绘制由 R 中的 caret 包训练的 SVM 线性模型

Purpose

我试图通过可视化 SVM 线性分类模型plot。我正在使用中提供的示例代码和数据kernlab包注意到了caret实际上通过训练 svmksvm函数（参考这里的src代码（https://github.com/topepo/caret/blob/master/models/files/svmLinear.R https://github.com/topepo/caret/blob/master/models/files/svmLinear.R))

Problem

当我绘制插入符号模型对象的最终模型时，它没有产生图形。和我
尝试了三种方法后都没有找到出路。

Code

require(caret)
require(kernlab)
# ===== sample codes from ksvm
x <- rbind(matrix(rnorm(120),ncol =  2), matrix(rnorm(120, mean = 3), ncol = 2))
y <- matrix(c(rep(1, 60), rep(-1, 60)))

# ===== train linear svm model via three wrappers

#===
#dedicated to caret because if I am not preparing data in this way, 
#caret reports errors and stop training with following messages: 
#Error in train.default(x, y, weights = w, ...) : 
#At least one of the class levels is not a valid R variable name; This will cause errors when class 
#probabilities are generated because the variables names will be converted to  X.1, X1 . Please use 
#factor levels that can be used as valid R variable names  (see ?make.names for help).
dat0 <- data.frame(x = x, y= factor(y, levels = c(-1, 1), labels = c('c0', 'c1')))
svp0 <- ksvm(y~., dat0,  type = 'C-svc', kernel = 'vanilladot')

dat <- data.frame(x = x, y = as.factor(y))
svp <- ksvm(y~., dat, type = 'C-svc', kernel = 'vanilladot')

svp1 <- ksvm(x, y, kernel = 'vanilladot', type = 'C-svc')

kernlab::plot(svp0, data = dat0) # works
kernlab::plot(svp, data = dat) # works
kernlab::plot(svp1, data = x) # works

ctr <- trainControl(method='cv',
                    number=5, 
                    classProbs=TRUE,
                    summaryFunction=twoClassSummary 
                    )
svp.c <- train(y ~., dat0, method = "svmLinear",  
               trControl = ctr, 
               preProcess = c('center', 'scale'),
               metric = "ROC")
kernlab::plot(svp.c$finalModel, data = dat0) #Not working
# Error in seq.default(min(sub[, 2]), max(sub[, 2]), length = grid) : 
# 'from' cannot be NA, NaN or infinite

的结构svp.c$finalModel（由插入符训练）与其他相同svp（由原厂训练ksvm）， 我认为。但为什么plot不适用于前一个？

总之，我想知道是否有人设法做到了plotSVM 训练者caret包裹。

谢谢。

编辑2：附件是我的session.Info()。希望它有助于确定我错过了什么。

R版本3.2.2（2015-08-14）平台：x86_64-apple-darwin13.4.0 （64 位）运行于：OS X 10.11.1 (El Capitan)

locale: 1 https://i.stack.imgur.com/Ptk4R.pngen_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

附加基础包：

1 https://i.stack.imgur.com/Ptk4R.png统计图形 grDevices utils 数据集 方法 基础

其他附件包：

1 https://i.stack.imgur.com/Ptk4R.pngkernlab_0.9-22 caret_6.0-62 ggplot2_1.0.1lattice_0.20-33

通过命名空间加载（并且未附加）：1 https://i.stack.imgur.com/Ptk4R.pngRcpp_0.12.2
magrittr_1.5样条_3.2.2 MASS_7.3-44 munsell_0.4.2

[6] colorspace_1.2-6 foreach_1.4.3 minqa_1.2.4
stringr_1.0.0 car_2.1-0

[11]plyr_1.8.3tools_3.2.2parallel_3.2.2
nnet_7.3-11 pbkrtest_0.4-2

[16]grid_3.2.2 gtable_0.1.2 nlme_3.1-122
mgcv_1.8-7 quantreg_5.19 [21] MatrixModels_0.4-1 iterators_1.0.8 lme4_1.1-9 摘要_0.6.8 Matrix_1.2-2

[26] nloptr_1.0.4 reshape2_1.4.1 codetools_0.2-14
stringi_1.0-1compiler_3.2.0

[31] pROC_1.8 scales_0.3.0.9000 stats4_3.2.2
SparseM_1.7 proto_0.3-10

当我使用kernlab::plot(svp.c$finalModel)我得到以下输出：

附录：结果sessionInfo()

R version 3.2.2 (2015-08-14)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.1 (El Capitan)

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] kernlab_0.9-20  caret_6.0-47    ggplot2_1.0.1   lattice_0.20-33

loaded via a namespace (and not attached):
 [1] Rcpp_0.11.6         magrittr_1.5        splines_3.2.2       MASS_7.3-43        
 [5] munsell_0.4.2       colorspace_1.2-6    foreach_1.4.2       minqa_1.2.4        
 [9] car_2.0-25          stringr_1.0.0       plyr_1.8.3          tools_3.2.2        
[13] parallel_3.2.2      pbkrtest_0.4-2      nnet_7.3-10         grid_3.2.2         
[17] gtable_0.1.2        nlme_3.1-121        mgcv_1.8-7          quantreg_5.11      
[21] iterators_1.0.7     gtools_3.5.0        lme4_1.1-8          digest_0.6.8       
[25] Matrix_1.2-2        nloptr_1.0.4        reshape2_1.4.1      codetools_0.2-14   
[29] stringi_0.5-5       compiler_3.2.2      BradleyTerry2_1.0-6 pROC_1.8           
[33] scales_0.2.5        SparseM_1.6         brglm_0.5-9         proto_0.3-10