CRAN Package Check Results for Package kernlab

Last updated on 2015-08-02 06:47:59.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 0.9-20 10.07 87.08 97.15 NOTE
r-devel-linux-x86_64-debian-gcc 0.9-20 11.23 87.58 98.81 NOTE
r-devel-linux-x86_64-fedora-clang 0.9-20 193.18 NOTE
r-devel-linux-x86_64-fedora-gcc 0.9-20 184.05 NOTE
r-devel-osx-x86_64-clang 0.9-20 156.73 NOTE
r-devel-windows-ix86+x86_64 0.9-20 58.00 185.00 243.00 NOTE
r-patched-linux-x86_64 0.9-20 11.62 85.44 97.05 NOTE
r-patched-solaris-sparc 0.9-20 1163.60 ERROR
r-patched-solaris-x86 0.9-20 234.80 NOTE
r-release-linux-x86_64 0.9-20 11.28 87.20 98.48 NOTE
r-release-osx-x86_64-mavericks 0.9-20 NOTE
r-release-windows-ix86+x86_64 0.9-20 57.00 154.00 211.00 OK
r-oldrel-windows-ix86+x86_64 0.9-20 75.00 161.00 236.00 OK

Memtest notes: valgrind

Check Details

Version: 0.9-20
Check: R code for possible problems
Result: NOTE
    gausspr,formula: no visible global function definition for
     ‘model.matrix’
    gausspr,formula: no visible global function definition for
     ‘model.extract’
    gausspr,formula: no visible binding for global variable ‘response’
    gausspr,matrix: no visible binding for global variable ‘C’
    kfa,formula: no visible global function definition for ‘model.matrix’
    kfa,matrix: no visible binding for global variable ‘sd’
    kha,formula: no visible global function definition for ‘model.matrix’
    kha,matrix: no visible global function definition for ‘runif’
    kkmeans,formula: no visible global function definition for
     ‘model.matrix’
    kmmd,matrix: no visible binding for global variable ‘C’
    kmmd,matrix: no visible binding for global variable ‘nu’
    kmmd,matrix: no visible binding for global variable ‘epsilon’
    kmmd,matrix: no visible binding for global variable ‘class.weights’
    kmmd,matrix: no visible binding for global variable ‘cache’
    kmmd,matrix: no visible binding for global variable ‘shrinking’
    kpca,formula: no visible global function definition for ‘model.matrix’
    kqr,formula: no visible global function definition for ‘model.matrix’
    kqr,formula: no visible global function definition for ‘model.extract’
    kqr,formula: no visible binding for global variable ‘response’
    kqr,kernelMatrix: no visible binding for global variable ‘kernel’
    kqr,kernelMatrix: no visible binding for global variable ‘tmpsc’
    kqr,matrix: no visible binding for global variable ‘var’
    ksvm,formula: no visible global function definition for ‘model.matrix’
    ksvm,formula: no visible global function definition for ‘model.extract’
    ksvm,formula: no visible binding for global variable ‘response’
    ksvm,kernelMatrix: no visible global function definition for ‘rnorm’
    ksvm,kernelMatrix: no visible global function definition for ‘sd’
    ksvm,list: no visible global function definition for ‘sd’
    ksvm,matrix: no visible binding for global variable ‘var’
    ksvm,matrix: no visible global function definition for ‘sd’
    lssvm,formula: no visible global function definition for ‘model.matrix’
    lssvm,formula: no visible global function definition for
     ‘model.extract’
    lssvm,formula: no visible binding for global variable ‘response’
    lssvm,kernelMatrix: no visible binding for global variable ‘kernel’
    lssvm,list: no visible binding for global variable ‘x.scale’
    lssvm,list: no visible binding for global variable ‘cache’
    lssvm,matrix: no visible binding for global variable ‘var’
    lssvm,matrix: no visible binding for global variable ‘C’
    lssvm,matrix: no visible binding for global variable ‘nu’
    lssvm,matrix: no visible binding for global variable ‘epsilon’
    lssvm,matrix: no visible binding for global variable ‘class.weights’
    lssvm,matrix: no visible binding for global variable ‘cache’
    lssvm,matrix: no visible binding for global variable ‘shrinking’
    plot,ksvm-missing: no visible global function definition for
     ‘model.matrix’
    plot,ksvm-missing: no visible global function definition for
     ‘delete.response’
    plot,ksvm-missing: no visible global function definition for ‘hcl’
    plot,ksvm-missing: no visible global function definition for
     ‘filled.contour’
    plot,ksvm-missing: no visible global function definition for ‘axis’
    plot,ksvm-missing: no visible global function definition for ‘points’
    plot,ksvm-missing: no visible global function definition for ‘title’
    predict,gausspr: no visible global function definition for
     ‘model.matrix’
    predict,gausspr: no visible global function definition for
     ‘delete.response’
    predict,gausspr: no visible binding for global variable ‘na.action’
    predict,kfa: no visible global function definition for ‘model.matrix’
    predict,kfa: no visible global function definition for
     ‘delete.response’
    predict,kha: no visible global function definition for ‘model.matrix’
    predict,kha: no visible global function definition for
     ‘delete.response’
    predict,kpca: no visible global function definition for ‘model.matrix’
    predict,kpca: no visible global function definition for
     ‘delete.response’
    predict,kqr: no visible global function definition for ‘model.matrix’
    predict,kqr: no visible global function definition for
     ‘delete.response’
    predict,kqr: no visible binding for global variable ‘na.action’
    predict,ksvm: no visible global function definition for ‘model.matrix’
    predict,ksvm: no visible global function definition for
     ‘delete.response’
    predict,lssvm: no visible global function definition for ‘model.matrix’
    predict,lssvm: no visible global function definition for
     ‘delete.response’
    predict,onlearn: possible error in kernelMult(kernelf(object), x,
     matrix(xmatrix(object), ncol = d), matrix(alpha(object)), ncol = 1):
     unused argument (ncol = 1)
    predict,rvm: no visible global function definition for ‘model.matrix’
    predict,rvm: no visible global function definition for
     ‘delete.response’
    predict,rvm: no visible binding for global variable ‘na.action’
    rvm,formula: no visible global function definition for ‘model.matrix’
    rvm,formula: no visible global function definition for ‘model.extract’
    rvm,formula: no visible binding for global variable ‘response’
    rvm,kernelMatrix: no visible binding for global variable
     ‘class.weights’
    rvm,kernelMatrix: no visible binding for global variable ‘C’
    rvm,kernelMatrix: no visible binding for global variable ‘nu’
    rvm,kernelMatrix: no visible binding for global variable ‘epsilon’
    rvm,matrix: no visible binding for global variable ‘class.weights’
    rvm,matrix: no visible binding for global variable ‘C’
    rvm,matrix: no visible binding for global variable ‘nu’
    rvm,matrix: no visible binding for global variable ‘epsilon’
    sigest,formula: no visible global function definition for
     ‘model.matrix’
    sigest,matrix: no visible binding for global variable ‘var’
    sigest,matrix: no visible global function definition for ‘quantile’
    specc,formula: no visible global function definition for ‘model.matrix’
    specc,kernelMatrix: no visible global function definition for ‘kmeans’
    specc,list: no visible global function definition for ‘kmeans’
    specc,matrix: no visible global function definition for ‘kmeans’
    specc,matrix: no visible global function definition for ‘median’
    Undefined global functions or variables:
     C axis cache class.weights delete.response epsilon filled.contour hcl
     kernel kmeans median model.extract model.matrix na.action nu points
     quantile response rnorm runif sd shrinking title tmpsc var x.scale
    Consider adding
     importFrom("grDevices", "hcl")
     importFrom("graphics", "axis", "filled.contour", "points", "title")
     importFrom("stats", "C", "delete.response", "kernel", "kmeans",
     "median", "model.extract", "model.matrix", "na.action",
     "quantile", "rnorm", "runif", "sd", "var")
    to your NAMESPACE.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-ix86+x86_64

Version: 0.9-20
Check: R code for possible problems
Result: NOTE
    gausspr,formula: no visible binding for global variable ‘response’
    kmmd,matrix: no visible binding for global variable ‘nu’
    kmmd,matrix: no visible binding for global variable ‘epsilon’
    kmmd,matrix: no visible binding for global variable ‘class.weights’
    kmmd,matrix: no visible binding for global variable ‘cache’
    kmmd,matrix: no visible binding for global variable ‘shrinking’
    kqr,formula: no visible binding for global variable ‘response’
    kqr,kernelMatrix: no visible binding for global variable ‘tmpsc’
    ksvm,formula: no visible binding for global variable ‘response’
    lssvm,formula: no visible binding for global variable ‘response’
    lssvm,list: no visible binding for global variable ‘x.scale’
    lssvm,list: no visible binding for global variable ‘cache’
    lssvm,matrix: no visible binding for global variable ‘nu’
    lssvm,matrix: no visible binding for global variable ‘epsilon’
    lssvm,matrix: no visible binding for global variable ‘class.weights’
    lssvm,matrix: no visible binding for global variable ‘cache’
    lssvm,matrix: no visible binding for global variable ‘shrinking’
    predict,onlearn: possible error in kernelMult(kernelf(object), x,
     matrix(xmatrix(object), ncol = d), matrix(alpha(object)), ncol = 1):
     unused argument (ncol = 1)
    rvm,formula: no visible binding for global variable ‘response’
    rvm,kernelMatrix: no visible binding for global variable
     ‘class.weights’
    rvm,kernelMatrix: no visible binding for global variable ‘nu’
    rvm,kernelMatrix: no visible binding for global variable ‘epsilon’
    rvm,matrix: no visible binding for global variable ‘class.weights’
    rvm,matrix: no visible binding for global variable ‘nu’
    rvm,matrix: no visible binding for global variable ‘epsilon’
    Undefined global functions or variables:
     cache class.weights epsilon nu response shrinking tmpsc x.scale
Flavor: r-devel-osx-x86_64-clang

Version: 0.9-20
Check: R code for possible problems
Result: NOTE
    gausspr,formula : .local: no visible binding for global variable
     ‘response’
    kmmd,matrix : .local: no visible binding for global variable ‘nu’
    kmmd,matrix : .local: no visible binding for global variable ‘epsilon’
    kmmd,matrix : .local: no visible binding for global variable
     ‘class.weights’
    kmmd,matrix : .local: no visible binding for global variable ‘cache’
    kmmd,matrix : .local: no visible binding for global variable
     ‘shrinking’
    kqr,formula : .local: no visible binding for global variable ‘response’
    kqr,kernelMatrix : .local: no visible binding for global variable
     ‘tmpsc’
    ksvm,formula : .local: no visible binding for global variable
     ‘response’
    lssvm,formula : .local: no visible binding for global variable
     ‘response’
    lssvm,list : .local: no visible binding for global variable ‘x.scale’
    lssvm,list : .local: no visible binding for global variable ‘cache’
    lssvm,matrix : .local: no visible binding for global variable ‘nu’
    lssvm,matrix : .local: no visible binding for global variable ‘epsilon’
    lssvm,matrix : .local: no visible binding for global variable
     ‘class.weights’
    lssvm,matrix : .local: no visible binding for global variable ‘cache’
    lssvm,matrix : .local: no visible binding for global variable
     ‘shrinking’
    predict,onlearn : .local: possible error in kernelMult(kernelf(object),
     x, matrix(xmatrix(object), ncol = d), matrix(alpha(object)), ncol =
     1): unused argument (ncol = 1)
    rvm,formula : .local: no visible binding for global variable ‘response’
    rvm,kernelMatrix : .local: no visible binding for global variable
     ‘class.weights’
    rvm,kernelMatrix : .local: no visible binding for global variable ‘nu’
    rvm,kernelMatrix : .local: no visible binding for global variable
     ‘epsilon’
    rvm,matrix : .local: no visible binding for global variable
     ‘class.weights’
    rvm,matrix : .local: no visible binding for global variable ‘nu’
    rvm,matrix : .local: no visible binding for global variable ‘epsilon’
Flavors: r-patched-linux-x86_64, r-patched-solaris-sparc, r-patched-solaris-x86, r-release-linux-x86_64

Version: 0.9-20
Check: examples
Result: ERROR
    Running examples in ‘kernlab-Ex.R’ failed
    The error most likely occurred in:
    
    > ### Name: ksvm
    > ### Title: Support Vector Machines
    > ### Aliases: ksvm ksvm,formula-method ksvm,vector-method ksvm,matrix-method
    > ### ksvm,kernelMatrix-method ksvm,list-method show,ksvm-method
    > ### coef,ksvm-method
    > ### Keywords: methods regression nonlinear classif neural
    >
    > ### ** Examples
    >
    >
    > ## simple example using the spam data set
    > data(spam)
    >
    > ## create test and training set
    > index <- sample(1:dim(spam)[1])
    > spamtrain <- spam[index[1:floor(dim(spam)[1]/2)], ]
    > spamtest <- spam[index[((ceiling(dim(spam)[1]/2)) + 1):dim(spam)[1]], ]
    >
    > ## train a support vector machine
    > filter <- ksvm(type~.,data=spamtrain,kernel="rbfdot",
    + kpar=list(sigma=0.05),C=5,cross=3)
    > filter
    Support Vector Machine object of class "ksvm"
    
    SV type: C-svc (classification)
     parameter : cost C = 5
    
    Gaussian Radial Basis kernel function.
     Hyperparameter : sigma = 0.05
    
    Number of Support Vectors : 922
    
    Objective Function Value : -1073.61
    Training error : 0.017391
    Cross validation error : 0.09348
    >
    > ## predict mail type on the test set
    > mailtype <- predict(filter,spamtest[,-58])
    >
    > ## Check results
    > table(mailtype,spamtest[,58])
    
    mailtype nonspam spam
     nonspam 1349 104
     spam 55 792
    >
    >
    > ## Another example with the famous iris data
    > data(iris)
    >
    > ## Create a kernel function using the build in rbfdot function
    > rbf <- rbfdot(sigma=0.1)
    > rbf
    Gaussian Radial Basis kernel function.
     Hyperparameter : sigma = 0.1
    >
    > ## train a bound constraint support vector machine
    > irismodel <- ksvm(Species~.,data=iris,type="C-bsvc",
    + kernel=rbf,C=10,prob.model=TRUE)
    >
    > irismodel
    Support Vector Machine object of class "ksvm"
    
    SV type: C-bsvc (classification)
     parameter : cost C = 10
    
    Gaussian Radial Basis kernel function.
     Hyperparameter : sigma = 0.1
    
    Number of Support Vectors : 32
    
    Objective Function Value : -5.8442 -3.0652 -136.9786
    Training error : 0.02
    Probability model included.
    >
    > ## get fitted values
    > fitted(irismodel)
     [1] setosa setosa setosa setosa setosa setosa
     [7] setosa setosa setosa setosa setosa setosa
     [13] setosa setosa setosa setosa setosa setosa
     [19] setosa setosa setosa setosa setosa setosa
     [25] setosa setosa setosa setosa setosa setosa
     [31] setosa setosa setosa setosa setosa setosa
     [37] setosa setosa setosa setosa setosa setosa
     [43] setosa setosa setosa setosa setosa setosa
     [49] setosa setosa versicolor versicolor versicolor versicolor
     [55] versicolor versicolor versicolor versicolor versicolor versicolor
     [61] versicolor versicolor versicolor versicolor versicolor versicolor
     [67] versicolor versicolor versicolor versicolor versicolor versicolor
     [73] virginica versicolor versicolor versicolor versicolor virginica
     [79] versicolor versicolor versicolor versicolor versicolor virginica
     [85] versicolor versicolor versicolor versicolor versicolor versicolor
     [91] versicolor versicolor versicolor versicolor versicolor versicolor
     [97] versicolor versicolor versicolor versicolor virginica virginica
    [103] virginica virginica virginica virginica virginica virginica
    [109] virginica virginica virginica virginica virginica virginica
    [115] virginica virginica virginica virginica virginica virginica
    [121] virginica virginica virginica virginica virginica virginica
    [127] virginica virginica virginica virginica virginica virginica
    [133] virginica virginica virginica virginica virginica virginica
    [139] virginica virginica virginica virginica virginica virginica
    [145] virginica virginica virginica virginica virginica virginica
    Levels: setosa versicolor virginica
    >
    > ## Test on the training set with probabilities as output
    > predict(irismodel, iris[,-5], type="probabilities")
     setosa versicolor virginica
     [1,] 0.983846258 0.0093172557 0.006836487
     [2,] 0.978645002 0.0136052278 0.007749770
     [3,] 0.985595610 0.0076995180 0.006704872
     [4,] 0.982113992 0.0101230747 0.007762933
     [5,] 0.984928589 0.0083717855 0.006699626
     [6,] 0.971826655 0.0187151799 0.009458166
     [7,] 0.983692067 0.0085001724 0.007807760
     [8,] 0.982711888 0.0100873934 0.007200719
     [9,] 0.981979606 0.0100189253 0.008001469
     [10,] 0.982355257 0.0105661519 0.007078591
     [11,] 0.979825873 0.0125007307 0.007673396
     [12,] 0.982833202 0.0096232859 0.007543512
     [13,] 0.983558214 0.0096630828 0.006778704
     [14,] 0.988782361 0.0049129924 0.006304647
     [15,] 0.973550163 0.0172190287 0.009230808
     [16,] 0.955485351 0.0315096808 0.013004969
     [17,] 0.977381624 0.0144474484 0.008170928
     [18,] 0.981469167 0.0110723459 0.007458488
     [19,] 0.967226516 0.0221546049 0.010618879
     [20,] 0.981151103 0.0112845970 0.007564300
     [21,] 0.971439252 0.0190617313 0.009499017
     [22,] 0.978727843 0.0130459743 0.008226183
     [23,] 0.988154939 0.0053392397 0.006505821
     [24,] 0.955673058 0.0314567092 0.012870233
     [25,] 0.977557181 0.0134083536 0.009034466
     [26,] 0.970756269 0.0200715043 0.009172227
     [27,] 0.973605496 0.0168919244 0.009502579
     [28,] 0.981406810 0.0112214032 0.007371787
     [29,] 0.981698417 0.0110561685 0.007245414
     [30,] 0.981128093 0.0109116188 0.007960288
     [31,] 0.978128169 0.0136077379 0.008264094
     [32,] 0.966327183 0.0231136774 0.010559140
     [33,] 0.978848454 0.0130561800 0.008095366
     [34,] 0.972843038 0.0180108522 0.009146110
     [35,] 0.978901552 0.0132224703 0.007875978
     [36,] 0.984464471 0.0089774116 0.006558117
     [37,] 0.979050478 0.0130484480 0.007901074
     [38,] 0.986722925 0.0069107848 0.006366291
     [39,] 0.984769854 0.0078553995 0.007374746
     [40,] 0.981576714 0.0110594132 0.007363873
     [41,] 0.983690798 0.0092926974 0.007016504
     [42,] 0.955555569 0.0314899424 0.012954489
     [43,] 0.986087895 0.0066221473 0.007289958
     [44,] 0.964813918 0.0232669357 0.011919147
     [45,] 0.972417749 0.0177609147 0.009821336
     [46,] 0.975610321 0.0157396494 0.008650030
     [47,] 0.981867827 0.0107258304 0.007406343
     [48,] 0.984750095 0.0080707392 0.007179166
     [49,] 0.981256923 0.0113829477 0.007360130
     [50,] 0.982983191 0.0099843630 0.007032446
     [51,] 0.029252548 0.9432104200 0.027537032
     [52,] 0.017040714 0.9574335995 0.025525686
     [53,] 0.017020176 0.8611546378 0.121825186
     [54,] 0.006868112 0.8938155097 0.099316378
     [55,] 0.006423721 0.8683449893 0.125231289
     [56,] 0.005398649 0.9434571513 0.051144199
     [57,] 0.019806843 0.9084043471 0.071788810
     [58,] 0.043000208 0.9383465157 0.018653276
     [59,] 0.009467780 0.9704681774 0.020064043
     [60,] 0.007999976 0.9358774058 0.056122619
     [61,] 0.036784196 0.9007548094 0.062460994
     [62,] 0.008183214 0.9651360624 0.026680723
     [63,] 0.008031775 0.9825588169 0.009409409
     [64,] 0.006137838 0.9049620860 0.088900076
     [65,] 0.015797579 0.9797664145 0.004436006
     [66,] 0.017961262 0.9700646712 0.011974067
     [67,] 0.008236733 0.8923225638 0.099440704
     [68,] 0.009546346 0.9870752888 0.003378365
     [69,] 0.008713214 0.6167995983 0.374487188
     [70,] 0.006872366 0.9844126186 0.008715016
     [71,] 0.013435904 0.4992946437 0.487269452
     [72,] 0.006986019 0.9876023016 0.005411680
     [73,] 0.007755773 0.4123867345 0.579857493
     [74,] 0.005602458 0.9644623579 0.029935184
     [75,] 0.008957570 0.9831986713 0.007843758
     [76,] 0.011983418 0.9729047249 0.015111857
     [77,] 0.009051875 0.8914741144 0.099474011
     [78,] 0.009980970 0.4002409694 0.589778061
     [79,] 0.005995640 0.8916738239 0.102330536
     [80,] 0.017227237 0.9788248594 0.003947903
     [81,] 0.007736746 0.9796577941 0.012605460
     [82,] 0.011303741 0.9819048019 0.006791457
     [83,] 0.007016664 0.9881172203 0.004866115
     [84,] 0.007498820 0.1372280131 0.855273167
     [85,] 0.009482424 0.8697147495 0.120802826
     [86,] 0.031451830 0.9376416179 0.030906553
     [87,] 0.014454203 0.9213480591 0.064197738
     [88,] 0.005342455 0.9225469306 0.072110614
     [89,] 0.013052157 0.9792918529 0.007655990
     [90,] 0.005335193 0.9447675024 0.049897305
     [91,] 0.005444922 0.9349869163 0.059568162
     [92,] 0.007834906 0.9504791047 0.041685989
     [93,] 0.005303167 0.9853804132 0.009316420
     [94,] 0.035158731 0.9460700446 0.018771225
     [95,] 0.005137955 0.9610656311 0.033796414
     [96,] 0.014202706 0.9803778900 0.005419404
     [97,] 0.007889492 0.9799842032 0.012126305
     [98,] 0.007804632 0.9830533604 0.009142008
     [99,] 0.042477484 0.9454068281 0.012115688
    [100,] 0.006314068 0.9803855909 0.013300341
    [101,] 0.007991647 0.0010109304 0.990997422
    [102,] 0.005332873 0.0114868076 0.983180320
    [103,] 0.006409118 0.0030929276 0.990497954
    [104,] 0.006063001 0.0136974481 0.980239551
    [105,] 0.005072606 0.0009385997 0.993988794
    [106,] 0.008713017 0.0022574179 0.989029565
    [107,] 0.012024086 0.0897338046 0.898242109
    [108,] 0.007997014 0.0058757465 0.986127240
    [109,] 0.006810189 0.0056123930 0.987577418
    [110,] 0.013033674 0.0096081804 0.977358146
    [111,] 0.010189594 0.0898858563 0.899924550
    [112,] 0.005596535 0.0113254728 0.983077992
    [113,] 0.006245464 0.0063787840 0.987375752
    [114,] 0.004750465 0.0045486277 0.990700908
    [115,] 0.004392511 0.0006269814 0.994980507
    [116,] 0.007839240 0.0064511667 0.985709594
    [117,] 0.007266075 0.0366560505 0.956077875
    [118,] 0.020632163 0.0324084174 0.946959419
    [119,] 0.015081915 0.0024997039 0.982418381
    [120,] 0.010873209 0.1700223871 0.819104404
    [121,] 0.007034392 0.0033684608 0.989597148
    [122,] 0.006062250 0.0143784108 0.979559339
    [123,] 0.011227172 0.0028504988 0.985922329
    [124,] 0.006991988 0.0916547112 0.901353301
    [125,] 0.008594602 0.0139305109 0.977474887
    [126,] 0.009212634 0.0311918684 0.959595498
    [127,] 0.007466557 0.1531821316 0.839351312
    [128,] 0.009006663 0.2104328651 0.780560472
    [129,] 0.004567903 0.0012580956 0.994174002
    [130,] 0.010834395 0.0998162682 0.889349337
    [131,] 0.008677507 0.0061921326 0.985130360
    [132,] 0.029915312 0.0845832428 0.885501445
    [133,] 0.004417017 0.0006952928 0.994887690
    [134,] 0.007765247 0.4270054764 0.565229277
    [135,] 0.008325895 0.0898943692 0.901779736
    [136,] 0.009732419 0.0035379575 0.986729623
    [137,] 0.010964820 0.0062585086 0.982776671
    [138,] 0.008377115 0.0544356248 0.937187260
    [139,] 0.009313238 0.2649907012 0.725696061
    [140,] 0.007373053 0.0153894226 0.977237524
    [141,] 0.006184894 0.0013085317 0.992506575
    [142,] 0.007904553 0.0122058254 0.979889621
    [143,] 0.005332873 0.0114868076 0.983180320
    [144,] 0.006578351 0.0018115477 0.991610102
    [145,] 0.008166540 0.0021704423 0.989663018
    [146,] 0.006400448 0.0043670753 0.989232477
    [147,] 0.006266524 0.0215252549 0.972208221
    [148,] 0.006936193 0.0247416542 0.968322153
    [149,] 0.012534914 0.0171025695 0.970362516
    [150,] 0.008746406 0.0986049013 0.892648693
    >
    >
    > ## Demo of the plot function
    > x <- rbind(matrix(rnorm(120),,2),matrix(rnorm(120,mean=3),,2))
    > y <- matrix(c(rep(1,60),rep(-1,60)))
    >
    > svp <- ksvm(x,y,type="C-svc")
    Using automatic sigma estimation (sigest) for RBF or laplace kernel
    > plot(svp,data=x)
    >
    >
    > ### Use kernelMatrix
    > K <- as.kernelMatrix(crossprod(t(x)))
    >
    > svp2 <- ksvm(K, y, type="C-svc")
    >
    > svp2
    Support Vector Machine object of class "ksvm"
    
    SV type: C-svc (classification)
     parameter : cost C = 1
    
    [1] " Kernel matrix used as input."
    
    Number of Support Vectors : 7
    
    Objective Function Value : -4.3822
    Training error : 0.008333
    >
    > # test data
    > xtest <- rbind(matrix(rnorm(20),,2),matrix(rnorm(20,mean=3),,2))
    > # test kernel matrix i.e. inner/kernel product of test data with
    > # Support Vectors
    >
    > Ktest <- as.kernelMatrix(crossprod(t(xtest),t(x[SVindex(svp2), ])))
    >
    > predict(svp2, Ktest)
     [1] 1 1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1 -1
    >
    >
    > #### Use custom kernel
    >
    > k <- function(x,y) {(sum(x*y) +1)*exp(-0.001*sum((x-y)^2))}
    > class(k) <- "kernel"
    >
    > data(promotergene)
    >
    > ## train svm using custom kernel
    > gene <- ksvm(Class~.,data=promotergene[c(1:20, 80:100),],kernel=k,
    + C=5,cross=5)
    >
    > gene
    Support Vector Machine object of class "ksvm"
    
    SV type: C-svc (classification)
     parameter : cost C = 5
    
    
    Number of Support Vectors : 41
    
    Objective Function Value : -0.5191
    Training error : 0
    Cross validation error : 0.169444
    >
    >
    > #### Use text with string kernels
    > data(reuters)
    > is(reuters)
    [1] "list" "vector" "input" "listI" "lpinput" "output"
    > tsv <- ksvm(reuters,rlabels,kernel="stringdot",
    + kpar=list(length=5),cross=3,C=10)
    
     *** caught segfault ***
    address c6b1040, cause 'memory not mapped'
    
    Traceback:
     1: .Call("stringtv", as.character(x[i]), as.character(x[i:length(x)]), as.integer(length(x) - i + 1), as.integer(nchar(x[i])), as.integer(nchar(x[i:length(x)])), as.integer(sktype), as.double(kpar(kernel)$lambda))
     2: kernelMatrix(kernel, x[c(indexes[[i]], indexes[[j]])])
     3: kernelMatrix(kernel, x[c(indexes[[i]], indexes[[j]])])
     4: .local(x, ...)
     5: ksvm(reuters, rlabels, kernel = "stringdot", kpar = list(length = 5), cross = 3, C = 10)
     6: ksvm(reuters, rlabels, kernel = "stringdot", kpar = list(length = 5), cross = 3, C = 10)
    aborting ...
Flavor: r-patched-solaris-sparc

Version: 0.9-20
Check: re-building of vignette outputs
Result: NOTE
    Error in re-building vignettes:
     ...
    Error in texi2dvi(file = file, pdf = TRUE, clean = clean, quiet = quiet, :
     Running 'texi2dvi' on 'kernlab.tex' failed.
    LaTeX errors:
    ! Undefined control sequence.
    l.15 x<9c><ed>\<cd>
     k^^[G^^[^^?<ec>dc'U<a5>^^N-.u<ba><b2><bb>E<b8><88>e<a3>V^^P<b0>!<dd>^^F<ab><84>^ғo=<e4><a4><d0>C^^O=<e5>VBo<f9>^^C^^...
    The control sequence at the end of the top line
    of your error message was never \def'ed. If you have
    ! LaTeX Error: Missing \begin{document}.
    
    See the LaTeX manual or LaTeX Companion for explanation.
    Type H <return> for immediate help.
     ...
    ! Extra }, or forgotten \endgroup.
    l.17 ...<f7>}^^Lb<9a>f<9c>5<9f><b8>~<fd>zL<f2><d6><d6><d6>^^XcCCC+++^^_|<f0><c1><fa><fa>z6<9b>}
     <f0><e0><c1><e8><e8><a8><e7>yO<9e><<f1><<8f><88><d2><e9><f4><da><da><da>ŋ^^W...
    I've deleted a group-closing symbol because it seems to be
    spurious, as in `$x}$'. But perhaps the } is legitimate and
    ! Extra }, or forgotten \endgroup.
    l.17 ...O<9e><<f1><<8f><88><d2><e9><f4><da><da
    Calls: buildVignettes -> texi2pdf -> texi2dvi
    Execution halted
Flavor: r-release-osx-x86_64-mavericks