data after write() is off by 1 ?

I am new to R, so I am sure I am making a simple mistake.  I am including complete information in hopes
someone can help me.

Basically my data in R looks good, I write it to a file, and every value is off by 1.

Here is my flow:

str(prediction)

  Factor w/ 10 levels "0","1","2","3",..: 3 1 10 10 4 8 1 4 1 4 ...
  - attr(*, "names")= chr [1:28000] "1" "2" "3" "4" ...

print(prediction)

     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22    23
     2     0     9     9     3     7     0     3     0     3     5     7     4     0     4     3     3     1     9     0     9     1     1

ok, so it shows my values are 2, 0, 9, 9, 3 etc

# I write my file out
write(prediction, file="prediction.csv")

# look at the first 10 values
$ head -10 prediction.csv
3 1 10 10 4
8 1 4 1 4
6 8 5 1 5
4 4 2 10 1
10 2 2 6 8
5 3 8 5 8
8 6 5 3 7
3 6 6 2 7
8 8 5 10 9
8 9 3 7 8

The complete work of what I did was as follows:

# First I load in a dataset, label the first column as a factor

dataset <- read.csv('train.csv',head=TRUE)
dataset$label <- as.factor(dataset$label)

# it has 42000 obs. 785 variables

str(dataset)

'data.frame':	42000 obs. of  785 variables:
  $ label   : Factor w/ 10 levels "0","1","2","3",..: 2 1 2 5 1 1 8 4 6 4 ...
  $ pixel0  : int  0 0 0 0 0 0 0 0 0 0 ...
  $ pixel1  : int  0 0 0 0 0 0 0 0 0 0 ...
  $ pixel2  : int  0 0 0 0 0 0 0 0 0 0 ...
   [list output truncated]

# I make a sampling testset and trainset

index <- 1:nrow(dataset)
testindex <- sample(index, trunc(length(index)*30/100))
testset <- dataset[testindex,]
trainset <- dataset[-testindex,]

# build model, predict, view

model  <- svm(label~., data = trainset, type="C-classification", kernel="radial", gamma=0.0000001, cost=16)
prediction <- predict(model, testset)
tab <- table(pred = prediction, true = testset[,1])

     true
pred    0    1    2    3    4    5    6    7    8    9
    0 1210    0    3    1    0    5    7    2    5    8
    1    0 1415    2    0    2    1    0    7    5    0
    2    0    2 1127   12    3    0    2    7    2    0
    3    0    0    7 1296    0   10    0    2   15    6
    4    1    1    8    2 1201    2    4    3    5   16
    5    3    1    0   13    0 1100    3    1    2    3
    6    3    0    3    0    5    9 1263    0    1    0
    7    0    2    9    6    6    1    0 1296    1   13
    8    3    5    7   11    1    2    0    2 1190    4
    9    1    1    2    3   17    2    0    4    4 1190


Ok everything looks great up to this point..........so I try to apply my model to a "real" testset, which is the same format as my previous
dataset, except it does not have the label/factor column, so its 28000 obs 784 variables:

testset <- read.csv('test.csv',head=TRUE)
str(testset)

'data.frame':	28000 obs. of  784 variables:
  $ pixel0  : int  0 0 0 0 0 0 0 0 0 0 ...
  $ pixel1  : int  0 0 0 0 0 0 0 0 0 0 ...
  $ pixel2  : int  0 0 0 0 0 0 0 0 0 0 ...
   [list output truncated]

prediction <- predict(model, testset)
summary(prediction)

    0    1    2    3    4    5    6    7    8    9
2780 3204 2824 2767 2771 2516 2744 2898 2736 2760

print(prediction)

     1     2     3     4     5     6     7     8     9    10    11    12    13    14    15    16    17    18    19    20    21    22    23
     2     0     9     9     3     7     0     3     0     3     5     7     4     0     4     3     3     1     9     0     9     1     1
    24    25    26    27    28    29    30    31    32    33    34    35    36    37    38    39    40    41    42    43    44    45    46
     5     7     4     2     7     4     7     7     5     4     2     6     2     5     5     1     6     7     7     4     9     8     7
   [list output truncated]

write(prediction, file="prediction.csv")

$ head -10 prediction.csv
3 1 10 10 4
8 1 4 1 4
6 8 5 1 5
4 4 2 10 1
10 2 2 6 8
5 3 8 5 8
8 6 5 3 7
3 6 6 2 7
8 8 5 10 9
8 9 3 7 8


I am obviously making a mistake.  Everything is off by a value of 1.


Can someone tell me what I am doing wrong?

Brian



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