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195

answers:

1

I'm using the support vector machine from the e1071 package to classify my data and want to visualize how the machine actually does the classification. However, when using the plot.svm function, I get an error that I can't resolve.

Script:

library("e1071")

data <-read.table("2010223_11042_complete")
names(data) <- c("Class","V1", "V2")

model <- svm(Class~.,data, type = "C-classification", kernel = "linear")
plot(model,data,fill=TRUE, grid=200, svSymbol=4, dataSymbol=1, color.palette=terrain.colors)

Output:

plot(model,data,fill=TRUE, grid=200, svSymbol=4, dataSymbol=1, color.palette=terrain.colors)
Error in rect(0, levels[-length(levels)], 1, levels[-1L], col = col) : 
  cannot mix zero-length and non-zero-length coordinates

Traceback:

traceback()
4: rect(0, levels[-length(levels)], 1, levels[-1L], col = col)
3: filled.contour(xr, yr, matrix(as.numeric(preds), nr = length(xr), 
       byrow = TRUE), plot.axes = {
       axis(1)
       axis(2)
       colind <- as.numeric(model.response(model.frame(x, data)))
       dat1 <- data[-x$index, ]
       dat2 <- data[x$index, ]
       coltmp1 <- symbolPalette[colind[-x$index]]
       coltmp2 <- symbolPalette[colind[x$index]]
       points(formula, data = dat1, pch = dataSymbol, col = coltmp1)
       points(formula, data = dat2, pch = svSymbol, col = coltmp2)
   }, levels = 1:(length(levels(preds)) + 1), key.axes = axis(4, 
       1:(length(levels(preds))) + 0.5, labels = levels(preds), 
       las = 3), plot.title = title(main = "SVM classification plot", 
       xlab = names(lis)[2], ylab = names(lis)[1]), ...)
2: plot.svm(model, data, fill = TRUE, grid = 200, svSymbol = 4, 
       dataSymbol = 1, color.palette = terrain.colors)
1: plot(model, data, fill = TRUE, grid = 200, svSymbol = 4, 
       dataSymbol = 1, color.palette = terrain.colors)

Part of my (4488 lines long) data file:

-1 0 23.532
+1 1 61.1157
+1 1 61.1157
+1 1 61.1157
-1 1 179.03
-1 0 17.0865
-1 0 27.6201
-1 0 17.0865
-1 0 27.6201
-1 1 89.6398
-1 0 42.7418
-1 1 89.6398

Since I`m just starting with R, I have no idea what this means and how I should deal with it, nor did I find anything useful in other places.

+1  A: 

Without being sure what exactly causes the problem, I would try to transform the Class column to a factor (so defining the type as C-classification will no longer be necessary) using something like this:

data$Class <- as.factor(data$Class)

or in one step:

model <- svm(as.factor(Class)~.,data, kernel = "linear")
gd047
Thanks dude. Works like a charm. Like you proposed, I transformed my Class column to a factor and removed the 'type' argument in the call of svm. The error's gone.
RvdLee