machine learning - error tuning SVM in R -

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i'm tuning svm in r , receive following error:

#error in if (any(co)) { : missing value true/false needed

i'm using caret package 

svmrtune <- train(x=datatrain[,predmodelcontinuous],y=datatrain[,outcome],method = "svmradial", tunelength = 14, trcontrol = trctrl)

the training set structure is

str(datatrain) 'data.frame':   40001 obs. of  42 variables:  $ polnum     : num  2e+08 2e+08 2e+08 2e+08 2e+08 ...  $ sex        : factor w/ 2 levels "male","female": 1 1 1 2 1 2 1 1 1 2 ...  $ type       : factor w/ 6 levels "a","b","c","d",..: 3 1 1 2 2 4 3 3 3 2 ...  $ catgry     : ord.factor w/ 3 levels "large"<"medium"<..: 2 2 2 3 3 3 3 2 2 2 ...  $ occup      : factor w/ 5 levels "employed","housewife",..: 2 1 1 1 5 4 1 1 4 2 ...  $ age        : num  48 23 23 39 24 39 28 43 45 38 ...  $ group      : factor w/ 20 levels "1","2","3","4",..: 15 16 12 16 14 8 16 9 12 8 ...  $ bonus      : ord.factor w/ 21 levels "-50"<"-40"<"-30"<..: 14 8 4 3 5 2 5 5 1 15 ...  $ poldur     : num  7 1 1 14 2 4 11 2 8 5 ...  $ value      : num  1120 21755 18430 11930 24850 ...  $ adind      : factor w/ 2 levels "no","yes": 2 1 1 2 1 2 2 2 1 1 ...  $ pcode      : chr  "sc22" "ct109" "ma1" "sa12" ...  $ area       : factor w/ 10 levels "ct","ju","ma",..: 7 1 3 6 6 6 6 4 1 2 ...  $ density    : num  270.5 57.3 43.2 167.9 169.8 ...  $ prem       : num  1159 532 527 197 908 ...  $ premad     : num  53.1 413.7 410.7 61.6 824.6 ...  $ numclm     : num  0 1 0 1 0 0 0 1 0 0 ...  $ invite     : num  1 1 1 1 1 1 1 1 1 1 ...  $ renewaltp  : num  1302 928 632 291 960 ...  $ renewalad  : num  58.4 599 440.4 71.3 682 ...  $ markettp   : num  1110 884 565 253 833 ...  $ marketad   : num  53.4 611.4 431.6 55.5 587 ...  $ premtot    : num  1212 532 527 259 908 ...  $ renewaltot : num  1361 928 632 362 960 ...  $ markettot  : num  1163 884 565 309 833 ...  $ renew      : ord.factor w/ 2 levels "no"<"yes": 1 1 1 1 1 1 1 1 1 1 ...  $ premchng   : num  1.12 1.74 1.2 1.4 1.06 ...  $ compmeas   : num  1.17 1.05 1.12 1.17 1.15 ...  $ numclmrec  : ord.factor w/ 3 levels "none"<"one"<"two or more": 1 2 1 2 1 1 1 2 1 1 ...  $ premchngrec: factor w/ 20 levels "[0.546,0.758)",..: 16 20 18 19 14 3 7 19 17 11 ...  $ agerec     : factor w/ 20 levels "[19,22)","[22,25)",..: 14 2 2 9 2 9 4 11 12 9 ...  $ valuerec   : factor w/ 20 levels "[ 1005, 3290)",..: 1 15 13 9 17 5 12 12 19 1 ...  $ densityrec : factor w/ 20 levels "[ 14.4, 25.0)",..: 19 6 5 15 15 13 15 1 5 11 ...  $ compmeasrec: factor w/ 20 levels "[0.716,0.869)",..: 12 6 10 13 12 18 11 16 18 14 ...  $ poldurrec  : ord.factor w/ 16 levels "1"<"2"<"3"<"4"<..: 7 1 1 14 2 4 11 2 8 5 ...  $ agest      : num  0.407 -1.34 -1.34 -0.222 -1.27 ...  $ numclmst   : num  -0.433 1.627 -0.433 1.627 -0.433 ...  $ premchngst : num  0.591 3.709 0.98 1.985 0.265 ...  $ valuest    : num  -1.462 0.499 0.183 -0.434 0.793 ...  $ densityst  : num  1.918 -0.748 -0.924 0.636 0.659 ...  $ compmeasst : num  0.224 -0.539 -0.098 0.248 0.113 ...  $ poldurst   : num  0.097 -1.2 -1.2 1.61 -0.984 ...

and 

sessioninfo() r version 3.0.2 (2013-09-25) platform: x86_64-w64-mingw32/x64 (64-bit)  locale:  [1] lc_collate=italian_italy.1252  lc_ctype=italian_italy.1252    [3] lc_monetary=italian_italy.1252 lc_numeric=c                   [5] lc_time=italian_italy.1252    attached base packages:   [1] parallel  splines   grid      stats     graphics  grdevices utils      [8] datasets  methods   base    other attached packages:   [1] c50_0.1.0-16       kernlab_0.9-19     nnet_7.3-8         plyr_1.8.1          [5] gbm_2.1            randomforest_4.6-7 rpart_4.1-8        klar_0.6-10         [9] mass_7.3-31        doparallel_1.0.8   iterators_1.0.6    foreach_1.4.1      [13] proc_1.7.1         mda_0.4-4          class_7.3-10       earth_3.2-7        [17] plotrix_3.5-5      plotmo_1.3-3       formula_1.1-1      survival_2.37-7    [21] caret_6.0-24       ggplot2_0.9.3.1    lattice_0.20-29    rj_1.1.3-1          loaded via namespace (and not attached):   [1] car_2.0-19          cluster_1.15.2      codetools_0.2-8      [4] colorspace_1.2-4    combinat_0.0-8      compiler_3.0.2       [7] dichromat_2.0-0     digest_0.6.4        gtable_0.1.2        [10] hmisc_3.14-3        labeling_0.2        latticeextra_0.6-26 [13] munsell_0.4.2       proto_0.3-10        rcolorbrewer_1.0-5  [16] rcpp_0.11.1         reshape2_1.2.2      rj.gd_1.1.3-1       [19] scales_0.2.3        stringr_0.6.2       tools_3.0.2

just posting in case else runs across problem. appears caused including factor or character variable in training data set.

why svm can not take factor variable, not know. replaced factors hand coded dummies, , worked fine, approach inelegant document.


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