Dear all,

I'm a trainee statistician in a company and we'd like to understand svm 
mechanism, at first with simple examples.

I use e1071 package and I have several questions. I'm working with data 
extracted from cats data (from R). My dataset corresponds to a completely 
separable case with a binary response variable ( Sex with 2 levels: F and 
M), two  explanatory variables (Bwt and Hwt) and the classes are balanced.

 I've used  svm() with a linear kernel and I'd like to plot the linear 
hyperplane and the support vectors. I use plot.svm() and, according to me, 
I would have found aligned support vectors (because the hyperplane is 
linear) for each class but it wasn't the case. Could you explain me why ?

In addition, when I change the option 'scale' (from TRUE to FALSE) the 
results change. Could you explain me why ? the option 'scale' of svm() 
acts on the dataset or on the weight vector w and threshold b  ?

Thank you very much,
Gladys.

My dataset 'catseq' is following:

n  Sex  Bwt Hwt
1     F 2.0  7.0
2     F 2.0  7.4
3     F 2.0  9.5
4     F 2.1  7.2
5     F 2.1  7.3
6     F 2.1  7.6
7     F 2.1  8.1
8     F 2.1  8.2
9     F 2.1  8.3
10    F 2.1  8.5
11    F 2.1  8.7
12    F 2.1  9.8
13    F 2.2  7.1
14    F 2.2  8.7
15    F 2.2  9.1
16    F 2.2  9.7
17    F 2.2 10.9
18    F 2.2 11.0
19    F 2.3  7.3
20    F 2.3  7.9
21    F 2.3  8.4
22    F 2.3  9.0
23    F 2.3  9.0
24    F 2.3  9.5
25    F 2.3  9.6
26    F 2.3  9.7
27    F 2.3 10.1
28    F 2.3 10.1
29    F 2.3 10.6
30    F 2.3 11.2
31    F 2.4  6.3
32    F 2.4  8.7
33    F 2.4  8.8
34    F 2.4 10.2
35    F 2.5  9.0
36    F 2.5 10.9
37    F 2.6  8.7
38    F 2.6 10.1
39    F 2.6 10.1
40    F 2.7  8.5
42    F 2.7 10.8
43    F 2.9  9.9
91    M 2.8 11.4
92    M 2.8 12.0
93    M 2.8 13.3
94    M 2.8 13.5
98    M 2.9 11.3
99    M 2.9 11.8
103   M 3.0 11.6
104   M 3.0 12.2
105   M 3.0 12.4
106   M 3.0 12.7
107   M 3.0 13.3
108   M 3.0 13.8
110   M 3.1 11.5
111   M 3.1 12.1
112   M 3.1 12.5
113   M 3.1 13.0
114   M 3.1 14.3
115   M 3.2 11.6
116   M 3.2 11.9
117   M 3.2 12.3
118   M 3.2 13.0
119   M 3.2 13.5
120   M 3.2 13.6
121   M 3.3 11.5
122   M 3.3 12.0
123   M 3.3 14.1
124   M 3.3 14.9
125   M 3.3 15.4
126   M 3.4 11.2
127   M 3.4 12.2
128   M 3.4 12.4
129   M 3.4 12.8
130   M 3.4 14.4
131   M 3.5 11.7
132   M 3.5 12.9
133   M 3.5 15.6
134   M 3.5 15.7
135   M 3.5 17.2
136   M 3.6 11.8
137   M 3.6 13.3
138   M 3.6 14.8
139   M 3.6 15.0


My program is following:

library(e1071)
library(MASS)

catseq <- read.table('P:/catsredeq.txt',header=T,sep="")
plot(catseq$Hwt,catseq$Bwt,pch=as.integer(catseq[,2]),col=as.integer(catseq[,2]),xlab="poids 
du coeur",ylab="poids du corps")
title(main="Données 'chats' (cas séparable)")

catseq <- catseq[,2:4]
attach(catseq)


svm12 <- svm(Sex~.,data=catseq,kernel="linear",scale=T)
svm12
x11()
plot(svm12,catseq,grid=200)

x11()
svm22 <- svm(Sex~.,data=catseq,kernel="linear",scale=F)
svm22
plot(svm22,catseq,grid=200)
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