我在R中调整SVM时收到了以下错误：
#Error in if (any(co)) { : missing value where TRUE/FALSE needed我使用的是caret包
svmRTune <- train(x=dataTrain[,predModelContinuous],y=dataTrain[,outcome],method = "svmRadial", tuneLength = 14, trControl = trCtrl)训练集的结构是
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 ...以及
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 a 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 回答：
我发布这条信息是为了以防其他人也遇到这个问题。看起来这个问题是由训练数据集中包含了因子或字符变量引起的。
为什么SVM不能处理因子变量，我也不知道。我用手动编码的虚拟变量替换了我的因子变量，这样就正常工作了，但这种方法太不优雅，无法详细记录。
