kpca-class.RdThe Kernel Principal Components Analysis class
Objects can be created by calls of the form new("kpca", ...).
or by calling the kpca function.
pcv:Object of class "matrix" containing the
principal component vectors
eig:Object of class "vector" containing the
corresponding eigenvalues
rotated:Object of class "matrix" containing the
projection of the data on the principal components
kernelf:Object of class "function" containing
the kernel function used
kpar:Object of class "list" containing the
kernel parameters used
xmatrix:Object of class "matrix" containing
the data matrix used
kcall:Object of class "ANY" containing the
function call
n.action:Object of class "ANY" containing the
action performed on NA
signature(object = "kpca"): returns the eigenvalues
signature(object = "kpca"): returns the
performed call
signature(object = "kpca"): returns the used
kernel function
signature(object = "kpca"): returns the principal
component vectors
signature(object = "kpca"): embeds new data
signature(object = "kpca"): returns the
projected data
signature(object = "kpca"): returns the used
data matrix
# another example using the iris
data(iris)
test <- sample(1:50,20)
kpc <- kpca(~.,data=iris[-test,-5],kernel="rbfdot",
kpar=list(sigma=0.2),features=2)
#print the principal component vectors
pcv(kpc)
#> [,1] [,2]
#> [1,] -0.307733687 0.065970840
#> [2,] -0.299182287 0.051421702
#> [3,] -0.296516213 0.051839701
#> [4,] -0.307735361 0.071765233
#> [5,] -0.302126303 0.069008638
#> [6,] -0.282394780 0.060567487
#> [7,] -0.301256785 0.048366210
#> [8,] -0.296674830 0.057691698
#> [9,] -0.304294693 0.046981125
#> [10,] -0.297823308 0.058123877
#> [11,] -0.268291982 0.087723131
#> [12,] -0.290907373 0.077295049
#> [13,] -0.273280850 0.030137707
#> [14,] -0.301382807 0.063670137
#> [15,] -0.302397316 0.055336614
#> [16,] -0.294693572 0.107006869
#> [17,] -0.295847369 0.024247867
#> [18,] -0.303058010 0.031751141
#> [19,] -0.299790777 0.041430860
#> [20,] -0.298818628 0.034297631
#> [21,] -0.296263604 0.035174508
#> [22,] -0.287731658 0.080103117
#> [23,] -0.274938448 0.083270319
#> [24,] -0.304464739 0.076458706
#> [25,] -0.306990276 0.077363323
#> [26,] -0.306450897 0.051027710
#> [27,] -0.308109798 0.073981838
#> [28,] -0.297427888 0.027277536
#> [29,] -0.284698552 0.010820524
#> [30,] -0.297913002 0.049585366
#> [31,] 0.112346232 0.037674090
#> [32,] 0.114697803 -0.116204910
#> [33,] 0.128002609 0.075661074
#> [34,] 0.030533117 -0.367179927
#> [35,] 0.124694016 -0.102608712
#> [36,] 0.089645618 -0.279251387
#> [37,] 0.125368255 -0.065534161
#> [38,] -0.089488293 -0.325509364
#> [39,] 0.116965836 -0.098399890
#> [40,] 0.009447950 -0.367320175
#> [41,] -0.058365196 -0.331794666
#> [42,] 0.080990261 -0.275892594
#> [43,] 0.036220529 -0.332652838
#> [44,] 0.122139503 -0.160484235
#> [45,] -0.010105542 -0.361513011
#> [46,] 0.105166512 -0.097831715
#> [47,] 0.090709008 -0.256333110
#> [48,] 0.044303723 -0.347618571
#> [49,] 0.100262785 -0.201026727
#> [50,] 0.018312425 -0.378448467
#> [51,] 0.124867823 -0.091951281
#> [52,] 0.061229443 -0.305284559
#> [53,] 0.130980457 -0.079988862
#> [54,] 0.113049352 -0.186702086
#> [55,] 0.096097379 -0.203172263
#> [56,] 0.107516812 -0.126033156
#> [57,] 0.125898350 -0.001747386
#> [58,] 0.141301059 0.084914818
#> [59,] 0.110453389 -0.213226122
#> [60,] -0.030495743 -0.368875576
#> [61,] 0.001733862 -0.383112494
#> [62,] -0.014659646 -0.381938934
#> [63,] 0.033540835 -0.359037360
#> [64,] 0.135694335 -0.057061049
#> [65,] 0.078083945 -0.271019396
#> [66,] 0.102876773 -0.153704124
#> [67,] 0.126189423 -0.015562410
#> [68,] 0.092836146 -0.223193429
#> [69,] 0.048022773 -0.334277830
#> [70,] 0.033041698 -0.370661408
#> [71,] 0.065305090 -0.327486453
#> [72,] 0.115971353 -0.177348953
#> [73,] 0.044373574 -0.355070498
#> [74,] -0.083614444 -0.332073085
#> [75,] 0.059429129 -0.341923126
#> [76,] 0.058637049 -0.320683561
#> [77,] 0.064457685 -0.322946753
#> [78,] 0.092892439 -0.240147427
#> [79,] -0.115131976 -0.304516668
#> [80,] 0.055015620 -0.341453727
#> [81,] 0.102005803 0.330500580
#> [82,] 0.129979511 -0.049025986
#> [83,] 0.109462526 0.393705881
#> [84,] 0.140168338 0.175985601
#> [85,] 0.127532565 0.305411322
#> [86,] 0.045868941 0.446167437
#> [87,] 0.044564078 -0.274631918
#> [88,] 0.079544852 0.418072088
#> [89,] 0.122108649 0.262721442
#> [90,] 0.072414094 0.432236003
#> [91,] 0.142601432 0.127633999
#> [92,] 0.144939730 0.117059014
#> [93,] 0.135277044 0.282297367
#> [94,] 0.118194749 -0.085178885
#> [95,] 0.119524442 0.029505863
#> [96,] 0.135631794 0.196998222
#> [97,] 0.143298485 0.195191262
#> [98,] 0.021249275 0.417691083
#> [99,] 0.018881695 0.408995354
#> [100,] 0.114650777 -0.114741589
#> [101,] 0.118526898 0.360425339
#> [102,] 0.115951176 -0.109735843
#> [103,] 0.035131606 0.427842238
#> [104,] 0.140756598 -0.028336661
#> [105,] 0.126730409 0.320870668
#> [106,] 0.099648890 0.395214595
#> [107,] 0.137296571 -0.069013171
#> [108,] 0.138478054 -0.046740133
#> [109,] 0.137390125 0.224521472
#> [110,] 0.108672904 0.336786348
#> [111,] 0.085182725 0.409914716
#> [112,] 0.026913048 0.409310693
#> [113,] 0.134909639 0.234953979
#> [114,] 0.141005227 -0.007584441
#> [115,] 0.125034595 0.067592349
#> [116,] 0.063027500 0.438514979
#> [117,] 0.119945082 0.262062973
#> [118,] 0.142533001 0.182043495
#> [119,] 0.131949991 -0.091653396
#> [120,] 0.133020190 0.279129512
#> [121,] 0.124867146 0.317630774
#> [122,] 0.128241101 0.225548855
#> [123,] 0.129979511 -0.049025986
#> [124,] 0.113083538 0.380100269
#> [125,] 0.113583088 0.346359998
#> [126,] 0.135530894 0.213789459
#> [127,] 0.137808132 0.002501059
#> [128,] 0.146097859 0.142475960
#> [129,] 0.126803836 0.193252177
#> [130,] 0.135756543 -0.027488423
rotated(kpc)
#> [,1] [,2]
#> 1 -10.70120706 1.17197151
#> 2 -10.40383859 0.91350618
#> 4 -10.31112786 0.92093192
#> 5 -10.70126528 1.27490885
#> 7 -10.50621450 1.22593797
#> 9 -9.82006567 1.07598098
#> 10 -10.47597767 0.85922538
#> 11 -10.31664364 1.02489260
#> 12 -10.58161863 0.83461936
#> 13 -10.35658109 1.03257027
#> 15 -9.32965149 1.55840080
#> 17 -10.11608468 1.37314601
#> 19 -9.50313563 0.53539616
#> 20 -10.48036000 1.13109953
#> 22 -10.51563878 0.98305456
#> 23 -10.24774688 1.90097628
#> 26 -10.28786931 0.43076318
#> 27 -10.53861393 0.56405879
#> 30 -10.42499834 0.73601894
#> 31 -10.39119261 0.60929717
#> 32 -10.30234358 0.62487488
#> 33 -10.00565159 1.42303132
#> 34 -9.56077734 1.47929662
#> 36 -10.58753188 1.35828838
#> 38 -10.67535549 1.37435889
#> 40 -10.65659902 0.90650691
#> 41 -10.71428605 1.31428683
#> 44 -10.34283066 0.48458524
#> 45 -9.90017761 0.19222653
#> 46 -10.35970013 0.88088368
#> 51 3.90675555 0.66927994
#> 52 3.98852966 -2.06437941
#> 53 4.45119428 1.34411846
#> 54 1.06176613 -6.52294883
#> 55 4.33614046 -1.82284305
#> 56 3.11735881 -4.96089893
#> 57 4.35958661 -1.16421391
#> 58 -3.11188797 -5.78267157
#> 59 4.06739879 -1.74807335
#> 60 0.32854534 -6.52544032
#> 61 -2.02960571 -5.89432990
#> 62 2.81637529 -4.90123000
#> 63 1.25954161 -5.90957533
#> 64 4.24730917 -2.85100131
#> 65 -0.35141259 -6.42227609
#> 66 3.65708619 -1.73797973
#> 67 3.15433739 -4.55375589
#> 68 1.54062857 -6.17544147
#> 69 3.48656281 -3.57123840
#> 70 0.63680078 -6.72313435
#> 71 4.34218444 -1.63351387
#> 72 2.12920774 -5.42337804
#> 73 4.55474669 -1.42100158
#> 74 3.93120604 -3.31676125
#> 75 3.34171395 -3.60935382
#> 76 3.73881613 -2.23897814
#> 77 4.37802025 -0.03104231
#> 78 4.91363785 1.50851115
#> 79 3.84093336 -3.78796056
#> 80 -1.06046648 -6.55307201
#> 81 0.06029374 -6.80599076
#> 82 -0.50977816 -6.78514248
#> 83 1.16635725 -6.37829617
#> 84 4.71866823 -1.01368913
#> 85 2.71531035 -4.81465766
#> 86 3.57746226 -2.73055267
#> 87 4.38814211 -0.27646611
#> 88 3.22830703 -3.96502969
#> 89 1.66995574 -5.93844329
#> 90 1.14900015 -6.58479731
#> 91 2.27093531 -5.81779454
#> 92 4.03281642 -3.15060290
#> 93 1.54305760 -6.30782489
#> 94 -2.90762929 -5.89927601
#> 95 2.06660316 -6.07426193
#> 96 2.03905920 -5.69694121
#> 97 2.24146741 -5.73714680
#> 98 3.23026455 -4.26621735
#> 99 -4.00362771 -5.40973647
#> 100 1.91312673 -6.06592306
#> 101 3.54717492 5.87134047
#> 102 4.51993954 -0.87094629
#> 103 3.80647687 6.99418220
#> 104 4.87424831 3.12638297
#> 105 4.43484884 5.42562999
#> 106 1.59505784 7.92616138
#> 107 1.54968224 -4.87883410
#> 108 2.76611229 7.42704771
#> 109 4.24623626 4.66724456
#> 110 2.51814556 7.67866955
#> 111 4.95885734 2.26741710
#> 112 5.04016990 2.07955257
#> 113 4.70415725 5.01501074
#> 114 4.11013332 -1.51320229
#> 115 4.15637241 0.52417145
#> 116 4.71649344 3.49967203
#> 117 4.98309683 3.46757141
#> 118 0.73892752 7.42027916
#> 119 0.65659672 7.26579959
#> 120 3.98689437 -2.03838352
#> 121 4.12168357 6.40295361
#> 122 4.03211477 -1.94945648
#> 123 1.22167512 7.60061434
#> 124 4.89470462 -0.50340058
#> 125 4.40695446 5.70026516
#> 126 3.46521507 7.02098449
#> 127 4.77438478 -1.22601851
#> 128 4.81546998 -0.83033814
#> 129 4.77763805 3.98862236
#> 130 3.77901836 5.98300710
#> 131 2.96216508 7.28213204
#> 132 0.93588098 7.27140158
#> 133 4.69138104 4.17395579
#> 134 4.90335051 -0.13473755
#> 135 4.34798383 1.20077760
#> 136 2.19173382 7.79021552
#> 137 4.17099982 4.65554687
#> 138 4.95647769 3.23400140
#> 139 4.58846151 -1.62822194
#> 140 4.62567687 4.95873382
#> 141 4.34216091 5.64270847
#> 142 4.45948764 4.00687383
#> 143 4.51993954 -0.87094629
#> 144 3.93239482 6.75247860
#> 145 3.94976629 6.15308292
#> 146 4.71298471 3.79796823
#> 147 4.79217393 0.04443129
#> 148 5.08044296 2.53108442
#> 149 4.40950783 3.43312356
#> 150 4.72083146 -0.48833164
kernelf(kpc)
#> new("rbfkernel", .Data = function (x, y = NULL)
#> {
#> if (!is(x, "vector"))
#> stop("x must be a vector")
#> if (!is(y, "vector") && !is.null(y))
#> stop("y must a vector")
#> if (is(x, "vector") && is.null(y)) {
#> return(1)
#> }
#> if (is(x, "vector") && is(y, "vector")) {
#> if (!length(x) == length(y))
#> stop("number of dimension must be the same on both data points")
#> return(exp(sigma * (2 * crossprod(x, y) - crossprod(x) -
#> crossprod(y))))
#> }
#> }, kpar = list(sigma = 0.2))
#> <bytecode: 0x564204d155f8>
#> <environment: 0x564207ed5290>
#> attr(,"kpar")
#> attr(,"kpar")$sigma
#> [1] 0.2
#>
#> attr(,"class")
#> [1] "rbfkernel"
#> attr(,"class")attr(,"package")
#> [1] "kernlab"
eig(kpc)
#> Comp.1 Comp.2
#> 0.2674942 0.1366538