 3.577 3.551 3.372 3.712 3.578 3.668 3.613 3.591 3.890
## 3 3.603 3.601 3.488 3.687 3.644 3.578 3.590 3.702 3.707
## 4 3.451 3.487 3.200 3.547 3.399 3.382 3.425 3.488 3.600
## 5 3.553 3.465 3.444 3.488 3.408 3.572 3.428 3.527 3.732
## 6 3.563 3.568 3.356 3.583 3.497 3.472 3.425 3.570 3.726
head(dutchSpeakersDistMeta)
## Speaker Sex AgeYear AgeGroup ConversationType EduLevel
## 410 N01001 female 1952 age45to55 <NA> high
## 409 N01002 male 1952 age45to55 maleFemale high
## 1157 N01003 male 1949 age45to55 maleFemale high
## 252 N01004 female 1971 age25to34 <NA> high
## 251 N01005 female 1944 age56up femaleOnly mid
## 254 N01006 male 1969 age25to34 maleFemale high
dutchSpeakersDist.d=dist(dutchSpeakersDist)
dutchSpeakersDist.mds=cmdscale(dutchSpeakersDist.d, k=3) #ruumiline
head(dutchSpeakersDist.mds)
## [,1] [,2] [,3]
## 1 -2.0815243 0.5731975 0.35933471
## 2 -1.1507874 0.4247729 0.22458085
## 3 -1.3908399 0.1544284 0.15167342
## 4 1.2480230 0.8574089 -0.10860655
## 5 0.2988568 0.7163566 -0.04921619
## 6 0.4885506 0.1551359 -0.38153902
p=dutchSpeakersDist.mds
#install.packages("plot3D")
library(plot3D)
## Warning: package 'plot3D' was built under R version 3.3.3
points3D(p[,1], p[,2], p[,3])
asukohad=cmdscale(dutchSpeakersDist.d, k=2) #tasand
head(asukohad)
## [,1] [,2]
## 1 -2.0815243 0.5731975
## 2 -1.1507874 0.4247729
## 3 -1.3908399 0.1544284
## 4 1.2480230 0.8574089
## 5 0.2988568 0.7163566
## 6 0.4885506 0.1551359
plot(asukohad)
asukohad=cmdscale(dutchSpeakersDist.d, k=1) # joon
head(asukohad)
## [,1]
## 1 -2.0815243
## 2 -1.1507874
## 3 -1.3908399
## 4 1.2480230
## 5 0.2988568
## 6 0.4885506
plot(asukohad, sample(0, nrow(asukohad), replace = TRUE), col=rgb(0, 0, 0, 0.3))
#Paigutage multidimensionaalse skaleerimise abil kahemõõtmelisele joonisele
#ühe päeva tunnid, nii et sarnasema ilmaga tunnid oleksid lähestikku
ilm=read.table("http://www.tlu.ee/~jaagup/andmed/ilm/harkutund.txt", header=TRUE, sep=",")
ilmtervik=ilm[complete.cases(ilm), ]
ilmskaleeritud=scale(ilmtervik[1:24, 5:11])
ilmskaleeritud
## RH1H TA1H TAN1H TAX1H WD1H WS1H
## 1 -0.8675355 1.82433345 1.94868906 1.7645235 1.45381038 2.0368548
## 2 -0.6940284 1.76249164 1.76111471 1.7645235 1.10489589 0.5925396
## 3 -0.3470142 1.51512439 1.57354036 1.5197341 0.65629154 0.1481349
## 4 -0.3470142 1.32959896 1.32344123 1.3361420 0.80582632 0.5925396
## 5 -0.3470142 1.14407352 1.07334210 1.0913527 0.95536111 0.7036408
## 6 -0.5205213 0.83486446 0.82324297 0.8465633 0.75598140 0.7036408
## 7 -0.6940284 0.64933903 0.63566862 0.6629712 0.40706691 1.0369443
## 8 -0.8675355 0.34012997 0.13547036 0.4181819 0.25753212 2.1479560
## 9 -0.8675355 -0.15460453 -0.30220312 -0.0713969 0.20768720 1.4813490
## 10 -1.2145497 -0.46381359 -0.42725268 -0.4385810 0.50675676 0.9258431
## 11 -1.0410426 -0.21644634 -0.23967833 -0.1937916 0.75598140 -0.8517757
## 12 -1.0410426 -0.03092091 0.01042080 -0.0713969 0.75598140 -0.8517757
## 13 -0.8675355 0.15460453 0.13547036 0.1121951 0.45691183 -0.8517757
## 14 -0.8675355 -0.03092091 -0.05210399 0.1121951 0.15784227 -0.6295733
## 15 -0.3470142 -0.21644634 -0.17715355 -0.2549889 -0.49014179 -0.9628768
## 16 0.6940284 -0.40197178 -0.36472790 -0.3773836 -0.93874613 -1.0739780
## 17 1.2145497 -0.46381359 -0.48977746 -0.5609757 -0.14122729 -0.8517757
## 18 1.3880568 -0.64933903 -0.61482703 -0.6833704 -0.19107222 -0.4073710
## 19 1.2145497 -0.71118084 -0.61482703 -0.8057650 -0.24091715 -0.2962698
## 20 1.0410426 -0.77302265 -0.67735181 -0.8057650 0.05815242 -0.4073710
## 21 1.0410426 -0.95854809 -0.98997572 -0.8669624 -1.13812584 -0.9628768
## 22 1.2145497 -1.26775715 -1.42764920 -1.2341465 -1.98548960 -0.8517757
## 23 1.5615639 -1.57696621 -1.49017399 -1.6013305 -2.18486931 -0.7406745
## 24 1.5615639 -1.63880802 -1.55269877 -1.6625279 -1.98548960 -0.6295733
## WSX1H
## 1 1.1511143
## 2 0.5494429
## 3 0.5995822
## 4 1.3015321
## 5 1.1009750
## 6 2.0034821
## 7 0.9505572
## 8 1.0508357
## 9 0.6497215
## 10 1.5020893
## 11 -0.7040391
## 12 -0.5034819
## 13 -0.6538998
## 14 -0.2527855
## 15 -0.9045962
## 16 -1.1552926
## 17 -1.0550140
## 18 -0.5034819
## 19 -0.6538998
## 20 -0.5536212
## 21 -0.9547355
## 22 -0.8544569
## 23 -1.2054319
## 24 -0.9045962
## attr(,"scaled:center")
## RH1H TA1H TAN1H TAX1H WD1H WS1H
## 91.000000 1.450000 1.283333 1.616667 191.833333 3.766667
## WSX1H
## 7.904167
## attr(,"scaled:scale")
## RH1H TA1H TAN1H TAX1H WD1H WS1H
## 5.7634531 1.6170289 1.5993658 1.6340578 20.0622221 0.9000805
## WSX1H
## 1.9944443
head(ilm, 24)
## Kuu Paev Kell PR1H RH1H TA1H TAN1H TAX1H WD1H WS1H WSX1H
## 1 1 1 0:00 0 86 4.4 4.4 4.5 221 5.6 10.2
## 2 1 1 1:00 0 87 4.3 4.1 4.5 214 4.3 9.0
## 3 1 1 2:00 0 89 3.9 3.8 4.1 205 3.9 9.1
## 4 1 1 3:00 0 89 3.6 3.4 3.8 208 4.3 10.5
## 5 1 1 4:00 0 89 3.3 3.0 3.4 211 4.4 10.1
## 6 1 1 5:00 0 88 2.8 2.6 3.0 207 4.4 11.9
## 7 1 1 6:00 0 87 2.5 2.3 2.7 200 4.7 9.8
## 8 1 1 7:00 0 86 2.0 1.5 2.3 197 5.7 10.0
## 9 1 1 8:00 0 86 1.2 0.8 1.5 196 5.1 9.2
## 10 1 1 9:00 0 84 0.7 0.6 0.9 202 4.6 10.9
## 11 1 1 10:00 0 85 1.1 0.9 1.3 207 3.0 6.5
## 12 1 1 11:00 0 85 1.4 1.3 1.5 207 3.0 6.9
## 13 1 1 12:00 0 86 1.7 1.5 1.8 201 3.0 6.6
## 14 1 1 13:00 0 86 1.4 1.2 1.8 195 3.2 7.4
## 15 1 1 14:00 0 89 1.1 1.0 1.2 182 2.9 6.1
## 16 1 1 15:00 0 95 0.8 0.7 1.0 173 2.8 5.6
## 17 1 1 16:00 0 98 0.7 0.5 0.7 189 3.0 5.8
## 18 1 1 17:00 0 99 0.4 0.3 0.5 188 3.4 6.9
## 19 1 1 18:00 0 98 0.3 0.3 0.3 187 3.5 6.6
## 20 1 1 19:00 0 97 0.2 0.2 0.3 193 3.4 6.8
## 21 1 1 20:00 0 97 -0.1 -0.3 0.2 169 2.9 6.0
## 22 1 1 21:00 0 98 -0.6 -1.0 -0.4 152 3.0 6.2
## 23 1 1 22:00 0 100 -1.1 -1.1 -1.0 148 3.1 5.5
## 24 1 1 23:00 0 100 -1.2 -1.2 -1.1 152 3.2 6.1
asukohad=cmdscale(dist(ilmskaleeritud), 2)
asukohad
## [,1] [,2]
## 1 -4.18964099 0.265698338
## 2 -3.20854052 -0.904271336
## 3 -2.51552588 -0.898137111
## 4 -2.72148318 -0.108009576
## 5 -2.46445686 0.001512365
## 6 -2.44900031 0.687953716
## 7 -1.86891879 0.592333181
## 8 -1.83695567 1.715215160
## 9 -0.87605640 1.397116233
## 10 -0.91417413 1.529709791
## 11 0.15286941 -1.058364752
## 12 -0.14750951 -1.095988681
## 13 -0.11987904 -1.209213462
## 14 -0.07158169 -0.684340299
## 15 0.97777326 -0.900227866
## 16 1.83318498 -0.796968971
## 17 1.74488328 -0.639994801
## 18 1.65405993 0.086133627
## 19 1.70900514 0.128383702
## 20 1.58456880 0.049627626
## 21 2.59911851 -0.130742634
## 22 3.36571272 0.481879503
## 23 3.95488341 0.629787764
## 24 3.80766354 0.860908483
plot(asukohad, type="n")
text(asukohad, cex=0.5)
plot(cmdscale(dist(scale(ilmtervik[1:2000, 4:11])), 2))
asukohad=cmdscale(dist(scale(ilmtervik[1:2000, 4:11])), 2)
plot(asukohad, type="n")
text(x=asukohad[, 1], y=asukohad[, 2], labels=ilmtervik[1:2000, "Kuu"])
ryhmad=kmeans(asukohad, centers=3)
plot(asukohad, type="n")
text(x=asukohad[, 1], y=asukohad[, 2], labels=ryhmad$cluster)
plot(asukohad, col=ryhmad$cluster)
head(lexicalMeasures)
## Word CelS Fdif Vf Dent Ient NsyS NsyC
## 1 doe 3.912023 1.0216510 1.386294 0.14144 0.02114 0.6931472 0.000000
## 2 whore 4.521789 0.3504830 1.386294 0.42706 0.94198 1.0986123 0.000000
## 3 stress 6.505784 2.0893560 1.609438 0.06197 1.44339 2.4849066 1.945910
## 4 pork 5.017280 -0.5263339 1.945910 0.43035 0.00000 1.0986123 2.639057
## 5 plug 4.890349 -1.0445450 2.197225 0.35920 1.75393 2.4849066 2.484907
## 6 prop 4.770685 0.9248014 1.386294 0.06268 1.74730 1.6094379 1.386294
## Len Ncou Bigr InBi spelV spelN phonV phonN friendsV
## 1 3 8 7.036333 12.02268 10 3.737670 41 8.837826 8
## 2 5 5 9.537878 12.59780 20 7.870930 38 9.775825 20
## 3 6 0 9.883931 13.30069 10 6.693324 13 7.040536 10
## 4 4 8 8.309180 12.07807 5 6.677084 6 3.828641 4
## 5 4 3 7.943717 11.92678 17 4.762174 17 4.762174 17
## 6 4 9 8.349620 12.19724 19 6.234411 21 6.249975 19
## friendsN ffV ffN fbV fbN ffNonzero NVratio
## 1 3.295837 0.6931472 2.708050 3.496508 8.833900 1 3.9120230
## 2 7.870930 0.0000000 0.000000 2.944439 9.614738 0 4.9628446
## 3 6.693324 0.0000000 0.000000 1.386294 5.817111 0 0.1773868
## 4 3.526361 0.6931472 6.634633 1.098612 2.564949 1 5.0172798
## 5 4.762174 0.0000000 0.000000 0.000000 0.000000 0 0.3458730
## 6 6.234411 0.0000000 0.000000 1.098612 2.197225 0 -0.8020728
plot(hclust(dist(cor(lexicalMeasures[, -1]))))
ilm=read.table("http://www.tlu.ee/~jaagup/andmed/ilm/harkutund.txt", header=TRUE, sep=",")
ilmtervik=ilm[complete.cases(ilm), ]
ilmskaleeritud=scale(ilmtervik[2000:5000, 4:11])
plot(hclust(dist(cor(ilmskaleeritud))))
jaanuar1=ilmtervik[1:24, 5:11]
plot(hclust(dist(cor(scale(jaanuar1)))))
plot(hclust(dist(cor(scale(t(jaanuar1))))))
jaanuar1
## RH1H TA1H TAN1H TAX1H WD1H WS1H WSX1H
## 1 86 4.4 4.4 4.5 221 5.6 10.2
## 2 87 4.3 4.1 4.5 214 4.3 9.0
## 3 89 3.9 3.8 4.1 205 3.9 9.1
## 4 89 3.6 3.4 3.8 208 4.3 10.5
## 5 89 3.3 3.0 3.4 211 4.4 10.1
## 6 88 2.8 2.6 3.0 207 4.4 11.9
## 7 87 2.5 2.3 2.7 200 4.7 9.8
## 8 86 2.0 1.5 2.3 197 5.7 10.0
## 9 86 1.2 0.8 1.5 196 5.1 9.2
## 10 84 0.7 0.6 0.9 202 4.6 10.9
## 11 85 1.1 0.9 1.3 207 3.0 6.5
## 12 85 1.4 1.3 1.5 207 3.0 6.9
## 13 86 1.7 1.5 1.8 201 3.0 6.6
## 14 86 1.4 1.2 1.8 195 3.2 7.4
## 15 89 1.1 1.0 1.2 182 2.9 6.1
## 16 95 0.8 0.7 1.0 173 2.8 5.6
## 17 98 0.7 0.5 0.7 189 3.0 5.8
## 18 99 0.4 0.3 0.5 188 3.4 6.9
## 19 98 0.3 0.3 0.3 187 3.5 6.6
## 20 97 0.2 0.2 0.3 193 3.4 6.8
## 21 97 -0.1 -0.3 0.2 169 2.9 6.0
## 22 98 -0.6 -1.0 -0.4 152 3.0 6.2
## 23 100 -1.1 -1.1 -1.0 148 3.1 5.5
## 24 100 -1.2 -1.2 -1.1 152 3.2 6.1
t(jaanuar1)
## 1 2 3 4 5 6 7 8 9 10 11
## RH1H 86.0 87.0 89.0 89.0 89.0 88.0 87.0 86.0 86.0 84.0 85.0
## TA1H 4.4 4.3 3.9 3.6 3.3 2.8 2.5 2.0 1.2 0.7 1.1
## TAN1H 4.4 4.1 3.8 3.4 3.0 2.6 2.3 1.5 0.8 0.6 0.9
## TAX1H 4.5 4.5 4.1 3.8 3.4 3.0 2.7 2.3 1.5 0.9 1.3
## WD1H 221.0 214.0 205.0 208.0 211.0 207.0 200.0 197.0 196.0 202.0 207.0
## WS1H 5.6 4.3 3.9 4.3 4.4 4.4 4.7 5.7 5.1 4.6 3.0
## WSX1H 10.2 9.0 9.1 10.5 10.1 11.9 9.8 10.0 9.2 10.9 6.5
## 12 13 14 15 16 17 18 19 20 21 22
## RH1H 85.0 86.0 86.0 89.0 95.0 98.0 99.0 98.0 97.0 97.0 98.0
## TA1H 1.4 1.7 1.4 1.1 0.8 0.7 0.4 0.3 0.2 -0.1 -0.6
## TAN1H 1.3 1.5 1.2 1.0 0.7 0.5 0.3 0.3 0.2 -0.3 -1.0
## TAX1H 1.5 1.8 1.8 1.2 1.0 0.7 0.5 0.3 0.3 0.2 -0.4
## WD1H 207.0 201.0 195.0 182.0 173.0 189.0 188.0 187.0 193.0 169.0 152.0
## WS1H 3.0 3.0 3.2 2.9 2.8 3.0 3.4 3.5 3.4 2.9 3.0
## WSX1H 6.9 6.6 7.4 6.1 5.6 5.8 6.9 6.6 6.8 6.0 6.2
## 23 24
## RH1H 100.0 100.0
## TA1H -1.1 -1.2
## TAN1H -1.1 -1.2
## TAX1H -1.0 -1.1
## WD1H 148.0 152.0
## WS1H 3.1 3.2
## WSX1H 5.5 6.1
ilmskaleeritud=scale(ilmtervik[ilmtervik$Kuu==1 & ilmtervik$Kell=="10:00", 4:11])
plot(hclust(dist(cor(ilmskaleeritud))))
row.names(ilmskaleeritud)=1:31
plot(hclust(dist(cor(t(ilmskaleeritud)))))
ryhmad=cutree(hclust(dist(cor(t(ilmskaleeritud)))), k=3) #joon kolme haru kohalt
plot(ilmskaleeritud[, c("TA1H", "WS1H")], type="n") #teljed
text(ilmskaleeritud[, c("TA1H", "WS1H")],
labels = row.names(ilmskaleeritud), col=ryhmad)
